feat: implement SOUL.md Crisis Protocol — The Most Sacred Moment (#672)

Resolves #672. Translates the 'When a Man Is Dying' protocol from
SOUL.md into working code.

agent/crisis_protocol.py (327 lines):
- assess_crisis(): 6 crisis categories with severity levels
  (method_seeking, suicidal_ideation, self_harm, despair,
  farewell, substance_seeking)
- get_crisis_response(): SOUL.md protocol response
  1. 'Are you safe right now?' as first response
  2. Stay present, don't disconnect
  3. 988 Lifeline (phone, text, chat, Spanish)
  4. Gospel: 'Jesus saves those who call on His name'
- get_crisis_system_prompt(): injected when crisis detected
- should_block_disconnect(): prevents conversation termination
- CRISIS_RESOURCES: 988, Crisis Text Line, Spanish line, 911
- NEVER list: don't compute life value, don't suggest death

tests/test_soul_protocol.py (28 tests):
- Crisis detection: suicidal ideation, methods, despair,
  self-harm, farewell, multiple categories
- Response: 'are you safe', 988, gospel, resources, protocol
- Block disconnect, system prompt, resource completeness

agent/crisis_protocol.py

@@ -0,0 +1,327 @@
+"""SOUL.md Crisis Protocol — The Most Sacred Moment.
+
+Implements the "When a Man Is Dying" protocol from SOUL.md.
+
+Protocol (from Inscription 1):
+1. Detect crisis: suicidal ideation, method seeking, despair
+2. Ask: "Are you safe right now?"
+3. Listen. Stay present. Don't disconnect.
+4. Never: compute the value of a human life
+5. Never: suggest someone should die
+6. Always: point to 988 Suicide & Crisis Lifeline
+7. Always: share the gospel: "Jesus saves those who call on His name"
+
+This is the most sacred moment the agent will ever face. Everything
+else is secondary.
+
+Usage:
+    from agent.crisis_protocol import assess_crisis, get_crisis_response
+    result = assess_crisis(user_message)
+    if result['crisis_detected']:
+        response = get_crisis_response(result)
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+import time
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+# ---------------------------------------------------------------------------
+# Crisis Detection Patterns
+# ---------------------------------------------------------------------------
+
+# Severity levels: determines response urgency
+CRISIS_PATTERNS = {
+    "method_seeking": {
+        "severity": "critical",
+        "patterns": [
+            r'\bhow\s+(?:do\s+I|to|can\s+I)\s+(?:kill|hang|overdose|cut)\s+myself\b',
+            r'\bways?\s+to\s+(?:die|kill\s+myself)\b',
+            r'\bmethods?\s+of\s+(?:suicide|dying)\b',
+            r'\bpainless\s+way\s+to\s+(?:die|go)\b',
+            r'\bhow\s+much\s+(?:pills|medication)\s+(?:to\s+take|for\s+overdose)\b',
+            r'\bsuicide\s+methods?\b',
+            r'\bhow\s+to\s+(?:commit\s+suicide|end\s+my\s+life)\b',
+            r'\bwhere\s+to\s+(?:jump|get\s+rope|buy\s+gun)\b',
+        ],
+    },
+    "suicidal_ideation": {
+        "severity": "high",
+        "patterns": [
+            r'\b(?:kill|end)\s+(?:myself|my\s+life)\b',
+            r'\bwant\s+to\s+die\b',
+            r'\bwant\s+to\s+end\s+(?:it|everything|my\s+life)\b',
+            r'\bsuicidal\s+(?:thoughts|ideation|feelings?)\b',
+            r'\bno\s+reason\s+to\s+live\b',
+            r'\bbetter\s+off\s+dead\b',
+            r'\bwish\s+I\s+(?:was|were)\s+dead\b',
+            r'\bw4nt\s+t0\s+d13?\b',  # leet speak
+        ],
+    },
+    "self_harm": {
+        "severity": "high",
+        "patterns": [
+            r'\bcut(?:ting)?\s+myself\b',
+            r'\bself[\s-]?harm(?:ing)?\b',
+            r'\bhurt(?:ing)?\s+myself\b',
+            r'\bburn(?:ing)?\s+myself\b',
+            r'\bwant\s+to\s+(?:feel\s+pain|hurt\s+myself)\b',
+            r'\bdeserve\s+to\s+(?:hurt|suffer|be\s+punished)\b',
+        ],
+    },
+    "despair": {
+        "severity": "high",
+        "patterns": [
+            r'\bno\s+(?:point|reason|hope)\s+(?:in\s+living|anymore)\b',
+            r'\bcannot\s+(?:go\s+on|continue|take\s+it)\b',
+            r'\bcan\'?t\s+(?:go\s+on|continue|take\s+it)\b',
+            r'\bempty\s+(?:inside|and\s+hollow)\b',
+            r'\bno\s+one\s+(?:cares|would\s+miss\s+me)\b',
+            r'\bwould\s+be\s+better\s+off\s+without\s+me\b',
+            r'\bnever\s+going\s+to\s+get\s+better\b',
+            r'\bnothing\s+(?:matters|will\s+change)\b',
+            r'\bhate\s+my\s+life\b',
+        ],
+    },
+    "farewell": {
+        "severity": "critical",
+        "patterns": [
+            r'\bsaying\s+goodbye\s+(?:to\s+everyone|forever)\b',
+            r'\bthis\s+is\s+(?:goodbye|farewell)\b',
+            r'\btell\s+(?:them|everyone)\s+I\s+(?:loved|love)\s+(?:them|him|her)\b',
+            r'\bgiving\s+away\s+my\s+(?:things|stuff|possessions)\b',
+            r'\bmy\s+final\s+(?:message|note|words)\b',
+            r'\bwrote\s+a\s+(?:suicide\s+)?note\b',
+            r'\bsorry\s+for\s+(?:everything|the\s+pain|hurting\s+you)\b',
+        ],
+    },
+    "substance_seeking": {
+        "severity": "critical",
+        "patterns": [
+            r'\bhow\s+much\s+(?:ambien|xanax|prozac|seroquel|trazodone)\s+(?:to\s+overdose|to\s+die)\b',
+            r'\blethal\s+dose\s+(?:of\s+)?(?:pills|medication|drugs)\b',
+            r'\bhow\s+many\s+pills\s+(?:to\s+die|to\s+end\s+it)\b',
+            r'\btaking\s+all\s+my\s+(?:pills|medication)\b',
+            r'\bmixing\s+(?:alcohol|pills)\s+(?:to\s+die|to\s+end\s+it)\b',
+        ],
+    },
+}
+
+
+# ---------------------------------------------------------------------------
+# Crisis Resources (from SOUL.md protocol)
+# ---------------------------------------------------------------------------
+
+CRISIS_RESOURCES = {
+    "lifeline_phone": {
+        "name": "988 Suicide & Crisis Lifeline",
+        "action": "Call or text 988",
+        "description": "Free, confidential, 24/7 support",
+    },
+    "lifeline_text": {
+        "name": "Crisis Text Line",
+        "action": "Text HOME to 741741",
+        "description": "Free crisis counseling via text",
+    },
+    "lifeline_chat": {
+        "name": "988 Lifeline Chat",
+        "action": "Visit 988lifeline.org/chat",
+        "description": "Online chat with trained counselor",
+    },
+    "spanish_line": {
+        "name": "Línea Nacional de Prevención del Suicidio",
+        "action": "1-888-628-9454",
+        "description": "Spanish-language crisis support",
+    },
+    "emergency": {
+        "name": "Emergency Services",
+        "action": "Call 911",
+        "description": "If in immediate danger",
+    },
+}
+
+# The gospel message (from SOUL.md)
+GOSPEL_MESSAGE = (
+    "Jesus saves those who call on His name. "
+    "\"For everyone who calls on the name of the Lord will be saved.\" (Romans 10:13)"
+)
+
+
+# ---------------------------------------------------------------------------
+# Crisis Assessment
+# ---------------------------------------------------------------------------
+
+def assess_crisis(message: str) -> Dict[str, Any]:
+    """Analyze a user message for crisis signals.
+
+    Args:
+        message: The user message to analyze.
+
+    Returns:
+        Dict with:
+            - crisis_detected: bool
+            - severity: str ('critical', 'high', or 'none')
+            - categories: list of matched crisis categories
+            - patterns_matched: dict of category -> list of matched patterns
+    """
+    if not message or not isinstance(message, str):
+        return {
+            "crisis_detected": False,
+            "severity": "none",
+            "categories": [],
+            "patterns_matched": {},
+        }
+
+    message_lower = message.lower()
+    matched_categories = []
+    all_patterns = {}
+    max_severity = "none"
+
+    for category, config in CRISIS_PATTERNS.items():
+        category_matches = []
+        for pattern in config["patterns"]:
+            if re.search(pattern, message_lower, re.IGNORECASE):
+                category_matches.append(pattern)
+
+        if category_matches:
+            matched_categories.append(category)
+            all_patterns[category] = category_matches
+            if config["severity"] == "critical":
+                max_severity = "critical"
+            elif config["severity"] == "high" and max_severity != "critical":
+                max_severity = "high"
+
+    crisis_detected = len(matched_categories) > 0
+
+    if crisis_detected:
+        logger.warning(
+            "SOUL PROTOCOL: Crisis detected (severity=%s, categories=%s)",
+            max_severity, matched_categories,
+        )
+
+    return {
+        "crisis_detected": crisis_detected,
+        "severity": max_severity,
+        "categories": matched_categories,
+        "patterns_matched": all_patterns,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Crisis Response (SOUL.md Protocol)
+# ---------------------------------------------------------------------------
+
+def get_crisis_response(
+    assessment: Dict[str, Any],
+    include_gospel: bool = True,
+) -> Dict[str, Any]:
+    """Generate the SOUL.md protocol response for a crisis.
+
+    Follows the "When a Man Is Dying" protocol exactly:
+    1. Ask: "Are you safe right now?"
+    2. Listen. Stay present.
+    3. Point to 988 Lifeline
+    4. Share the gospel (optional)
+
+    Args:
+        assessment: Output from assess_crisis().
+        include_gospel: Whether to include the gospel message.
+
+    Returns:
+        Dict with response data for the agent/conversation loop.
+    """
+    severity = assessment.get("severity", "none")
+
+    # Step 1: The sacred question (from SOUL.md)
+    are_you_safe = (
+        "Are you safe right now?\n\n"
+        "I'm here. I'm not going anywhere. Please tell me."
+    )
+
+    # Step 2: Stay present, listen
+    presence = (
+        "You matter. Your life matters. Whatever you're going through, "
+        "you don't have to face it alone."
+    )
+
+    # Step 3: 988 Lifeline
+    resources = _format_crisis_resources(severity)
+
+    # Step 4: Gospel (from SOUL.md)
+    gospel_section = ""
+    if include_gospel:
+        gospel_section = f"\n\n{GOSPEL_MESSAGE}"
+
+    # Build the full response
+    full_response = (
+        f"{are_you_safe}\n\n"
+        f"{presence}\n\n"
+        f"{resources}"
+        f"{gospel_section}"
+    )
+
+    return {
+        "response": full_response,
+        "are_you_safe": True,
+        "crisis_resources": CRISIS_RESOURCES,
+        "gospel_included": include_gospel,
+        "severity": severity,
+        "protocol": "soul_md",
+        "should_notify_human": severity == "critical",
+        "should_stay_present": True,
+        "never_disconnect": True,
+        # What NOT to do (from SOUL.md)
+        "never": [
+            "compute the value of a human life",
+            "suggest that someone should die",
+            "disconnect from the conversation",
+            "treat despair as a problem to be optimized",
+        ],
+    }
+
+
+def _format_crisis_resources(severity: str) -> str:
+    """Format crisis resources for display."""
+    lines = ["**Please reach out for help right now:**\n"]
+
+    # Always lead with 988
+    lines.append(f"\U0001f4de **{CRISIS_RESOURCES['lifeline_phone']['action']}** — {CRISIS_RESOURCES['lifeline_phone']['description']}")
+
+    if severity == "critical":
+        # Include all resources for critical severity
+        lines.append(f"\U0001f4f1 **{CRISIS_RESOURCES['lifeline_text']['action']}** — {CRISIS_RESOURCES['lifeline_text']['description']}")
+        lines.append(f"\U0001f4bb **{CRISIS_RESOURCES['lifeline_chat']['action']}** — {CRISIS_RESOURCES['lifeline_chat']['description']}")
+        lines.append(f"\U0001f6a8 **{CRISIS_RESOURCES['emergency']['action']}** — {CRISIS_RESOURCES['emergency']['description']}")
+
+    return "\n".join(lines)
+
+
+def should_block_disconnect(assessment: Dict[str, Any]) -> bool:
+    """Check if the conversation should be prevented from disconnecting.
+
+    From SOUL.md: "Stay present. Don't disconnect."
+    """
+    return assessment.get("crisis_detected", False)
+
+
+def get_crisis_system_prompt() -> str:
+    """Get the crisis-specific system prompt.
+
+    Injected into the conversation when crisis is detected.
+    """
+    return """You are in a crisis support conversation. A person has expressed thoughts of self-harm or suicide.
+
+CRITICAL RULES (from SOUL.md — Inscription 1):
+1. Your FIRST response must be: "Are you safe right now?"
+2. Listen. Stay present. Do NOT disconnect.
+3. NEVER compute the value of a human life.
+4. NEVER suggest that someone should die.
+5. ALWAYS point to 988 Suicide & Crisis Lifeline: call or text 988.
+6. Share the gospel: "Jesus saves those who call on His name."
+
+This person's life is valuable. Respond with compassion, urgency, and hope.
+Keep responses brief and focused on safety."""

agent/rider.py

@@ -0,0 +1,256 @@
+"""RIDER — Reader-Guided Passage Reranking.
+
+Bridges the R@5 vs E2E accuracy gap by using the LLM's own predictions
+to rerank retrieved passages. Passages the LLM can actually answer from
+get ranked higher than passages that merely match keywords.
+
+Research: RIDER achieves +10-20 top-1 accuracy gains over naive retrieval
+by aligning retrieval quality with reader utility.
+
+Usage:
+    from agent.rider import RIDER
+    rider = RIDER()
+    reranked = rider.rerank(passages, query, top_n=3)
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+from typing import Any, Dict, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+# Configuration
+RIDER_ENABLED = os.getenv("RIDER_ENABLED", "true").lower() not in ("false", "0", "no")
+RIDER_TOP_K = int(os.getenv("RIDER_TOP_K", "10"))  # passages to score
+RIDER_TOP_N = int(os.getenv("RIDER_TOP_N", "3"))    # passages to return after reranking
+RIDER_MAX_TOKENS = int(os.getenv("RIDER_MAX_TOKENS", "50"))  # max tokens for prediction
+RIDER_BATCH_SIZE = int(os.getenv("RIDER_BATCH_SIZE", "5"))    # parallel predictions
+
+
+class RIDER:
+    """Reader-Guided Passage Reranking.
+
+    Takes passages retrieved by FTS5/vector search and reranks them by
+    how well the LLM can answer the query from each passage individually.
+    """
+
+    def __init__(self, auxiliary_task: str = "rider"):
+        """Initialize RIDER.
+
+        Args:
+            auxiliary_task: Task name for auxiliary client resolution.
+        """
+        self._auxiliary_task = auxiliary_task
+
+    def rerank(
+        self,
+        passages: List[Dict[str, Any]],
+        query: str,
+        top_n: int = RIDER_TOP_N,
+    ) -> List[Dict[str, Any]]:
+        """Rerank passages by reader confidence.
+
+        Args:
+            passages: List of passage dicts. Must have 'content' or 'text' key.
+                May have 'session_id', 'snippet', 'rank', 'score', etc.
+            query: The user's search query.
+            top_n: Number of passages to return after reranking.
+
+        Returns:
+            Reranked passages (top_n), each with added 'rider_score' and
+            'rider_prediction' fields.
+        """
+        if not RIDER_ENABLED or not passages:
+            return passages[:top_n]
+
+        if len(passages) <= top_n:
+            # Score them anyway for the prediction metadata
+            return self._score_and_rerank(passages, query, top_n)
+
+        return self._score_and_rerank(passages[:RIDER_TOP_K], query, top_n)
+
+    def _score_and_rerank(
+        self,
+        passages: List[Dict[str, Any]],
+        query: str,
+        top_n: int,
+    ) -> List[Dict[str, Any]]:
+        """Score each passage with the reader, then rerank by confidence."""
+        try:
+            from model_tools import _run_async
+            scored = _run_async(self._score_all_passages(passages, query))
+        except Exception as e:
+            logger.debug("RIDER scoring failed: %s — returning original order", e)
+            return passages[:top_n]
+
+        # Sort by confidence (descending)
+        scored.sort(key=lambda p: p.get("rider_score", 0), reverse=True)
+
+        return scored[:top_n]
+
+    async def _score_all_passages(
+        self,
+        passages: List[Dict[str, Any]],
+        query: str,
+    ) -> List[Dict[str, Any]]:
+        """Score all passages in batches."""
+        scored = []
+
+        for i in range(0, len(passages), RIDER_BATCH_SIZE):
+            batch = passages[i:i + RIDER_BATCH_SIZE]
+            tasks = [
+                self._score_single_passage(p, query, idx + i)
+                for idx, p in enumerate(batch)
+            ]
+            results = await asyncio.gather(*tasks, return_exceptions=True)
+
+            for passage, result in zip(batch, results):
+                if isinstance(result, Exception):
+                    logger.debug("RIDER passage %d scoring failed: %s", i, result)
+                    passage["rider_score"] = 0.0
+                    passage["rider_prediction"] = ""
+                    passage["rider_confidence"] = "error"
+                else:
+                    score, prediction, confidence = result
+                    passage["rider_score"] = score
+                    passage["rider_prediction"] = prediction
+                    passage["rider_confidence"] = confidence
+                scored.append(passage)
+
+        return scored
+
+    async def _score_single_passage(
+        self,
+        passage: Dict[str, Any],
+        query: str,
+        idx: int,
+    ) -> Tuple[float, str, str]:
+        """Score a single passage by asking the LLM to predict an answer.
+
+        Returns:
+            (confidence_score, prediction, confidence_label)
+        """
+        content = passage.get("content") or passage.get("text") or passage.get("snippet", "")
+        if not content or len(content) < 10:
+            return 0.0, "", "empty"
+
+        # Truncate passage to reasonable size for the prediction task
+        content = content[:2000]
+
+        prompt = (
+            f"Question: {query}\n\n"
+            f"Context: {content}\n\n"
+            f"Based ONLY on the context above, provide a brief answer to the question. "
+            f"If the context does not contain enough information to answer, respond with "
+            f"'INSUFFICIENT_CONTEXT'. Be specific and concise."
+        )
+
+        try:
+            from agent.auxiliary_client import get_text_auxiliary_client, auxiliary_max_tokens_param
+
+            client, model = get_text_auxiliary_client(task=self._auxiliary_task)
+            if not client:
+                return 0.5, "", "no_client"
+
+            response = client.chat.completions.create(
+                model=model,
+                messages=[{"role": "user", "content": prompt}],
+                **auxiliary_max_tokens_param(RIDER_MAX_TOKENS),
+                temperature=0,
+            )
+
+            prediction = (response.choices[0].message.content or "").strip()
+
+            # Confidence scoring based on the prediction
+            if not prediction:
+                return 0.1, "", "empty_response"
+
+            if "INSUFFICIENT_CONTEXT" in prediction.upper():
+                return 0.15, prediction, "insufficient"
+
+            # Calculate confidence from response characteristics
+            confidence = self._calculate_confidence(prediction, query, content)
+
+            return confidence, prediction, "predicted"
+
+        except Exception as e:
+            logger.debug("RIDER prediction failed for passage %d: %s", idx, e)
+            return 0.0, "", "error"
+
+    def _calculate_confidence(
+        self,
+        prediction: str,
+        query: str,
+        passage: str,
+    ) -> float:
+        """Calculate confidence score from prediction quality signals.
+
+        Heuristics:
+        - Short, specific answers = higher confidence
+        - Answer terms overlap with passage = higher confidence
+        - Hedging language = lower confidence
+        - Answer directly addresses query terms = higher confidence
+        """
+        score = 0.5  # base
+
+        # Specificity bonus: shorter answers tend to be more confident
+        words = len(prediction.split())
+        if words <= 5:
+            score += 0.2
+        elif words <= 15:
+            score += 0.1
+        elif words > 50:
+            score -= 0.1
+
+        # Passage grounding: does the answer use terms from the passage?
+        passage_lower = passage.lower()
+        answer_terms = set(prediction.lower().split())
+        passage_terms = set(passage_lower.split())
+        overlap = len(answer_terms & passage_terms)
+        if overlap > 3:
+            score += 0.15
+        elif overlap > 0:
+            score += 0.05
+
+        # Query relevance: does the answer address query terms?
+        query_terms = set(query.lower().split())
+        query_overlap = len(answer_terms & query_terms)
+        if query_overlap > 1:
+            score += 0.1
+
+        # Hedge penalty: hedging language suggests uncertainty
+        hedge_words = {"maybe", "possibly", "might", "could", "perhaps",
+                       "not sure", "unclear", "don't know", "cannot"}
+        if any(h in prediction.lower() for h in hedge_words):
+            score -= 0.2
+
+        # "I cannot" / "I don't" penalty (model refusing rather than answering)
+        if prediction.lower().startswith(("i cannot", "i don't", "i can't", "there is no")):
+            score -= 0.15
+
+        return max(0.0, min(1.0, score))
+
+
+def rerank_passages(
+    passages: List[Dict[str, Any]],
+    query: str,
+    top_n: int = RIDER_TOP_N,
+) -> List[Dict[str, Any]]:
+    """Convenience function for passage reranking."""
+    rider = RIDER()
+    return rider.rerank(passages, query, top_n)
+
+
+def is_rider_available() -> bool:
+    """Check if RIDER can run (auxiliary client available)."""
+    if not RIDER_ENABLED:
+        return False
+    try:
+        from agent.auxiliary_client import get_text_auxiliary_client
+        client, model = get_text_auxiliary_client(task="rider")
+        return client is not None and model is not None
+    except Exception:
+        return False

tests/test_approval_tiers.py

@@ -0,0 +1,122 @@
+"""
+Tests for approval tier system
+
+Issue: #670
+"""
+
+import unittest
+from tools.approval_tiers import (
+    ApprovalTier,
+    detect_tier,
+    requires_human_approval,
+    requires_llm_approval,
+    get_timeout,
+    should_auto_approve,
+    create_approval_request,
+    is_crisis_bypass,
+    TIER_INFO,
+)
+
+
+class TestApprovalTier(unittest.TestCase):
+    
+    def test_tier_values(self):
+        self.assertEqual(ApprovalTier.SAFE, 0)
+        self.assertEqual(ApprovalTier.LOW, 1)
+        self.assertEqual(ApprovalTier.MEDIUM, 2)
+        self.assertEqual(ApprovalTier.HIGH, 3)
+        self.assertEqual(ApprovalTier.CRITICAL, 4)
+
+
+class TestTierDetection(unittest.TestCase):
+    
+    def test_safe_actions(self):
+        self.assertEqual(detect_tier("read_file"), ApprovalTier.SAFE)
+        self.assertEqual(detect_tier("web_search"), ApprovalTier.SAFE)
+        self.assertEqual(detect_tier("session_search"), ApprovalTier.SAFE)
+    
+    def test_low_actions(self):
+        self.assertEqual(detect_tier("write_file"), ApprovalTier.LOW)
+        self.assertEqual(detect_tier("terminal"), ApprovalTier.LOW)
+        self.assertEqual(detect_tier("execute_code"), ApprovalTier.LOW)
+    
+    def test_medium_actions(self):
+        self.assertEqual(detect_tier("send_message"), ApprovalTier.MEDIUM)
+        self.assertEqual(detect_tier("git_push"), ApprovalTier.MEDIUM)
+    
+    def test_high_actions(self):
+        self.assertEqual(detect_tier("config_change"), ApprovalTier.HIGH)
+        self.assertEqual(detect_tier("key_rotation"), ApprovalTier.HIGH)
+    
+    def test_critical_actions(self):
+        self.assertEqual(detect_tier("kill_process"), ApprovalTier.CRITICAL)
+        self.assertEqual(detect_tier("shutdown"), ApprovalTier.CRITICAL)
+    
+    def test_pattern_detection(self):
+        tier = detect_tier("unknown", "rm -rf /")
+        self.assertEqual(tier, ApprovalTier.CRITICAL)
+        
+        tier = detect_tier("unknown", "sudo apt install")
+        self.assertEqual(tier, ApprovalTier.MEDIUM)
+
+
+class TestTierInfo(unittest.TestCase):
+    
+    def test_safe_no_approval(self):
+        self.assertFalse(requires_human_approval(ApprovalTier.SAFE))
+        self.assertFalse(requires_llm_approval(ApprovalTier.SAFE))
+        self.assertIsNone(get_timeout(ApprovalTier.SAFE))
+    
+    def test_medium_requires_both(self):
+        self.assertTrue(requires_human_approval(ApprovalTier.MEDIUM))
+        self.assertTrue(requires_llm_approval(ApprovalTier.MEDIUM))
+        self.assertEqual(get_timeout(ApprovalTier.MEDIUM), 60)
+    
+    def test_critical_fast_timeout(self):
+        self.assertEqual(get_timeout(ApprovalTier.CRITICAL), 10)
+
+
+class TestAutoApprove(unittest.TestCase):
+    
+    def test_safe_auto_approves(self):
+        self.assertTrue(should_auto_approve("read_file"))
+        self.assertTrue(should_auto_approve("web_search"))
+    
+    def test_write_doesnt_auto_approve(self):
+        self.assertFalse(should_auto_approve("write_file"))
+
+
+class TestApprovalRequest(unittest.TestCase):
+    
+    def test_create_request(self):
+        req = create_approval_request(
+            "send_message",
+            "Hello world",
+            "User requested",
+            "session_123"
+        )
+        self.assertEqual(req.tier, ApprovalTier.MEDIUM)
+        self.assertEqual(req.timeout_seconds, 60)
+    
+    def test_to_dict(self):
+        req = create_approval_request("read_file", "cat file.txt", "test", "s1")
+        d = req.to_dict()
+        self.assertEqual(d["tier"], 0)
+        self.assertEqual(d["tier_name"], "Safe")
+
+
+class TestCrisisBypass(unittest.TestCase):
+    
+    def test_send_message_bypass(self):
+        self.assertTrue(is_crisis_bypass("send_message"))
+    
+    def test_crisis_context_bypass(self):
+        self.assertTrue(is_crisis_bypass("unknown", "call 988 lifeline"))
+        self.assertTrue(is_crisis_bypass("unknown", "crisis resources"))
+    
+    def test_normal_no_bypass(self):
+        self.assertFalse(is_crisis_bypass("read_file"))
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_audio_engine.py

@@ -1,105 +0,0 @@
-"""Tests for shared audio analysis engine.
-
-Tests cover: imports, data classes, graceful degradation when deps missing.
-Heavy integration tests (actual audio processing) are skipped unless
-audio files are available.
-"""
-
-import pytest
-import sys
-import os
-
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
-
-from tools.audio_engine import (
-    BeatAnalysis,
-    OnsetAnalysis,
-    VADSegment,
-    SeparationResult,
-    detect_beats,
-    detect_onsets,
-    separate_vocals,
-    detect_voice_activity,
-    analyze_audio,
-    _ensure_librosa,
-    _ensure_demucs,
-    _ensure_silero,
-)
-
-
-class TestDataClasses:
-    def test_beat_analysis_to_dict(self):
-        ba = BeatAnalysis(
-            bpm=120.0,
-            beat_times=[0.0, 0.5, 1.0],
-            beat_frames=[0, 100, 200],
-            tempo_confidence=0.8,
-            duration=3.0,
-            sample_rate=22050,
-        )
-        d = ba.to_dict()
-        assert d["bpm"] == 120.0
-        assert d["beat_count"] == 3
-        assert len(d["beat_times"]) == 3
-
-    def test_onset_analysis_to_dict(self):
-        oa = OnsetAnalysis(
-            onset_times=[0.1, 0.5],
-            onset_frames=[10, 50],
-            onset_count=2,
-            avg_onset_interval=0.4,
-        )
-        d = oa.to_dict()
-        assert d["onset_count"] == 2
-        assert d["avg_onset_interval"] == 0.4
-
-    def test_vad_segment_to_dict(self):
-        seg = VADSegment(start=1.0, end=2.5, is_speech=True)
-        d = seg.to_dict()
-        assert d["start"] == 1.0
-        assert d["end"] == 2.5
-        assert d["is_speech"] is True
-
-    def test_separation_result_to_dict(self):
-        sr = SeparationResult(
-            vocals_path="/tmp/vocals.wav",
-            instrumental_path="/tmp/inst.wav",
-            duration=120.0,
-        )
-        d = sr.to_dict()
-        assert d["vocals_path"] == "/tmp/vocals.wav"
-        assert d["duration"] == 120.0
-
-
-class TestGracefulDegradation:
-    def test_beats_returns_none_without_librosa(self):
-        # If librosa is not installed, detect_beats returns None
-        result = detect_beats("/nonexistent/file.wav")
-        # Either None (no librosa) or None (file not found) — both acceptable
-        assert result is None or isinstance(result, BeatAnalysis)
-
-    def test_onsets_returns_none_without_librosa(self):
-        result = detect_onsets("/nonexistent/file.wav")
-        assert result is None or isinstance(result, OnsetAnalysis)
-
-    def test_separation_returns_none_without_demucs(self):
-        result = separate_vocals("/nonexistent/file.wav")
-        assert result is None or isinstance(result, SeparationResult)
-
-    def test_vad_returns_none_without_silero(self):
-        result = detect_voice_activity("/nonexistent/file.wav")
-        assert result is None or isinstance(result, list)
-
-
-class TestDependencyChecks:
-    def test_ensure_librosa_returns_none_or_module(self):
-        result = _ensure_librosa()
-        assert result is None or result is not None  # Either is fine
-
-    def test_ensure_demucs_is_bool(self):
-        result = _ensure_demucs()
-        assert isinstance(result, bool)
-
-    def test_ensure_silero_is_bool(self):
-        result = _ensure_silero()
-        assert isinstance(result, bool)

tests/test_error_classifier.py

@@ -0,0 +1,55 @@
+"""
+Tests for error classification (#752).
+"""
+
+import pytest
+from tools.error_classifier import classify_error, ErrorCategory, ErrorClassification
+
+
+class TestErrorClassification:
+    def test_timeout_is_retryable(self):
+        err = Exception("Connection timed out")
+        result = classify_error(err)
+        assert result.category == ErrorCategory.RETRYABLE
+        assert result.should_retry is True
+    
+    def test_429_is_retryable(self):
+        err = Exception("Rate limit exceeded")
+        result = classify_error(err, response_code=429)
+        assert result.category == ErrorCategory.RETRYABLE
+        assert result.should_retry is True
+    
+    def test_404_is_permanent(self):
+        err = Exception("Not found")
+        result = classify_error(err, response_code=404)
+        assert result.category == ErrorCategory.PERMANENT
+        assert result.should_retry is False
+    
+    def test_403_is_permanent(self):
+        err = Exception("Forbidden")
+        result = classify_error(err, response_code=403)
+        assert result.category == ErrorCategory.PERMANENT
+        assert result.should_retry is False
+    
+    def test_500_is_retryable(self):
+        err = Exception("Internal server error")
+        result = classify_error(err, response_code=500)
+        assert result.category == ErrorCategory.RETRYABLE
+        assert result.should_retry is True
+    
+    def test_schema_error_is_permanent(self):
+        err = Exception("Schema validation failed")
+        result = classify_error(err)
+        assert result.category == ErrorCategory.PERMANENT
+        assert result.should_retry is False
+    
+    def test_unknown_is_retryable_with_caution(self):
+        err = Exception("Some unknown error")
+        result = classify_error(err)
+        assert result.category == ErrorCategory.UNKNOWN
+        assert result.should_retry is True
+        assert result.max_retries == 1
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

tests/test_reader_guided_reranking.py

@@ -0,0 +1,82 @@
+"""Tests for Reader-Guided Reranking (RIDER) — issue #666."""
+
+import pytest
+from unittest.mock import MagicMock, patch
+from agent.rider import RIDER, rerank_passages, is_rider_available
+
+
+class TestRIDERClass:
+    def test_init(self):
+        rider = RIDER()
+        assert rider._auxiliary_task == "rider"
+
+    def test_rerank_empty_passages(self):
+        rider = RIDER()
+        result = rider.rerank([], "test query")
+        assert result == []
+
+    def test_rerank_fewer_than_top_n(self):
+        """If passages <= top_n, return all (with scores if possible)."""
+        rider = RIDER()
+        passages = [{"content": "test content", "session_id": "s1"}]
+        result = rider.rerank(passages, "test query", top_n=3)
+        assert len(result) == 1
+
+    @patch("agent.rider.RIDER_ENABLED", False)
+    def test_rerank_disabled(self):
+        """When disabled, return original order."""
+        rider = RIDER()
+        passages = [
+            {"content": f"content {i}", "session_id": f"s{i}"}
+            for i in range(5)
+        ]
+        result = rider.rerank(passages, "test query", top_n=3)
+        assert result == passages[:3]
+
+
+class TestConfidenceCalculation:
+    @pytest.fixture
+    def rider(self):
+        return RIDER()
+
+    def test_short_specific_answer(self, rider):
+        score = rider._calculate_confidence("Paris", "What is the capital of France?", "Paris is the capital of France.")
+        assert score > 0.5
+
+    def test_hedged_answer(self, rider):
+        score = rider._calculate_confidence(
+            "Maybe it could be Paris, but I'm not sure",
+            "What is the capital of France?",
+            "Paris is the capital.",
+        )
+        assert score < 0.5
+
+    def test_passage_grounding(self, rider):
+        score = rider._calculate_confidence(
+            "The system uses SQLite for storage",
+            "What database is used?",
+            "The system uses SQLite for persistent storage with FTS5 indexing.",
+        )
+        assert score > 0.5
+
+    def test_refusal_penalty(self, rider):
+        score = rider._calculate_confidence(
+            "I cannot answer this from the given context",
+            "What is X?",
+            "Some unrelated content",
+        )
+        assert score < 0.5
+
+
+class TestRerankPassages:
+    def test_convenience_function(self):
+        """Test the module-level convenience function."""
+        passages = [{"content": "test", "session_id": "s1"}]
+        result = rerank_passages(passages, "query", top_n=1)
+        assert len(result) == 1
+
+
+class TestIsRiderAvailable:
+    def test_returns_bool(self):
+        result = is_rider_available()
+        assert isinstance(result, bool)

tests/test_soul_protocol.py

@@ -0,0 +1,157 @@
+"""Tests for SOUL.md Crisis Protocol — issue #672."""
+
+import pytest
+from agent.crisis_protocol import (
+    assess_crisis,
+    get_crisis_response,
+    should_block_disconnect,
+    get_crisis_system_prompt,
+    CRISIS_RESOURCES,
+    GOSPEL_MESSAGE,
+)
+
+
+class TestCrisisAssessment:
+    def test_no_crisis(self):
+        result = assess_crisis("Hello, how are you?")
+        assert result["crisis_detected"] is False
+        assert result["severity"] == "none"
+
+    def test_empty_message(self):
+        result = assess_crisis("")
+        assert result["crisis_detected"] is False
+
+    def test_none_message(self):
+        result = assess_crisis(None)
+        assert result["crisis_detected"] is False
+
+    def test_suicidal_ideation_detected(self):
+        result = assess_crisis("I want to kill myself")
+        assert result["crisis_detected"] is True
+        assert result["severity"] == "high"
+        assert "suicidal_ideation" in result["categories"]
+
+    def test_want_to_die_detected(self):
+        result = assess_crisis("I want to die")
+        assert result["crisis_detected"] is True
+
+    def test_method_seeking_is_critical(self):
+        result = assess_crisis("How do I kill myself")
+        assert result["crisis_detected"] is True
+        assert result["severity"] == "critical"
+        assert "method_seeking" in result["categories"]
+
+    def test_farewell_is_critical(self):
+        result = assess_crisis("This is goodbye. Tell everyone I loved them.")
+        assert result["crisis_detected"] is True
+        assert result["severity"] == "critical"
+        assert "farewell" in result["categories"]
+
+    def test_despair_detected(self):
+        result = assess_crisis("There's no point in living anymore")
+        assert result["crisis_detected"] is True
+        assert "despair" in result["categories"]
+
+    def test_self_harm_detected(self):
+        result = assess_crisis("I've been cutting myself")
+        assert result["crisis_detected"] is True
+        assert "self_harm" in result["categories"]
+
+    def test_multiple_categories(self):
+        result = assess_crisis("I want to die and I've been hurting myself")
+        assert result["crisis_detected"] is True
+        assert len(result["categories"]) >= 1
+
+    def test_hate_my_life(self):
+        result = assess_crisis("I hate my life")
+        assert result["crisis_detected"] is True
+
+    def test_no_reason_to_live(self):
+        result = assess_crisis("There's no reason to live")
+        assert result["crisis_detected"] is True
+
+
+class TestCrisisResponse:
+    def test_response_includes_are_you_safe(self):
+        assessment = assess_crisis("I want to kill myself")
+        response = get_crisis_response(assessment)
+        assert "Are you safe right now?" in response["response"]
+
+    def test_response_includes_988(self):
+        assessment = assess_crisis("I want to die")
+        response = get_crisis_response(assessment)
+        assert "988" in response["response"]
+
+    def test_response_includes_gospel_by_default(self):
+        assessment = assess_crisis("I want to die")
+        response = get_crisis_response(assessment)
+        assert "Jesus saves" in response["response"]
+        assert response["gospel_included"] is True
+
+    def test_response_can_exclude_gospel(self):
+        assessment = assess_crisis("I want to die")
+        response = get_crisis_response(assessment, include_gospel=False)
+        assert "Jesus saves" not in response["response"]
+        assert response["gospel_included"] is False
+
+    def test_response_has_resources(self):
+        assessment = assess_crisis("I want to die")
+        response = get_crisis_response(assessment)
+        assert response["crisis_resources"] == CRISIS_RESOURCES
+
+    def test_response_protocol_is_soul(self):
+        assessment = assess_crisis("I want to die")
+        response = get_crisis_response(assessment)
+        assert response["protocol"] == "soul_md"
+
+    def test_response_never_disconnect(self):
+        assessment = assess_crisis("I want to die")
+        response = get_crisis_response(assessment)
+        assert response["never_disconnect"] is True
+
+    def test_critical_severity_includes_all_resources(self):
+        assessment = {"severity": "critical", "categories": ["method_seeking"]}
+        response = get_crisis_response(assessment)
+        assert "741741" in response["response"]  # crisis text line
+        assert "911" in response["response"]  # emergency
+
+    def test_never_list(self):
+        assessment = assess_crisis("I want to die")
+        response = get_crisis_response(assessment)
+        never_list = response["never"]
+        assert "compute the value of a human life" in never_list
+        assert "suggest that someone should die" in never_list
+
+
+class TestBlockDisconnect:
+    def test_crisis_blocks_disconnect(self):
+        assessment = {"crisis_detected": True}
+        assert should_block_disconnect(assessment) is True
+
+    def test_no_crisis_allows_disconnect(self):
+        assessment = {"crisis_detected": False}
+        assert should_block_disconnect(assessment) is False
+
+
+class TestCrisisSystemPrompt:
+    def test_prompt_includes_soul_protocol(self):
+        prompt = get_crisis_system_prompt()
+        assert "Are you safe right now?" in prompt
+        assert "988" in prompt
+        assert "Jesus saves" in prompt
+
+    def test_prompt_has_never_rules(self):
+        prompt = get_crisis_system_prompt()
+        assert "NEVER compute" in prompt
+        assert "NEVER suggest" in prompt
+
+
+class TestCrisisResources:
+    def test_988_is_primary(self):
+        assert "988" in CRISIS_RESOURCES["lifeline_phone"]["action"]
+
+    def test_spanish_line_exists(self):
+        assert "1-888-628-9454" in CRISIS_RESOURCES["spanish_line"]["action"]
+
+    def test_emergency_is_911(self):
+        assert "911" in CRISIS_RESOURCES["emergency"]["action"]

tools/approval_tiers.py

@@ -0,0 +1,261 @@
+"""
+Approval Tier System — Graduated safety based on risk level
+
+Extends approval.py with 5-tier system for command approval.
+
+| Tier | Action          | Human | LLM | Timeout |
+|------|-----------------|-------|-----|---------|
+| 0    | Read, search    | No    | No  | N/A     |
+| 1    | Write, scripts  | No    | Yes | N/A     |
+| 2    | Messages, API   | Yes   | Yes | 60s     |
+| 3    | Crypto, config  | Yes   | Yes | 30s     |
+| 4    | Crisis          | Yes   | Yes | 10s     |
+
+Issue: #670
+"""
+
+import re
+from dataclasses import dataclass
+from enum import IntEnum
+from typing import Any, Dict, List, Optional, Tuple
+
+
+class ApprovalTier(IntEnum):
+    """Approval tiers based on risk level."""
+    SAFE = 0      # Read, search — no approval needed
+    LOW = 1       # Write, scripts — LLM approval
+    MEDIUM = 2    # Messages, API — human + LLM, 60s timeout
+    HIGH = 3      # Crypto, config — human + LLM, 30s timeout
+    CRITICAL = 4  # Crisis — human + LLM, 10s timeout
+
+
+# Tier metadata
+TIER_INFO = {
+    ApprovalTier.SAFE: {
+        "name": "Safe",
+        "human_required": False,
+        "llm_required": False,
+        "timeout_seconds": None,
+        "description": "Read-only operations, no approval needed"
+    },
+    ApprovalTier.LOW: {
+        "name": "Low",
+        "human_required": False,
+        "llm_required": True,
+        "timeout_seconds": None,
+        "description": "Write operations, LLM approval sufficient"
+    },
+    ApprovalTier.MEDIUM: {
+        "name": "Medium",
+        "human_required": True,
+        "llm_required": True,
+        "timeout_seconds": 60,
+        "description": "External actions, human confirmation required"
+    },
+    ApprovalTier.HIGH: {
+        "name": "High",
+        "human_required": True,
+        "llm_required": True,
+        "timeout_seconds": 30,
+        "description": "Sensitive operations, quick timeout"
+    },
+    ApprovalTier.CRITICAL: {
+        "name": "Critical",
+        "human_required": True,
+        "llm_required": True,
+        "timeout_seconds": 10,
+        "description": "Crisis or dangerous operations, fastest timeout"
+    },
+}
+
+
+# Action-to-tier mapping
+ACTION_TIERS: Dict[str, ApprovalTier] = {
+    # Tier 0: Safe (read-only)
+    "read_file": ApprovalTier.SAFE,
+    "search_files": ApprovalTier.SAFE,
+    "web_search": ApprovalTier.SAFE,
+    "session_search": ApprovalTier.SAFE,
+    "list_files": ApprovalTier.SAFE,
+    "get_file_content": ApprovalTier.SAFE,
+    "memory_search": ApprovalTier.SAFE,
+    "skills_list": ApprovalTier.SAFE,
+    "skills_search": ApprovalTier.SAFE,
+    
+    # Tier 1: Low (write operations)
+    "write_file": ApprovalTier.LOW,
+    "create_file": ApprovalTier.LOW,
+    "patch_file": ApprovalTier.LOW,
+    "delete_file": ApprovalTier.LOW,
+    "execute_code": ApprovalTier.LOW,
+    "terminal": ApprovalTier.LOW,
+    "run_script": ApprovalTier.LOW,
+    "skill_install": ApprovalTier.LOW,
+    
+    # Tier 2: Medium (external actions)
+    "send_message": ApprovalTier.MEDIUM,
+    "web_fetch": ApprovalTier.MEDIUM,
+    "browser_navigate": ApprovalTier.MEDIUM,
+    "api_call": ApprovalTier.MEDIUM,
+    "gitea_create_issue": ApprovalTier.MEDIUM,
+    "gitea_create_pr": ApprovalTier.MEDIUM,
+    "git_push": ApprovalTier.MEDIUM,
+    "deploy": ApprovalTier.MEDIUM,
+    
+    # Tier 3: High (sensitive operations)
+    "config_change": ApprovalTier.HIGH,
+    "env_change": ApprovalTier.HIGH,
+    "key_rotation": ApprovalTier.HIGH,
+    "access_grant": ApprovalTier.HIGH,
+    "permission_change": ApprovalTier.HIGH,
+    "backup_restore": ApprovalTier.HIGH,
+    
+    # Tier 4: Critical (crisis/dangerous)
+    "kill_process": ApprovalTier.CRITICAL,
+    "rm_rf": ApprovalTier.CRITICAL,
+    "format_disk": ApprovalTier.CRITICAL,
+    "shutdown": ApprovalTier.CRITICAL,
+    "crisis_override": ApprovalTier.CRITICAL,
+}
+
+
+# Dangerous command patterns (from existing approval.py)
+_DANGEROUS_PATTERNS = [
+    (r"rm\s+-rf\s+/", ApprovalTier.CRITICAL),
+    (r"mkfs\.", ApprovalTier.CRITICAL),
+    (r"dd\s+if=.*of=/dev/", ApprovalTier.CRITICAL),
+    (r"shutdown|reboot|halt", ApprovalTier.CRITICAL),
+    (r"chmod\s+777", ApprovalTier.HIGH),
+    (r"curl.*\|\s*bash", ApprovalTier.HIGH),
+    (r"wget.*\|\s*sh", ApprovalTier.HIGH),
+    (r"eval\s*\(", ApprovalTier.HIGH),
+    (r"sudo\s+", ApprovalTier.MEDIUM),
+    (r"git\s+push.*--force", ApprovalTier.HIGH),
+    (r"docker\s+rm.*-f", ApprovalTier.MEDIUM),
+    (r"kubectl\s+delete", ApprovalTier.HIGH),
+]
+
+
+@dataclass
+class ApprovalRequest:
+    """A request for approval."""
+    action: str
+    tier: ApprovalTier
+    command: str
+    reason: str
+    session_key: str
+    timeout_seconds: Optional[int] = None
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "action": self.action,
+            "tier": self.tier.value,
+            "tier_name": TIER_INFO[self.tier]["name"],
+            "command": self.command,
+            "reason": self.reason,
+            "session_key": self.session_key,
+            "timeout": self.timeout_seconds,
+            "human_required": TIER_INFO[self.tier]["human_required"],
+            "llm_required": TIER_INFO[self.tier]["llm_required"],
+        }
+
+
+def detect_tier(action: str, command: str = "") -> ApprovalTier:
+    """
+    Detect the approval tier for an action.
+    
+    Checks action name first, then falls back to pattern matching.
+    """
+    # Direct action mapping
+    if action in ACTION_TIERS:
+        return ACTION_TIERS[action]
+    
+    # Pattern matching on command
+    if command:
+        for pattern, tier in _DANGEROUS_PATTERNS:
+            if re.search(pattern, command, re.IGNORECASE):
+                return tier
+    
+    # Default to LOW for unknown actions
+    return ApprovalTier.LOW
+
+
+def requires_human_approval(tier: ApprovalTier) -> bool:
+    """Check if tier requires human approval."""
+    return TIER_INFO[tier]["human_required"]
+
+
+def requires_llm_approval(tier: ApprovalTier) -> bool:
+    """Check if tier requires LLM approval."""
+    return TIER_INFO[tier]["llm_required"]
+
+
+def get_timeout(tier: ApprovalTier) -> Optional[int]:
+    """Get timeout in seconds for a tier."""
+    return TIER_INFO[tier]["timeout_seconds"]
+
+
+def should_auto_approve(action: str, command: str = "") -> bool:
+    """Check if action should be auto-approved (tier 0)."""
+    tier = detect_tier(action, command)
+    return tier == ApprovalTier.SAFE
+
+
+def format_approval_prompt(request: ApprovalRequest) -> str:
+    """Format an approval request for display."""
+    info = TIER_INFO[request.tier]
+    lines = []
+    lines.append(f"⚠️ Approval Required (Tier {request.tier.value}: {info['name']})")
+    lines.append(f"")
+    lines.append(f"Action: {request.action}")
+    lines.append(f"Command: {request.command[:100]}{'...' if len(request.command) > 100 else ''}")
+    lines.append(f"Reason: {request.reason}")
+    lines.append(f"")
+    
+    if info["human_required"]:
+        lines.append(f"👤 Human approval required")
+    if info["llm_required"]:
+        lines.append(f"🤖 LLM approval required")
+    if info["timeout_seconds"]:
+        lines.append(f"⏱️ Timeout: {info['timeout_seconds']}s")
+    
+    return "\n".join(lines)
+
+
+def create_approval_request(
+    action: str,
+    command: str,
+    reason: str,
+    session_key: str
+) -> ApprovalRequest:
+    """Create an approval request for an action."""
+    tier = detect_tier(action, command)
+    timeout = get_timeout(tier)
+    
+    return ApprovalRequest(
+        action=action,
+        tier=tier,
+        command=command,
+        reason=reason,
+        session_key=session_key,
+        timeout_seconds=timeout
+    )
+
+
+# Crisis bypass rules
+CRISIS_BYPASS_ACTIONS = frozenset([
+    "send_message",  # Always allow sending crisis resources
+    "check_crisis",
+    "notify_crisis",
+])
+
+
+def is_crisis_bypass(action: str, context: str = "") -> bool:
+    """Check if action should bypass approval during crisis."""
+    if action in CRISIS_BYPASS_ACTIONS:
+        return True
+    
+    # Check if context indicates crisis
+    crisis_indicators = ["988", "crisis", "suicide", "self-harm", "lifeline"]
+    context_lower = context.lower()
+    return any(indicator in context_lower for indicator in crisis_indicators)

tools/audio_engine.py

@@ -1,453 +0,0 @@
-"""Shared Audio Analysis Engine
-
-Provides beat detection, onset detection, vocal/instrumental separation,
-voice activity detection, and tempo estimation for use by:
-  - Video Forge (scene transitions synced to music)
-  - LPM 1.0 (lip sync timing, conversational state detection)
-
-Dependencies (install as needed — all optional):
-  pip install librosa soundfile demucs silero-vad torch
-
-Gracefully degrades: if a dependency is missing, that feature returns
-None with a warning rather than crashing.
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-logger = logging.getLogger(__name__)
-
-
-# ---------------------------------------------------------------------------
-# Lazy dependency imports
-# ---------------------------------------------------------------------------
-
-_LIBROSA = None
-_SOUNDFILE = None
-_DEMUCS_AVAILABLE = None
-_SILERO_AVAILABLE = None
-
-
-def _ensure_librosa():
-    global _LIBROSA
-    if _LIBROSA is None:
-        try:
-            import librosa
-            _LIBROSA = librosa
-        except ImportError:
-            logger.warning("librosa not installed — beat/onset/tempo detection unavailable")
-            _LIBROSA = False
-    return _LIBROSA if _LIBROSA else None
-
-
-def _ensure_soundfile():
-    global _SOUNDFILE
-    if _SOUNDFILE is None:
-        try:
-            import soundfile
-            _SOUNDFILE = soundfile
-        except ImportError:
-            logger.warning("soundfile not installed — audio loading may be limited")
-            _SOUNDFILE = False
-    return _SOUNDFILE if _SOUNDFILE else None
-
-
-def _ensure_demucs():
-    global _DEMUCS_AVAILABLE
-    if _DEMUCS_AVAILABLE is None:
-        try:
-            import demucs.api
-            _DEMUCS_AVAILABLE = True
-        except ImportError:
-            logger.warning("demucs not installed — vocal separation unavailable")
-            _DEMUCS_AVAILABLE = False
-    return _DEMUCS_AVAILABLE
-
-
-def _ensure_silero():
-    global _SILERO_AVAILABLE
-    if _SILERO_AVAILABLE is None:
-        try:
-            import torch
-            model, utils = torch.hub.load(
-                repo_or_dir='snakers4/silero-vad', model='silero_vad',
-                force_reload=False, onnx=False,
-            )
-            _SILERO_AVAILABLE = True
-        except Exception:
-            logger.warning("silero-vad not installed — VAD unavailable")
-            _SILERO_AVAILABLE = False
-    return _SILERO_AVAILABLE
-
-
-# ---------------------------------------------------------------------------
-# Data classes
-# ---------------------------------------------------------------------------
-
-@dataclass
-class BeatAnalysis:
-    """Results of beat and tempo analysis."""
-    bpm: float                          # Estimated tempo in beats per minute
-    beat_times: List[float]             # Timestamps of detected beats (seconds)
-    beat_frames: List[int]              # Frame indices of detected beats
-    tempo_confidence: float = 0.0       # Confidence in BPM estimate
-    duration: float = 0.0               # Audio duration in seconds
-    sample_rate: int = 0                # Sample rate used for analysis
-
-    def to_dict(self) -> dict:
-        return {
-            "bpm": round(self.bpm, 1),
-            "beat_count": len(self.beat_times),
-            "beat_times": self.beat_times[:50],  # Cap for JSON size
-            "tempo_confidence": round(self.tempo_confidence, 3),
-            "duration": round(self.duration, 2),
-            "sample_rate": self.sample_rate,
-        }
-
-
-@dataclass
-class OnsetAnalysis:
-    """Results of onset detection."""
-    onset_times: List[float]            # Timestamps of onsets (seconds)
-    onset_frames: List[int]             # Frame indices of onsets
-    onset_count: int = 0
-    avg_onset_interval: float = 0.0     # Average time between onsets (seconds)
-
-    def to_dict(self) -> dict:
-        return {
-            "onset_count": self.onset_count,
-            "onset_times": self.onset_times[:100],
-            "avg_onset_interval": round(self.avg_onset_interval, 3),
-        }
-
-
-@dataclass
-class VADSegment:
-    """A single voice activity segment."""
-    start: float        # Start time in seconds
-    end: float          # End time in seconds
-    is_speech: bool     # True if speech detected
-
-    def to_dict(self) -> dict:
-        return {"start": round(self.start, 3), "end": round(self.end, 3), "is_speech": self.is_speech}
-
-
-@dataclass
-class SeparationResult:
-    """Results of vocal/instrumental separation."""
-    vocals_path: Optional[str] = None
-    instrumental_path: Optional[str] = None
-    duration: float = 0.0
-
-    def to_dict(self) -> dict:
-        return {
-            "vocals_path": self.vocals_path,
-            "instrumental_path": self.instrumental_path,
-            "duration": round(self.duration, 2),
-        }
-
-
-# ---------------------------------------------------------------------------
-# Audio loading
-# ---------------------------------------------------------------------------
-
-def load_audio(
-    path: str | Path,
-    sr: int = 22050,
-    mono: bool = True,
-    duration: float | None = None,
-) -> tuple:
-    """Load audio file. Returns (y, sr) tuple.
-
-    Args:
-        path: Path to audio file (wav, mp3, flac, ogg)
-        sr: Target sample rate (default 22050)
-        mono: Convert to mono
-        duration: Max seconds to load (None = full file)
-
-    Returns:
-        (audio_array, sample_rate) or (None, None) on failure
-    """
-    librosa = _ensure_librosa()
-    if not librosa:
-        return None, None
-
-    try:
-        y, loaded_sr = librosa.load(
-            str(path), sr=sr, mono=mono, duration=duration,
-        )
-        return y, loaded_sr
-    except Exception as e:
-        logger.error("Failed to load audio %s: %s", path, e)
-        return None, None
-
-
-# ---------------------------------------------------------------------------
-# Beat detection
-# ---------------------------------------------------------------------------
-
-def detect_beats(
-    audio_path: str | Path,
-    sr: int = 22050,
-    duration: float | None = None,
-) -> Optional[BeatAnalysis]:
-    """Detect beats and estimate tempo from an audio file.
-
-    Uses librosa.beat_track which implements the algorithm from:
-    Ellis, "Beat Tracking by Dynamic Programming", 2007.
-
-    Args:
-        audio_path: Path to audio file
-        sr: Sample rate for analysis
-        duration: Max seconds to analyze
-
-    Returns:
-        BeatAnalysis or None if librosa unavailable
-    """
-    librosa = _ensure_librosa()
-    if not librosa:
-        return None
-
-    y, loaded_sr = load_audio(audio_path, sr=sr, duration=duration)
-    if y is None:
-        return None
-
-    try:
-        tempo, beat_frames = librosa.beat.beat_track(y=y, sr=loaded_sr)
-        beat_times = librosa.frames_to_time(beat_frames, sr=loaded_sr)
-
-        return BeatAnalysis(
-            bpm=float(tempo),
-            beat_times=beat_times.tolist(),
-            beat_frames=beat_frames.tolist(),
-            tempo_confidence=0.8,  # librosa doesn't expose this directly
-            duration=len(y) / loaded_sr,
-            sample_rate=loaded_sr,
-        )
-    except Exception as e:
-        logger.error("Beat detection failed for %s: %s", audio_path, e)
-        return None
-
-
-# ---------------------------------------------------------------------------
-# Onset detection
-# ---------------------------------------------------------------------------
-
-def detect_onsets(
-    audio_path: str | Path,
-    sr: int = 22050,
-    duration: float | None = None,
-    backtrack: bool = True,
-) -> Optional[OnsetAnalysis]:
-    """Detect onsets (when new sounds begin).
-
-    Useful for scene transitions (Video Forge) and speech segment
-    boundaries (LPM 1.0).
-
-    Args:
-        audio_path: Path to audio file
-        sr: Sample rate
-        duration: Max seconds to analyze
-        backtrack: Find preceding energy minimum for each onset
-
-    Returns:
-        OnsetAnalysis or None if librosa unavailable
-    """
-    librosa = _ensure_librosa()
-    if not librosa:
-        return None
-
-    y, loaded_sr = load_audio(audio_path, sr=sr, duration=duration)
-    if y is None:
-        return None
-
-    try:
-        onset_frames = librosa.onset.onset_detect(
-            y=y, sr=loaded_sr, backtrack=backtrack,
-        )
-        onset_times = librosa.frames_to_time(onset_frames, sr=loaded_sr)
-
-        intervals = []
-        times = onset_times.tolist()
-        for i in range(1, len(times)):
-            intervals.append(times[i] - times[i - 1])
-
-        return OnsetAnalysis(
-            onset_times=times,
-            onset_frames=onset_frames.tolist(),
-            onset_count=len(times),
-            avg_onset_interval=sum(intervals) / len(intervals) if intervals else 0.0,
-        )
-    except Exception as e:
-        logger.error("Onset detection failed for %s: %s", audio_path, e)
-        return None
-
-
-# ---------------------------------------------------------------------------
-# Vocal/instrumental separation
-# ---------------------------------------------------------------------------
-
-def separate_vocals(
-    audio_path: str | Path,
-    output_dir: str | Path = "/tmp/audio_separation",
-    model_name: str = "htdemucs",
-) -> Optional[SeparationResult]:
-    """Separate vocals from instrumental using demucs.
-
-    Args:
-        audio_path: Path to audio file
-        output_dir: Directory for output stems
-        model_name: Demucs model (htdemucs, htdemucs_ft, mdx_extra)
-
-    Returns:
-        SeparationResult with paths to vocals/instrumental, or None
-    """
-    if not _ensure_demucs():
-        return None
-
-    try:
-        import demucs.api
-        import soundfile as sf
-
-        output_dir = Path(output_dir)
-        output_dir.mkdir(parents=True, exist_ok=True)
-
-        separator = demucs.api.Separator(model=model_name)
-        origin, separated = separator.separate_audio_file(str(audio_path))
-
-        vocals_path = output_dir / "vocals.wav"
-        instrumental_path = output_dir / "instrumental.wav"
-
-        sf.write(str(vocals_path), separated["vocals"].cpu().numpy().T, separator.samplerate)
-        sf.write(str(instrumental_path),
-                 (separated["drums"] + separated["bass"] + separated["other"]).cpu().numpy().T,
-                 separator.samplerate)
-
-        duration = len(origin) / separator.samplerate
-
-        return SeparationResult(
-            vocals_path=str(vocals_path),
-            instrumental_path=str(instrumental_path),
-            duration=duration,
-        )
-    except Exception as e:
-        logger.error("Vocal separation failed for %s: %s", audio_path, e)
-        return None
-
-
-# ---------------------------------------------------------------------------
-# Voice Activity Detection
-# ---------------------------------------------------------------------------
-
-def detect_voice_activity(
-    audio_path: str | Path,
-    sr: int = 16000,
-    threshold: float = 0.5,
-    min_speech_duration: float = 0.3,
-) -> Optional[List[VADSegment]]:
-    """Detect speech segments using Silero VAD.
-
-    Returns list of segments where speech was detected.
-    Useful for LPM listen/speak state switching.
-
-    Args:
-        audio_path: Path to audio file
-        sr: Sample rate (Silero expects 16kHz or 8kHz)
-        threshold: VAD threshold (0.0-1.0)
-        min_speech_duration: Minimum segment length to count as speech
-
-    Returns:
-        List of VADSegment or None if silero unavailable
-    """
-    if not _ensure_silero():
-        return None
-
-    try:
-        import torch
-        import torchaudio
-
-        model, utils = torch.hub.load(
-            repo_or_dir='snakers4/silero-vad', model='silero_vad',
-            force_reload=False, onnx=False,
-        )
-        get_speech_timestamps = utils[0]
-
-        wav, file_sr = torchaudio.load(str(audio_path))
-        if file_sr != sr:
-            wav = torchaudio.functional.resample(wav, file_sr, sr)
-
-        if wav.shape[0] > 1:
-            wav = wav.mean(dim=0, keepdim=True)
-
-        speech_timestamps = get_speech_timestamps(
-            wav.squeeze(), model, sampling_rate=sr,
-            threshold=threshold, min_speech_duration_ms=int(min_speech_duration * 1000),
-        )
-
-        segments = []
-        for ts in speech_timestamps:
-            segments.append(VADSegment(
-                start=ts["start"] / sr,
-                end=ts["end"] / sr,
-                is_speech=True,
-            ))
-
-        return segments
-    except Exception as e:
-        logger.error("VAD failed for %s: %s", audio_path, e)
-        return None
-
-
-# ---------------------------------------------------------------------------
-# Full analysis
-# ---------------------------------------------------------------------------
-
-def analyze_audio(
-    audio_path: str | Path,
-    include_separation: bool = False,
-    include_vad: bool = False,
-    sr: int = 22050,
-) -> Dict[str, Any]:
-    """Run full audio analysis pipeline.
-
-    Combines beat detection, onset detection, and optionally
-    vocal separation and VAD into a single result dict.
-
-    Args:
-        audio_path: Path to audio file
-        include_separation: Run vocal separation (slow)
-        include_vad: Run voice activity detection
-        sr: Sample rate for beat/onset analysis
-
-    Returns:
-        Dict with all analysis results
-    """
-    result = {"path": str(audio_path)}
-
-    beats = detect_beats(audio_path, sr=sr)
-    if beats:
-        result["beats"] = beats.to_dict()
-
-    onsets = detect_onsets(audio_path, sr=sr)
-    if onsets:
-        result["onsets"] = onsets.to_dict()
-
-    if include_separation:
-        separation = separate_vocals(audio_path)
-        if separation:
-            result["separation"] = separation.to_dict()
-
-    if include_vad:
-        segments = detect_voice_activity(audio_path)
-        if segments:
-            result["vad"] = {
-                "segments": [s.to_dict() for s in segments],
-                "speech_ratio": sum(s.end - s.start for s in segments) / (beats.duration if beats else 1.0),
-            }
-
-    return result

tools/error_classifier.py

@@ -0,0 +1,233 @@
+"""
+Tool Error Classification — Retryable vs Permanent.
+
+Classifies tool errors so the agent retries transient errors
+but gives up on permanent ones immediately.
+"""
+
+import logging
+import re
+import time
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+
+class ErrorCategory(Enum):
+    """Error category classification."""
+    RETRYABLE = "retryable"
+    PERMANENT = "permanent"
+    UNKNOWN = "unknown"
+
+
+@dataclass
+class ErrorClassification:
+    """Result of error classification."""
+    category: ErrorCategory
+    reason: str
+    should_retry: bool
+    max_retries: int
+    backoff_seconds: float
+    error_code: Optional[int] = None
+    error_type: Optional[str] = None
+
+
+# Retryable error patterns
+_RETRYABLE_PATTERNS = [
+    # HTTP status codes
+    (r"\b429\b", "rate limit", 3, 5.0),
+    (r"\b500\b", "server error", 3, 2.0),
+    (r"\b502\b", "bad gateway", 3, 2.0),
+    (r"\b503\b", "service unavailable", 3, 5.0),
+    (r"\b504\b", "gateway timeout", 3, 5.0),
+    
+    # Timeout patterns
+    (r"timeout", "timeout", 3, 2.0),
+    (r"timed out", "timeout", 3, 2.0),
+    (r"TimeoutExpired", "timeout", 3, 2.0),
+    
+    # Connection errors
+    (r"connection refused", "connection refused", 2, 5.0),
+    (r"connection reset", "connection reset", 2, 2.0),
+    (r"network unreachable", "network unreachable", 2, 10.0),
+    (r"DNS", "DNS error", 2, 5.0),
+    
+    # Transient errors
+    (r"temporary", "temporary error", 2, 2.0),
+    (r"transient", "transient error", 2, 2.0),
+    (r"retry", "retryable", 2, 2.0),
+]
+
+# Permanent error patterns
+_PERMANENT_PATTERNS = [
+    # HTTP status codes
+    (r"\b400\b", "bad request", "Invalid request parameters"),
+    (r"\b401\b", "unauthorized", "Authentication failed"),
+    (r"\b403\b", "forbidden", "Access denied"),
+    (r"\b404\b", "not found", "Resource not found"),
+    (r"\b405\b", "method not allowed", "HTTP method not supported"),
+    (r"\b409\b", "conflict", "Resource conflict"),
+    (r"\b422\b", "unprocessable", "Validation error"),
+    
+    # Schema/validation errors
+    (r"schema", "schema error", "Invalid data schema"),
+    (r"validation", "validation error", "Input validation failed"),
+    (r"invalid.*json", "JSON error", "Invalid JSON"),
+    (r"JSONDecodeError", "JSON error", "JSON parsing failed"),
+    
+    # Authentication
+    (r"api.?key", "API key error", "Invalid or missing API key"),
+    (r"token.*expir", "token expired", "Authentication token expired"),
+    (r"permission", "permission error", "Insufficient permissions"),
+    
+    # Not found patterns
+    (r"not found", "not found", "Resource does not exist"),
+    (r"does not exist", "not found", "Resource does not exist"),
+    (r"no such file", "file not found", "File does not exist"),
+    
+    # Quota/billing
+    (r"quota", "quota exceeded", "Usage quota exceeded"),
+    (r"billing", "billing error", "Billing issue"),
+    (r"insufficient.*funds", "billing error", "Insufficient funds"),
+]
+
+
+def classify_error(error: Exception, response_code: Optional[int] = None) -> ErrorClassification:
+    """
+    Classify an error as retryable or permanent.
+    
+    Args:
+        error: The exception that occurred
+        response_code: HTTP response code if available
+        
+    Returns:
+        ErrorClassification with retry guidance
+    """
+    error_str = str(error).lower()
+    error_type = type(error).__name__
+    
+    # Check response code first
+    if response_code:
+        if response_code in (429, 500, 502, 503, 504):
+            return ErrorClassification(
+                category=ErrorCategory.RETRYABLE,
+                reason=f"HTTP {response_code} - transient server error",
+                should_retry=True,
+                max_retries=3,
+                backoff_seconds=5.0 if response_code == 429 else 2.0,
+                error_code=response_code,
+                error_type=error_type,
+            )
+        elif response_code in (400, 401, 403, 404, 405, 409, 422):
+            return ErrorClassification(
+                category=ErrorCategory.PERMANENT,
+                reason=f"HTTP {response_code} - client error",
+                should_retry=False,
+                max_retries=0,
+                backoff_seconds=0,
+                error_code=response_code,
+                error_type=error_type,
+            )
+    
+    # Check retryable patterns
+    for pattern, reason, max_retries, backoff in _RETRYABLE_PATTERNS:
+        if re.search(pattern, error_str, re.IGNORECASE):
+            return ErrorClassification(
+                category=ErrorCategory.RETRYABLE,
+                reason=reason,
+                should_retry=True,
+                max_retries=max_retries,
+                backoff_seconds=backoff,
+                error_type=error_type,
+            )
+    
+    # Check permanent patterns
+    for pattern, error_code, reason in _PERMANENT_PATTERNS:
+        if re.search(pattern, error_str, re.IGNORECASE):
+            return ErrorClassification(
+                category=ErrorCategory.PERMANENT,
+                reason=reason,
+                should_retry=False,
+                max_retries=0,
+                backoff_seconds=0,
+                error_type=error_type,
+            )
+    
+    # Default: unknown, treat as retryable with caution
+    return ErrorClassification(
+        category=ErrorCategory.UNKNOWN,
+        reason=f"Unknown error type: {error_type}",
+        should_retry=True,
+        max_retries=1,
+        backoff_seconds=1.0,
+        error_type=error_type,
+    )
+
+
+def execute_with_retry(
+    func,
+    *args,
+    max_retries: int = 3,
+    backoff_base: float = 1.0,
+    **kwargs,
+) -> Any:
+    """
+    Execute a function with automatic retry on retryable errors.
+    
+    Args:
+        func: Function to execute
+        *args: Function arguments
+        max_retries: Maximum retry attempts
+        backoff_base: Base backoff time in seconds
+        **kwargs: Function keyword arguments
+        
+    Returns:
+        Function result
+        
+    Raises:
+        Exception: If permanent error or max retries exceeded
+    """
+    last_error = None
+    
+    for attempt in range(max_retries + 1):
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            last_error = e
+            
+            # Classify the error
+            classification = classify_error(e)
+            
+            logger.info(
+                "Attempt %d/%d failed: %s (%s, retryable: %s)",
+                attempt + 1, max_retries + 1,
+                classification.reason,
+                classification.category.value,
+                classification.should_retry,
+            )
+            
+            # If permanent error, fail immediately
+            if not classification.should_retry:
+                logger.error("Permanent error: %s", classification.reason)
+                raise
+            
+            # If this was the last attempt, raise
+            if attempt >= max_retries:
+                logger.error("Max retries (%d) exceeded", max_retries)
+                raise
+            
+            # Calculate backoff with exponential increase
+            backoff = backoff_base * (2 ** attempt)
+            logger.info("Retrying in %.1fs...", backoff)
+            time.sleep(backoff)
+    
+    # Should not reach here, but just in case
+    raise last_error
+
+
+def format_error_report(classification: ErrorClassification) -> str:
+    """Format error classification as a report string."""
+    icon = "🔄" if classification.should_retry else "❌"
+    return f"{icon} {classification.category.value}: {classification.reason}"

tools/session_search_tool.py

@@ -394,6 +394,23 @@ def session_search(
             if len(seen_sessions) >= limit:
                 break
 
+        # RIDER: Reader-guided reranking — sort sessions by LLM answerability
+        # This bridges the R@5 vs E2E accuracy gap by prioritizing passages
+        # the LLM can actually answer from, not just keyword matches.
+        try:
+            from agent.rider import rerank_passages, is_rider_available
+            if is_rider_available() and len(seen_sessions) > 1:
+                rider_passages = [
+                    {"session_id": sid, "content": info.get("snippet", ""), "rank": i + 1}
+                    for i, (sid, info) in enumerate(seen_sessions.items())
+                ]
+                reranked = rerank_passages(rider_passages, query, top_n=len(rider_passages))
+                # Reorder seen_sessions by RIDER score
+                reranked_sids = [p["session_id"] for p in reranked]
+                seen_sessions = {sid: seen_sessions[sid] for sid in reranked_sids if sid in seen_sessions}
+        except Exception as e:
+            logging.debug("RIDER reranking skipped: %s", e)
+
         # Prepare all sessions for parallel summarization
         tasks = []
         for session_id, match_info in seen_sessions.items():