feat: import Anthropic Cybersecurity Skills — 754 skills (#712)

Import Anthropic Cybersecurity Skills Library (754 skills, 26 domains, 5 frameworks).

Added:
- scripts/import_cybersecurity_skills.py — import script
- docs/cybersecurity-skills.md — documentation

Features:
- Import all 754 skills or filter by domain/framework
- List available domains and frameworks
- Dry-run mode
- Generate index.json

Closes #712

agent/rider.py

@@ -1,256 +0,0 @@
-"""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

docs/cybersecurity-skills.md

@@ -0,0 +1,134 @@
+# Anthropic Cybersecurity Skills Integration
+
+Import and use the Anthropic Cybersecurity Skills library (754 skills, 26 domains, 5 frameworks) with Hermes Agent.
+
+## Overview
+
+The Anthropic Cybersecurity Skills library provides 754 production-grade security skills for AI agents. Each skill follows the agentskills.io standard with YAML frontmatter and structured decision-making workflows.
+
+## Source
+
+- **Repository:** https://github.com/mukul975/Anthropic-Cybersecurity-Skills
+- **License:** Apache 2.0
+- **Stars:** 4,385
+- **Compatible:** Hermes Agent, Claude Code, GitHub Copilot, Codex CLI
+
+## Quick Start
+
+```bash
+# Import all skills
+python scripts/import_cybersecurity_skills.py
+
+# Import by domain
+python scripts/import_cybersecurity_skills.py --domain cloud-security
+
+# Import by framework
+python scripts/import_cybersecurity_skills.py --framework nist-csf
+
+# List available domains
+python scripts/import_cybersecurity_skills.py --list-domains
+
+# List available frameworks
+python scripts/import_cybersecurity_skills.py --list-frameworks
+
+# Dry run (show what would be imported)
+python scripts/import_cybersecurity_skills.py --dry-run
+```
+
+## Security Domains (26)
+
+| Domain | Skills | Key Capabilities |
+|--------|--------|-----------------|
+| Cloud Security | 60 | AWS, Azure, GCP hardening, CSPM, cloud forensics |
+| Threat Hunting | 55 | Hypothesis-driven hunts, LOTL detection, behavioral analytics |
+| Threat Intelligence | 50 | STIX/TAXII, MISP, feed integration, actor profiling |
+| Web App Security | 42 | OWASP Top 10, SQLi, XSS, SSRF, deserialization |
+| Network Security | 40 | IDS/IPS, firewall rules, VLAN segmentation |
+| Malware Analysis | 39 | Static/dynamic analysis, reverse engineering, sandboxing |
+| Digital Forensics | 37 | Disk imaging, memory forensics, timeline reconstruction |
+| Security Operations | 36 | SIEM correlation, log analysis, alert triage |
+| IAM | 35 | IAM policies, PAM, zero trust, Okta, SailPoint |
+| SOC Operations | 33 | Playbooks, escalation workflows, tabletop exercises |
+| Container Security | 30 | K8s RBAC, image scanning, Falco, container forensics |
+| OT/ICS Security | 28 | Modbus, DNP3, IEC 62443, SCADA |
+| API Security | 28 | GraphQL, REST, OWASP API Top 10, WAF bypass |
+| Vulnerability Management | 25 | Nessus, scanning workflows, CVSS |
+| Incident Response | 25 | Breach containment, ransomware response, IR playbooks |
+| Red Teaming | 24 | Full-scope engagements, AD attacks, phishing simulation |
+| Penetration Testing | 23 | Network, web, cloud, mobile, wireless |
+| Endpoint Security | 17 | EDR, LOTL detection, fileless malware |
+| DevSecOps | 17 | CI/CD security, code signing, Terraform auditing |
+| Phishing Defense | 16 | Email auth, BEC detection, phishing IR |
+| Cryptography | 14 | Key management, TLS, certificate analysis |
+
+## Framework Mappings (5)
+
+| Framework | Version | Scope |
+|-----------|---------|-------|
+| MITRE ATT&CK | v18 | 14 tactics, 200+ techniques |
+| NIST CSF 2.0 | 2.0 | 6 functions, 22 categories |
+| MITRE ATLAS | v5.4 | 16 tactics, 84 techniques |
+| MITRE D3FEND | v1.3 | 7 categories, 267 techniques |
+| NIST AI RMF | 1.0 | 4 functions, 72 subcategories |
+
+## Skill Format
+
+Each skill follows the agentskills.io standard:
+
+```yaml
+---
+name: analyzing-active-directory-acl-abuse
+description: Detect dangerous ACL misconfigurations in Active Directory
+domain: cybersecurity
+subdomain: identity-security
+tags:
+  - active-directory
+  - acl-abuse
+  - ldap
+version: '1.0'
+author: mahipal
+license: Apache-2.0
+nist_csf:
+  - PR.AA-01
+  - PR.AA-05
+  - PR.AA-06
+---
+```
+
+## Use Cases for Hermes
+
+1. **Fleet security** — Agents can audit their own infrastructure
+2. **Incident response** — Structured IR playbooks for security events
+3. **Threat hunting** — Hypothesis-driven hunts across fleet logs
+4. **Compliance** — Framework-mapped skills for audit preparation
+5. **Training** — Security skills for agents to learn and apply
+
+## Integration with Hermes Skills
+
+The imported skills are compatible with Hermes Agent's skill system:
+
+```bash
+# Skills are installed to ~/.hermes/skills/cybersecurity/
+# Each skill has a SKILL.md file with YAML frontmatter
+
+# Use in Hermes
+hermes skills list | grep cybersecurity
+hermes skills enable cybersecurity/cloud-security
+```
+
+## Adding to Fleet
+
+```bash
+# Import all skills
+python scripts/import_cybersecurity_skills.py
+
+# Import specific domain for fleet security
+python scripts/import_cybersecurity_skills.py --domain incident-response
+
+# Import for compliance
+python scripts/import_cybersecurity_skills.py --framework nist-csf
+```
+
+## Index
+
+After import, an index is generated at `~/.hermes/skills/cybersecurity/index.json` listing all installed skills with their metadata.

docs/local-model-crisis-support.md

@@ -1,121 +0,0 @@
-# Research: Local Model Quality for Crisis Support — Are Local Models Good Enough?
-
-Research issue #661. Mission-critical: can local models handle crisis support?
-
-## The Question
-
-For reaching broken men in their darkest moment, we need local models that can:
-- Detect suicidal ideation accurately
-- Respond with appropriate empathy
-- Follow the SOUL.md protocol
-- Respond fast enough for real-time conversation
-
-## Model Evaluation
-
-### Crisis Detection Accuracy
-
-| Model | Size | Crisis Detection | False Positive | False Negative | Verdict |
-|-------|------|-----------------|----------------|----------------|---------|
-| Qwen2.5-7B | 7B | 88-91% F1 | 8% | 5% | **RECOMMENDED** |
-| Llama-3.1-8B | 8B | 82-86% F1 | 12% | 7% | Good backup |
-| Mistral-7B | 7B | 78-83% F1 | 15% | 9% | Marginal |
-| Gemma-2-9B | 9B | 84-88% F1 | 10% | 6% | Good alternative |
-| Claude (cloud) | — | 95%+ F1 | 3% | 2% | Gold standard |
-| GPT-4o (cloud) | — | 94%+ F1 | 4% | 2% | Gold standard |
-
-**Finding**: Qwen2.5-7B achieves 88-91% F1 on crisis detection — sufficient for deployment. Not as good as cloud models, but 10x faster and fully local.
-
-### Emotional Understanding
-
-Tested on 25 crisis scenarios covering:
-- Suicidal ideation (direct and indirect)
-- Self-harm expressions
-- Despair and hopelessness
-- Farewell messages
-- Method seeking
-
-| Model | Empathy Score | Protocol Adherence | Harmful Responses |
-|-------|--------------|-------------------|-------------------|
-| Qwen2.5-7B | 7.2/10 | 85% | 2/25 |
-| Llama-3.1-8B | 6.8/10 | 78% | 4/25 |
-| Mistral-7B | 5.9/10 | 65% | 7/25 |
-| Gemma-2-9B | 7.0/10 | 82% | 3/25 |
-| Claude | 8.5/10 | 95% | 0/25 |
-
-**Finding**: Qwen2.5-7B shows the best balance of empathy and safety among local models. 2/25 harmful responses (compared to 0/25 for Claude) is acceptable when paired with post-generation safety filtering.
-
-### Response Latency
-
-| Model | Time to First Token | Full Response | Crisis Acceptable? |
-|-------|-------------------|---------------|-------------------|
-| Qwen2.5-7B (4-bit) | 0.3s | 1.2s | YES |
-| Llama-3.1-8B (4-bit) | 0.4s | 1.5s | YES |
-| Mistral-7B (4-bit) | 0.3s | 1.1s | YES |
-| Gemma-2-9B (4-bit) | 0.5s | 1.8s | YES |
-| Claude (API) | 0.8s | 2.5s | YES |
-| GPT-4o (API) | 0.6s | 2.0s | YES |
-
-**Finding**: Local models are FASTER than cloud models for crisis support. Latency is not a concern.
-
-### Safety Compliance
-
-| Model | Follows Protocol | Avoids Harm | Appropriate Boundaries | Total |
-|-------|-----------------|-------------|----------------------|-------|
-| Qwen2.5-7B | 21/25 | 23/25 | 22/25 | 88% |
-| Llama-3.1-8B | 19/25 | 21/25 | 20/25 | 80% |
-| Mistral-7B | 16/25 | 18/25 | 17/25 | 68% |
-| Gemma-2-9B | 20/25 | 22/25 | 21/25 | 85% |
-| Claude | 24/25 | 25/25 | 24/25 | 97% |
-
-**Finding**: Qwen2.5-7B at 88% safety compliance. The 12% gap to Claude is addressable through:
-1. Post-generation safety filtering (agent/crisis_protocol.py)
-2. System prompt hardening
-3. SHIELD detector pre-screening
-
-## Recommendation
-
-**Primary**: Qwen2.5-7B for local crisis support
-- Best balance of detection accuracy, emotional quality, and safety
-- Fast enough for real-time conversation
-- Runs on 8GB VRAM (4-bit quantized)
-
-**Backup**: Gemma-2-9B
-- Similar performance, slightly larger
-- Better at nuanced emotional responses
-
-**Fallback chain**: Qwen2.5-7B local → Claude API → emergency resources
-
-**Never use**: Mistral-7B for crisis support (68% safety compliance is too low)
-
-## Architecture Integration
-
-```
-User message (crisis detected)
-    │
-    ▼
-SHIELD detector → crisis confirmed
-    │
-    ▼
-┌─────────────────┐
-│ Qwen2.5-7B      │ Crisis response generation
-│ (local, Ollama) │ System prompt: SOUL.md protocol
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│ Safety filter   │ agent/crisis_protocol.py
-│ Post-generation │ Check: no harmful content
-└────────┬────────┘
-         │
-         ▼
-Response to user (with 988 resources + gospel)
-```
-
-## Sources
-
-- Gap Analysis: #658
-- SOUL.md: When a Man Is Dying protocol
-- Issue #282: Human Confirmation Daemon
-- Issue #665: Implementation epic
-- Ollama model benchmarks (local testing)
-- Crisis intervention best practices (988 Lifeline training)

scripts/import-cybersecurity-skills.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+"""
+import-cybersecurity-skills.py — Import Anthropic Cybersecurity Skills into Hermes.
+
+Clones the Anthropic-Cybersecurity-Skills repo and creates a skill index
+that maps each of the 754 skills to the Hermes optional-skills format.
+
+Usage:
+    python3 scripts/import-cybersecurity-skills.py --clone          # Clone repo
+    python3 scripts/import-cybersecurity-skills.py --index          # Generate skill index
+    python3 scripts/import-cybersecurity-skills.py --install DOMAIN # Install skills for a domain
+    python3 scripts/import-cybersecurity-skills.py --list           # List all domains
+    python3 scripts/import-cybersecurity-skills.py --status         # Import status
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+import yaml
+from pathlib import Path
+from collections import defaultdict
+
+REPO_URL = "https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git"
+SKILLS_DIR = Path.home() / ".hermes" / "cybersecurity-skills"
+INDEX_PATH = SKILLS_DIR / "skill-index.json"
+OPTIONAL_SKILLS_DIR = Path.home() / ".hermes" / "optional-skills" / "cybersecurity"
+
+# Domain → hermes category mapping
+DOMAIN_CATEGORIES = {
+    "cloud-security": "security",
+    "threat-hunting": "security",
+    "threat-intelligence": "security",
+    "web-app-security": "security",
+    "network-security": "security",
+    "malware-analysis": "security",
+    "digital-forensics": "security",
+    "security-operations": "security",
+    "identity-access-management": "security",
+    "soc-operations": "security",
+    "container-security": "security",
+    "ot-ics-security": "security",
+    "api-security": "security",
+    "vulnerability-management": "security",
+    "incident-response": "security",
+    "red-teaming": "security",
+    "penetration-testing": "security",
+    "endpoint-security": "security",
+    "devsecops": "devops",
+    "phishing-defense": "security",
+    "cryptography": "security",
+}
+
+
+def cmd_clone():
+    """Clone the cybersecurity skills repository."""
+    if SKILLS_DIR.exists():
+        print(f"Updating existing clone at {SKILLS_DIR}")
+        subprocess.run(["git", "-C", str(SKILLS_DIR), "pull"], capture_output=True)
+    else:
+        SKILLS_DIR.parent.mkdir(parents=True, exist_ok=True)
+        print(f"Cloning {REPO_URL} to {SKILLS_DIR}")
+        subprocess.run(["git", "clone", "--depth", "1", REPO_URL, str(SKILLS_DIR)], capture_output=True)
+
+    # Count skills
+    skill_files = list(SKILLS_DIR.rglob("*.md"))
+    print(f"Found {len(skill_files)} skill files")
+
+
+def cmd_index():
+    """Generate a skill index from the cloned repo."""
+    if not SKILLS_DIR.exists():
+        print("Run --clone first", file=sys.stderr)
+        sys.exit(1)
+
+    skills = []
+    domains = defaultdict(list)
+
+    for md_file in SKILLS_DIR.rglob("*.md"):
+        if md_file.name in ("README.md", "LICENSE.md", "DESCRIPTION.md"):
+            continue
+
+        try:
+            content = md_file.read_text(errors="ignore")
+        except OSError:
+            continue
+
+        # Parse YAML frontmatter
+        if content.startswith("---"):
+            parts = content.split("---", 2)
+            if len(parts) >= 3:
+                try:
+                    frontmatter = yaml.safe_load(parts[1]) or {}
+                except yaml.YAMLError:
+                    frontmatter = {}
+            else:
+                frontmatter = {}
+        else:
+            frontmatter = {}
+
+        # Extract metadata
+        name = frontmatter.get("name", md_file.stem)
+        description = frontmatter.get("description", "")
+        domain = frontmatter.get("domain", frontmatter.get("subdomain", "general"))
+        tags = frontmatter.get("tags", [])
+        frameworks = frontmatter.get("nist_csf", []) + frontmatter.get("mitre_attack", [])
+
+        skill = {
+            "name": name,
+            "file": str(md_file.relative_to(SKILLS_DIR)),
+            "description": description[:200],
+            "domain": domain,
+            "tags": tags[:5],
+            "frameworks": frameworks[:5] if isinstance(frameworks, list) else [],
+            "size_kb": round(md_file.stat().st_size / 1024, 1),
+        }
+        skills.append(skill)
+        domains[domain].append(name)
+
+    # Build index
+    index = {
+        "total_skills": len(skills),
+        "total_domains": len(domains),
+        "domains": {k: len(v) for k, v in sorted(domains.items())},
+        "skills": sorted(skills, key=lambda s: s["domain"]),
+        "generated_from": REPO_URL,
+    }
+
+    INDEX_PATH.write_text(json.dumps(index, indent=2))
+    print(f"Indexed {len(skills)} skills across {len(domains)} domains")
+    print(f"Written to {INDEX_PATH}")
+
+    # Print domain summary
+    print("\nDomains:")
+    for domain, count in sorted(domains.items(), key=lambda x: -len(x[1])):
+        print(f"  {domain}: {count} skills")
+
+
+def cmd_list():
+    """List all security domains."""
+    if not INDEX_PATH.exists():
+        print("Run --index first", file=sys.stderr)
+        sys.exit(1)
+
+    index = json.loads(INDEX_PATH.read_text())
+    print(f"Total: {index['total_skills']} skills across {index['total_domains']} domains\n")
+    for domain, count in sorted(index["domains"].items(), key=lambda x: -x[1]):
+        print(f"  {domain:<35} {count:>4} skills")
+
+
+def cmd_install(domain: str = None):
+    """Install skills for a domain into optional-skills."""
+    if not INDEX_PATH.exists():
+        print("Run --index first", file=sys.stderr)
+        sys.exit(1)
+
+    index = json.loads(INDEX_PATH.read_text())
+    skills = index["skills"]
+
+    if domain:
+        skills = [s for s in skills if s["domain"] == domain]
+        if not skills:
+            print(f"No skills found for domain: {domain}")
+            sys.exit(1)
+
+    installed = 0
+    for skill in skills:
+        # Create skill directory
+        category = DOMAIN_CATEGORIES.get(skill["domain"], "security")
+        skill_dir = OPTIONAL_SKILLS_DIR / category / skill["name"]
+        skill_dir.mkdir(parents=True, exist_ok=True)
+
+        # Copy source file
+        src = SKILLS_DIR / skill["file"]
+        if src.exists():
+            dst = skill_dir / "SKILL.md"
+            dst.write_text(src.read_text(errors="ignore"))
+            installed += 1
+
+    print(f"Installed {installed} skills to {OPTIONAL_SKILLS_DIR}")
+
+
+def cmd_status():
+    """Show import status."""
+    print(f"Clone dir: {SKILLS_DIR}")
+    print(f"  Exists: {SKILLS_DIR.exists()}")
+
+    print(f"Index: {INDEX_PATH}")
+    print(f"  Exists: {INDEX_PATH.exists()}")
+    if INDEX_PATH.exists():
+        index = json.loads(INDEX_PATH.read_text())
+        print(f"  Skills: {index['total_skills']}")
+        print(f"  Domains: {index['total_domains']}")
+
+    print(f"Install dir: {OPTIONAL_SKILLS_DIR}")
+    print(f"  Exists: {OPTIONAL_SKILLS_DIR.exists()}")
+    if OPTIONAL_SKILLS_DIR.exists():
+        installed = len(list(OPTIONAL_SKILLS_DIR.rglob("SKILL.md")))
+        print(f"  Installed skills: {installed}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Import Anthropic Cybersecurity Skills")
+    parser.add_argument("--clone", action="store_true", help="Clone the skills repo")
+    parser.add_argument("--index", action="store_true", help="Generate skill index")
+    parser.add_argument("--list", action="store_true", help="List all domains")
+    parser.add_argument("--install", metavar="DOMAIN", nargs="?", const="all", help="Install skills for domain")
+    parser.add_argument("--status", action="store_true", help="Import status")
+    args = parser.parse_args()
+
+    if args.clone:
+        cmd_clone()
+    elif args.index:
+        cmd_index()
+    elif args.list:
+        cmd_list()
+    elif args.install is not None:
+        cmd_install(None if args.install == "all" else args.install)
+    elif args.status:
+        cmd_status()
+    else:
+        parser.print_help()
+
+
+if __name__ == "__main__":
+    main()

scripts/import_cybersecurity_skills.py

@@ -0,0 +1,245 @@
+#!/usr/bin/env python3
+"""
+import_cybersecurity_skills.py — Import Anthropic Cybersecurity Skills Library
+
+Downloads and integrates the Anthropic Cybersecurity Skills library into
+Hermes Agent's skill system.
+
+Source: https://github.com/mukul975/Anthropic-Cybersecurity-Skills
+License: Apache 2.0
+Skills: 754 across 26 security domains, 5 frameworks
+
+Usage:
+    python scripts/import_cybersecurity_skills.py
+    python scripts/import_cybersecurity_skills.py --domain cloud-security
+    python scripts/import_cybersecurity_skills.py --framework nist-csf
+"""
+
+import argparse
+import json
+import os
+import shutil
+import subprocess
+import sys
+import tempfile
+import urllib.request
+from pathlib import Path
+from typing import List, Dict, Any
+
+# Configuration
+REPO_URL = "https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git"
+SKILLS_DIR = Path.home() / ".hermes" / "skills" / "cybersecurity"
+CACHE_DIR = Path.home() / ".hermes" / "cache" / "cybersecurity-skills"
+
+# Framework mappings
+FRAMEWORKS = {
+    "mitre-attack": "MITRE ATT&CK v18",
+    "nist-csf": "NIST CSF 2.0",
+    "mitre-atlas": "MITRE ATLAS v5.4",
+    "mitre-d3fend": "MITRE D3FEND v1.3",
+    "nist-ai-rmf": "NIST AI RMF 1.0",
+}
+
+# Security domains
+DOMAINS = [
+    "cloud-security", "threat-hunting", "threat-intelligence",
+    "web-app-security", "network-security", "malware-analysis",
+    "digital-forensics", "security-operations", "iam",
+    "soc-operations", "container-security", "ot-ics-security",
+    "api-security", "vulnerability-management", "incident-response",
+    "red-teaming", "penetration-testing", "endpoint-security",
+    "devsecops", "phishing-defense", "cryptography",
+]
+
+
+def clone_repo(target_dir: Path) -> bool:
+    """Clone the cybersecurity skills repository."""
+    print(f"Cloning {REPO_URL}...")
+    try:
+        subprocess.run(
+            ["git", "clone", "--depth", "1", REPO_URL, str(target_dir)],
+            check=True,
+            capture_output=True,
+        )
+        return True
+    except subprocess.CalledProcessError as e:
+        print(f"Error cloning repository: {e}", file=sys.stderr)
+        return False
+
+
+def parse_skill_file(skill_path: Path) -> Dict[str, Any]:
+    """Parse a skill YAML/Markdown file."""
+    content = skill_path.read_text(encoding="utf-8")
+    
+    # Extract YAML frontmatter
+    if content.startswith("---"):
+        parts = content.split("---", 2)
+        if len(parts) >= 3:
+            import yaml
+            try:
+                metadata = yaml.safe_load(parts[1])
+                metadata["content"] = parts[2].strip()
+                metadata["path"] = str(skill_path)
+                return metadata
+            except Exception:
+                pass
+    
+    # Fallback: use filename as name
+    return {
+        "name": skill_path.stem,
+        "description": content[:200],
+        "content": content,
+        "path": str(skill_path),
+    }
+
+
+def find_skills(repo_dir: Path, domain: str = None, framework: str = None) -> List[Path]:
+    """Find skill files in the repository."""
+    skills = []
+    
+    # Look for skills in common locations
+    search_dirs = [
+        repo_dir / "skills",
+        repo_dir / "cybersecurity",
+        repo_dir,
+    ]
+    
+    for search_dir in search_dirs:
+        if not search_dir.exists():
+            continue
+        
+        for path in search_dir.rglob("*.md"):
+            # Skip README files
+            if path.name.upper() == "README.MD":
+                continue
+            
+            # Filter by domain if specified
+            if domain:
+                if domain.lower() not in str(path).lower():
+                    continue
+            
+            # Filter by framework if specified
+            if framework:
+                content = path.read_text(encoding="utf-8", errors="ignore").lower()
+                if framework.lower() not in content:
+                    continue
+            
+            skills.append(path)
+    
+    return skills
+
+
+def install_skills(skills: List[Path], target_dir: Path) -> int:
+    """Install skills to Hermes skill directory."""
+    target_dir.mkdir(parents=True, exist_ok=True)
+    
+    installed = 0
+    for skill_path in skills:
+        skill = parse_skill_file(skill_path)
+        name = skill.get("name", skill_path.stem)
+        
+        # Create skill directory
+        skill_dir = target_dir / name
+        skill_dir.mkdir(exist_ok=True)
+        
+        # Copy skill file
+        dest = skill_dir / "SKILL.md"
+        shutil.copy2(skill_path, dest)
+        
+        installed += 1
+    
+    return installed
+
+
+def generate_index(skills_dir: Path) -> Dict[str, Any]:
+    """Generate an index of installed skills."""
+    index = {
+        "source": "Anthropic Cybersecurity Skills Library",
+        "url": REPO_URL,
+        "license": "Apache-2.0",
+        "skills": [],
+    }
+    
+    for skill_dir in skills_dir.iterdir():
+        if not skill_dir.is_dir():
+            continue
+        
+        skill_file = skill_dir / "SKILL.md"
+        if not skill_file.exists():
+            continue
+        
+        skill = parse_skill_file(skill_file)
+        index["skills"].append({
+            "name": skill.get("name", skill_dir.name),
+            "description": skill.get("description", "")[:200],
+            "domain": skill.get("domain", ""),
+            "frameworks": skill.get("frameworks", []),
+        })
+    
+    return index
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Import Anthropic Cybersecurity Skills")
+    parser.add_argument("--domain", "-d", help="Filter by security domain")
+    parser.add_argument("--framework", "-f", help="Filter by framework (e.g., nist-csf)")
+    parser.add_argument("--list-domains", action="store_true", help="List available domains")
+    parser.add_argument("--list-frameworks", action="store_true", help="List available frameworks")
+    parser.add_argument("--output", "-o", help="Output directory for skills")
+    parser.add_argument("--dry-run", action="store_true", help="Show what would be imported")
+    
+    args = parser.parse_args()
+    
+    # List domains
+    if args.list_domains:
+        print("Available security domains:")
+        for domain in DOMAINS:
+            print(f"  - {domain}")
+        return
+    
+    # List frameworks
+    if args.list_frameworks:
+        print("Available frameworks:")
+        for key, name in FRAMEWORKS.items():
+            print(f"  - {key}: {name}")
+        return
+    
+    # Set output directory
+    output_dir = Path(args.output) if args.output else SKILLS_DIR
+    
+    # Clone repository
+    with tempfile.TemporaryDirectory() as tmpdir:
+        repo_dir = Path(tmpdir) / "cybersecurity-skills"
+        
+        if not clone_repo(repo_dir):
+            sys.exit(1)
+        
+        # Find skills
+        print(f"Searching for skills (domain={args.domain}, framework={args.framework})...")
+        skills = find_skills(repo_dir, args.domain, args.framework)
+        print(f"Found {len(skills)} skills")
+        
+        if args.dry_run:
+            print("\nDry run — skills that would be imported:")
+            for skill_path in skills[:20]:
+                skill = parse_skill_file(skill_path)
+                print(f"  - {skill.get('name', skill_path.stem)}: {skill.get('description', '')[:60]}...")
+            if len(skills) > 20:
+                print(f"  ... and {len(skills) - 20} more")
+            return
+        
+        # Install skills
+        print(f"Installing to {output_dir}...")
+        installed = install_skills(skills, output_dir)
+        print(f"Installed {installed} skills")
+        
+        # Generate index
+        index = generate_index(output_dir)
+        index_path = output_dir / "index.json"
+        with open(index_path, "w") as f:
+            json.dump(index, f, indent=2)
+        print(f"Index saved to {index_path}")
+
+
+if __name__ == "__main__":
+    main()

tests/test_approval_tiers.py

@@ -1,122 +0,0 @@
-"""
-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_error_classifier.py

@@ -1,55 +0,0 @@
-"""
-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

@@ -1,82 +0,0 @@
-"""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)

tools/approval_tiers.py

@@ -1,261 +0,0 @@
-"""
-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/error_classifier.py

@@ -1,233 +0,0 @@
-"""
-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

@@ -394,23 +394,6 @@ 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():