feat: adapt token rewards based on system stress signals (#714)

Implements adaptive token rewards that respond to system stress:

- StressDetector module (timmy/stress_detector.py):
  - Monitors 4 stress signals: flaky test rate, P1 backlog growth,
    CI failure rate, open bug count
  - Calculates weighted stress score (0-1) and determines mode:
    calm (<0.3), elevated (0.3-0.6), high (>0.6)
  - Applies quest-specific multipliers based on current mode

- Configuration (config/stress_modes.yaml):
  - Thresholds for mode transitions
  - Signal weights and thresholds
  - Multipliers per mode (e.g., test_improve: 1.5x in high stress)

- Quest system integration:
  - Rewards now include stress bonus/penalty in notification
  - Quest status API includes adjusted_reward and multiplier
  - Agent can see current stress mode and why rewards changed

- API endpoints:
  - GET /quests/api/stress - current stress mode and signals
  - POST /quests/api/stress/refresh - force refresh stress detection

Fixes #714

config/stress_modes.yaml

@@ -0,0 +1,98 @@
+# ── System Stress Modes Configuration ────────────────────────────────────────
+#
+# This configuration defines how token rewards adapt based on system stress.
+# When the system detects elevated stress (flaky tests, growing backlog,
+# CI failures), quest rewards are adjusted to incentivize agents to focus
+# on the most critical areas.
+#
+# ── How It Works ─────────────────────────────────────────────────────────────
+#
+# 1. SIGNALS: System metrics are monitored continuously
+# 2. SCORE: Weighted contributions from triggered signals create a stress score
+# 3. MODE: Score determines the stress mode (calm, elevated, high)
+# 4. MULTIPLIERS: Token rewards are multiplied based on the current mode
+#
+# ── Stress Thresholds ────────────────────────────────────────────────────────
+
+thresholds:
+  # Minimum score to enter elevated mode (0.0 - 1.0)
+  elevated_min: 0.3
+  
+  # Minimum score to enter high stress mode (0.0 - 1.0)
+  high_min: 0.6
+
+# ── Stress Signals ───────────────────────────────────────────────────────────
+#
+# Each signal has:
+#   - threshold: Value at which signal is considered "triggered"
+#   - weight: Contribution to overall stress score (should sum to ~1.0)
+
+signals:
+  flaky_test_rate:
+    threshold: 0.15  # 15% of tests showing flakiness
+    weight: 0.30
+    description: "Percentage of test runs that are flaky"
+    
+  p1_backlog_growth:
+    threshold: 5  # 5 new P1 issues in lookback period
+    weight: 0.25
+    description: "Net growth in P1 priority issues over 7 days"
+    
+  ci_failure_rate:
+    threshold: 0.20  # 20% of CI runs failing
+    weight: 0.25
+    description: "Percentage of CI runs failing in lookback period"
+    
+  open_bug_count:
+    threshold: 20  # 20 open bugs
+    weight: 0.20
+    description: "Total open issues labeled as 'bug'"
+
+# ── Token Multipliers ────────────────────────────────────────────────────────
+#
+# Multipliers are applied to quest rewards based on current stress mode.
+# Values > 1.0 increase rewards, < 1.0 decrease rewards.
+#
+# Quest types:
+#   - test_improve: Test coverage/quality improvements
+#   - docs_update: Documentation updates
+#   - issue_count: Closing specific issue types
+#   - issue_reduce: Reducing overall issue backlog
+#   - daily_run: Daily Run session completion
+#   - custom: Special/manual quests
+#   - exploration: Exploratory work
+#   - refactor: Code refactoring
+
+multipliers:
+  calm:
+    # Calm periods: incentivize maintenance and exploration
+    test_improve: 1.0
+    docs_update: 1.2
+    issue_count: 1.0
+    issue_reduce: 1.0
+    daily_run: 1.0
+    custom: 1.0
+    exploration: 1.3
+    refactor: 1.2
+    
+  elevated:
+    # Elevated stress: start emphasizing stability
+    test_improve: 1.2
+    docs_update: 1.0
+    issue_count: 1.1
+    issue_reduce: 1.1
+    daily_run: 1.0
+    custom: 1.0
+    exploration: 1.0
+    refactor: 0.9  # Discourage risky changes
+    
+  high:
+    # High stress: crisis mode, focus on stabilization
+    test_improve: 1.5  # Strongly incentivize testing
+    docs_update: 0.8  # Deprioritize docs
+    issue_count: 1.3  # Reward closing issues
+    issue_reduce: 1.4  # Strongly reward reducing backlog
+    daily_run: 1.1
+    custom: 1.0
+    exploration: 0.7  # Discourage exploration
+    refactor: 0.6  # Discourage refactors during crisis

src/dashboard/routes/quests.py

@@ -187,6 +187,76 @@ async def reload_quest_config_api() -> JSONResponse:
     )
 
 
+# ---------------------------------------------------------------------------
+# Stress Mode Endpoints
+# ---------------------------------------------------------------------------
+
+
+@router.get("/api/stress")
+async def get_stress_status_api() -> JSONResponse:
+    """Get current stress mode status and multipliers.
+
+    Returns:
+        Current stress mode, score, active signals, and multipliers
+    """
+    try:
+        from timmy.stress_detector import (
+            detect_stress_mode,
+            get_stress_summary,
+        )
+
+        snapshot = detect_stress_mode()
+        summary = get_stress_summary()
+
+        return JSONResponse(
+            {
+                "status": "ok",
+                "stress": summary,
+                "raw": snapshot.to_dict(),
+            }
+        )
+    except Exception as exc:
+        logger.warning("Failed to get stress status: %s", exc)
+        return JSONResponse(
+            {
+                "status": "error",
+                "error": str(exc),
+            },
+            status_code=500,
+        )
+
+
+@router.post("/api/stress/refresh")
+async def refresh_stress_detection_api() -> JSONResponse:
+    """Force a fresh stress detection check.
+
+    Normally stress is cached for 60 seconds. This endpoint
+    bypasses the cache for immediate results.
+    """
+    try:
+        from timmy.stress_detector import detect_stress_mode, get_stress_summary
+
+        snapshot = detect_stress_mode(force_refresh=True)
+        summary = get_stress_summary()
+
+        return JSONResponse(
+            {
+                "status": "ok",
+                "stress": summary,
+                "raw": snapshot.to_dict(),
+            }
+        )
+    except Exception as exc:
+        logger.warning("Failed to refresh stress detection: %s", exc)
+        return JSONResponse(
+            {
+                "status": "error",
+                "error": str(exc),
+            },
+            status_code=500,
+        )
+
+
 # ---------------------------------------------------------------------------
 # Dashboard UI Endpoints
 # ---------------------------------------------------------------------------

src/timmy/quest_system.py

@@ -269,6 +269,22 @@ def _is_on_cooldown(progress: QuestProgress, quest: QuestDefinition) -> bool:
         return False
 
 
+def _apply_stress_multiplier(base_reward: int, quest_type: QuestType) -> tuple[int, float]:
+    """Apply stress-based multiplier to quest reward.
+
+    Returns:
+        Tuple of (adjusted_reward, multiplier_used)
+    """
+    try:
+        from timmy.stress_detector import apply_multiplier
+
+        multiplier = apply_multiplier(base_reward, quest_type.value)
+        return multiplier, multiplier / max(base_reward, 1)
+    except Exception as exc:
+        logger.debug("Failed to apply stress multiplier: %s", exc)
+        return base_reward, 1.0
+
+
 def claim_quest_reward(quest_id: str, agent_id: str) -> dict[str, Any] | None:
     """Claim the token reward for a completed quest.
 
@@ -292,13 +308,18 @@ def claim_quest_reward(quest_id: str, agent_id: str) -> dict[str, Any] | None:
         return None
 
     try:
+        # Apply stress-based multiplier
+        adjusted_reward, multiplier = _apply_stress_multiplier(
+            quest.reward_tokens, quest.quest_type
+        )
+
         # Award tokens via ledger
         from lightning.ledger import create_invoice_entry, mark_settled
 
         # Create a mock invoice for the reward
         invoice_entry = create_invoice_entry(
             payment_hash=f"quest_{quest_id}_{agent_id}_{int(time.time())}",
-            amount_sats=quest.reward_tokens,
+            amount_sats=adjusted_reward,
             memo=f"Quest reward: {quest.name}",
             source="quest_reward",
             agent_id=agent_id,
@@ -320,12 +341,21 @@ def claim_quest_reward(quest_id: str, agent_id: str) -> dict[str, Any] | None:
             progress.completed_at = ""
             progress.claimed_at = ""
 
-        notification = quest.notification_message.format(tokens=quest.reward_tokens)
+        # Build notification with multiplier info
+        notification = quest.notification_message.format(tokens=adjusted_reward)
+        if multiplier != 1.0:
+            pct = int((multiplier - 1.0) * 100)
+            if pct > 0:
+                notification += f" (+{pct}% stress bonus)"
+            else:
+                notification += f" ({pct}% stress adjustment)"
 
         return {
             "quest_id": quest_id,
             "agent_id": agent_id,
-            "tokens_awarded": quest.reward_tokens,
+            "tokens_awarded": adjusted_reward,
+            "base_reward": quest.reward_tokens,
+            "multiplier": round(multiplier, 2),
             "notification": notification,
             "completion_count": progress.completion_count,
         }
@@ -467,6 +497,14 @@ def get_agent_quests_status(agent_id: str) -> dict[str, Any]:
     total_rewards = 0
     completed_count = 0
 
+    # Get current stress mode for adjusted rewards display
+    try:
+        from timmy.stress_detector import get_current_stress_mode, get_multiplier
+
+        current_mode = get_current_stress_mode()
+    except Exception:
+        current_mode = None
+
     for quest_id, quest in definitions.items():
         progress = get_quest_progress(quest_id, agent_id)
         if not progress:
@@ -474,11 +512,23 @@ def get_agent_quests_status(agent_id: str) -> dict[str, Any]:
 
         is_on_cooldown = _is_on_cooldown(progress, quest) if quest.repeatable else False
 
+        # Calculate adjusted reward with stress multiplier
+        adjusted_reward = quest.reward_tokens
+        multiplier = 1.0
+        if current_mode:
+            try:
+                multiplier = get_multiplier(quest.quest_type.value, current_mode)
+                adjusted_reward = int(quest.reward_tokens * multiplier)
+            except Exception:
+                pass
+
         quest_info = {
             "quest_id": quest_id,
             "name": quest.name,
             "description": quest.description,
             "reward_tokens": quest.reward_tokens,
+            "adjusted_reward": adjusted_reward,
+            "multiplier": round(multiplier, 2),
             "type": quest.quest_type.value,
             "enabled": quest.enabled,
             "repeatable": quest.repeatable,
@@ -509,6 +559,7 @@ def get_agent_quests_status(agent_id: str) -> dict[str, Any]:
         "total_tokens_earned": total_rewards,
         "total_quests_completed": completed_count,
         "active_quests_count": len([q for q in quests_status if q["enabled"]]),
+        "stress_mode": current_mode.value if current_mode else None,
     }
 
 

src/timmy/stress_detector.py

@@ -0,0 +1,565 @@
+"""System stress detection for adaptive token rewards.
+
+Monitors system signals like flakiness, backlog growth, and CI failures
+to determine the current stress mode. Token rewards are then adjusted
+based on the stress mode to incentivize agents to focus on critical areas.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass, field
+from datetime import UTC, datetime, timedelta
+from enum import StrEnum
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from config import settings
+
+logger = logging.getLogger(__name__)
+
+# Path to stress mode configuration
+STRESS_CONFIG_PATH = Path(settings.repo_root) / "config" / "stress_modes.yaml"
+
+
+class StressMode(StrEnum):
+    """System stress modes.
+
+    - CALM: Normal operations, incentivize exploration and refactoring
+    - ELEVATED: Some stress signals detected, balance incentives
+    - HIGH: Critical stress, strongly incentivize bug fixes and stabilization
+    """
+
+    CALM = "calm"
+    ELEVATED = "elevated"
+    HIGH = "high"
+
+
+@dataclass
+class StressSignal:
+    """A single stress signal reading."""
+
+    name: str
+    value: float
+    threshold: float
+    weight: float
+    timestamp: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
+
+    @property
+    def is_triggered(self) -> bool:
+        """Whether this signal exceeds its threshold."""
+        return self.value >= self.threshold
+
+    @property
+    def contribution(self) -> float:
+        """Calculate this signal's contribution to stress score."""
+        if not self.is_triggered:
+            return 0.0
+        # Contribution is weighted ratio of value to threshold
+        return min(1.0, (self.value / max(self.threshold, 1.0))) * self.weight
+
+
+@dataclass
+class StressSnapshot:
+    """Complete stress assessment at a point in time."""
+
+    mode: StressMode
+    score: float
+    signals: list[StressSignal]
+    multipliers: dict[str, float]
+    timestamp: str = field(default_factory=lambda: datetime.now(UTC).isoformat())
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary for serialization."""
+        return {
+            "mode": self.mode.value,
+            "score": round(self.score, 3),
+            "signals": [
+                {
+                    "name": s.name,
+                    "value": s.value,
+                    "threshold": s.threshold,
+                    "triggered": s.is_triggered,
+                    "contribution": round(s.contribution, 3),
+                }
+                for s in self.signals
+            ],
+            "multipliers": self.multipliers,
+            "timestamp": self.timestamp,
+        }
+
+
+@dataclass
+class StressThresholds:
+    """Thresholds for entering/exiting stress modes."""
+
+    elevated_min: float = 0.3
+    high_min: float = 0.6
+
+    def get_mode_for_score(self, score: float) -> StressMode:
+        """Determine stress mode based on score."""
+        if score >= self.high_min:
+            return StressMode.HIGH
+        elif score >= self.elevated_min:
+            return StressMode.ELEVATED
+        return StressMode.CALM
+
+
+# In-memory storage for stress state
+_current_snapshot: StressSnapshot | None = None
+_last_check_time: datetime | None = None
+_config_cache: dict[str, Any] | None = None
+_config_mtime: float = 0.0
+
+
+def _load_stress_config() -> dict[str, Any]:
+    """Load stress mode configuration from YAML.
+
+    Returns:
+        Configuration dictionary with default fallbacks
+    """
+    global _config_cache, _config_mtime
+
+    # Check if config file has been modified
+    if STRESS_CONFIG_PATH.exists():
+        mtime = STRESS_CONFIG_PATH.stat().st_mtime
+        if mtime != _config_mtime or _config_cache is None:
+            try:
+                raw = STRESS_CONFIG_PATH.read_text()
+                _config_cache = yaml.safe_load(raw) or {}
+                _config_mtime = mtime
+                logger.debug("Loaded stress config from %s", STRESS_CONFIG_PATH)
+            except (OSError, yaml.YAMLError) as exc:
+                logger.warning("Failed to load stress config: %s", exc)
+                _config_cache = {}
+
+    if _config_cache is None:
+        _config_cache = {}
+
+    return _config_cache
+
+
+def get_default_config() -> dict[str, Any]:
+    """Get default stress configuration."""
+    return {
+        "thresholds": {
+            "elevated_min": 0.3,
+            "high_min": 0.6,
+        },
+        "signals": {
+            "flaky_test_rate": {
+                "threshold": 0.15,  # 15% flaky test rate
+                "weight": 0.3,
+                "description": "Percentage of tests that are flaky",
+            },
+            "p1_backlog_growth": {
+                "threshold": 5,  # 5 new P1 issues
+                "weight": 0.25,
+                "description": "Net growth in P1 priority issues",
+            },
+            "ci_failure_rate": {
+                "threshold": 0.2,  # 20% CI failure rate
+                "weight": 0.25,
+                "description": "Percentage of CI runs failing",
+            },
+            "open_bug_count": {
+                "threshold": 20,  # 20 open bugs
+                "weight": 0.2,
+                "description": "Total open issues labeled as bugs",
+            },
+        },
+        "multipliers": {
+            StressMode.CALM.value: {
+                "test_improve": 1.0,
+                "docs_update": 1.2,  # Calm periods good for docs
+                "issue_count": 1.0,
+                "issue_reduce": 1.0,
+                "daily_run": 1.0,
+                "custom": 1.0,
+                "exploration": 1.3,  # Encourage exploration
+                "refactor": 1.2,  # Encourage refactoring
+            },
+            StressMode.ELEVATED.value: {
+                "test_improve": 1.2,  # Start emphasizing tests
+                "docs_update": 1.0,
+                "issue_count": 1.1,
+                "issue_reduce": 1.1,
+                "daily_run": 1.0,
+                "custom": 1.0,
+                "exploration": 1.0,
+                "refactor": 0.9,  # Discourage risky refactors
+            },
+            StressMode.HIGH.value: {
+                "test_improve": 1.5,  # Strongly incentivize testing
+                "docs_update": 0.8,  # Deprioritize docs
+                "issue_count": 1.3,  # Reward closing issues
+                "issue_reduce": 1.4,  # Strongly reward reducing backlog
+                "daily_run": 1.1,
+                "custom": 1.0,
+                "exploration": 0.7,  # Discourage exploration
+                "refactor": 0.6,  # Discourage refactors during crisis
+            },
+        },
+    }
+
+
+def _get_config_value(key_path: str, default: Any = None) -> Any:
+    """Get a value from config using dot notation path."""
+    config = _load_stress_config()
+    keys = key_path.split(".")
+    value = config
+    for key in keys:
+        if isinstance(value, dict):
+            value = value.get(key)
+        else:
+            return default
+    return value if value is not None else default
+
+
+def _calculate_flaky_test_rate() -> float:
+    """Calculate current flaky test rate from available data."""
+    try:
+        # Try to load from daily run metrics or test results
+        test_results_path = Path(settings.repo_root) / ".loop" / "test_results.jsonl"
+        if not test_results_path.exists():
+            return 0.0
+
+        # Count recent test runs and flaky results
+        now = datetime.now(UTC)
+        cutoff = now - timedelta(days=7)
+
+        total_runs = 0
+        flaky_runs = 0
+
+        if test_results_path.exists():
+            for line in test_results_path.read_text().strip().splitlines():
+                try:
+                    entry = json.loads(line)
+                    ts_str = entry.get("timestamp", "")
+                    if not ts_str:
+                        continue
+                    ts = datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
+                    if ts >= cutoff:
+                        total_runs += 1
+                        if entry.get("is_flaky", False):
+                            flaky_runs += 1
+                except (json.JSONDecodeError, ValueError):
+                    continue
+
+        return flaky_runs / max(total_runs, 1)
+    except Exception as exc:
+        logger.debug("Failed to calculate flaky test rate: %s", exc)
+        return 0.0
+
+
+def _calculate_p1_backlog_growth() -> float:
+    """Calculate P1 issue backlog growth."""
+    try:
+        from dashboard.routes.daily_run import GiteaClient, _load_config
+
+        config = _load_config()
+        token = config.get("token")
+        client = GiteaClient(config, token)
+
+        if not client.is_available():
+            return 0.0
+
+        # Get current P1 issues
+        now = datetime.now(UTC)
+        cutoff_current = now - timedelta(days=7)
+        cutoff_previous = now - timedelta(days=14)
+
+        issues = client.get_paginated("issues", {"state": "all", "labels": "P1", "limit": 100})
+
+        current_count = 0
+        previous_count = 0
+
+        for issue in issues:
+            created_at = issue.get("created_at", "")
+            if not created_at:
+                continue
+            try:
+                created = datetime.fromisoformat(created_at.replace("Z", "+00:00"))
+                if created >= cutoff_current:
+                    current_count += 1
+                elif created >= cutoff_previous:
+                    previous_count += 1
+            except (ValueError, TypeError):
+                continue
+
+        # Return net growth (positive means growing backlog)
+        return max(0, current_count - previous_count)
+    except Exception as exc:
+        logger.debug("Failed to calculate P1 backlog growth: %s", exc)
+        return 0.0
+
+
+def _calculate_ci_failure_rate() -> float:
+    """Calculate CI failure rate from recent runs."""
+    try:
+        # Try to get CI metrics from Gitea or local files
+        ci_results_path = Path(settings.repo_root) / ".loop" / "ci_results.jsonl"
+        if not ci_results_path.exists():
+            return 0.0
+
+        now = datetime.now(UTC)
+        cutoff = now - timedelta(days=7)
+
+        total_runs = 0
+        failed_runs = 0
+
+        for line in ci_results_path.read_text().strip().splitlines():
+            try:
+                entry = json.loads(line)
+                ts_str = entry.get("timestamp", "")
+                if not ts_str:
+                    continue
+                ts = datetime.fromisoformat(ts_str.replace("Z", "+00:00"))
+                if ts >= cutoff:
+                    total_runs += 1
+                    if entry.get("status") != "success":
+                        failed_runs += 1
+            except (json.JSONDecodeError, ValueError):
+                continue
+
+        return failed_runs / max(total_runs, 1)
+    except Exception as exc:
+        logger.debug("Failed to calculate CI failure rate: %s", exc)
+        return 0.0
+
+
+def _calculate_open_bug_count() -> float:
+    """Calculate current open bug count."""
+    try:
+        from dashboard.routes.daily_run import GiteaClient, _load_config
+
+        config = _load_config()
+        token = config.get("token")
+        client = GiteaClient(config, token)
+
+        if not client.is_available():
+            return 0.0
+
+        issues = client.get_paginated("issues", {"state": "open", "labels": "bug", "limit": 100})
+
+        return float(len(issues))
+    except Exception as exc:
+        logger.debug("Failed to calculate open bug count: %s", exc)
+        return 0.0
+
+
+def _collect_stress_signals() -> list[StressSignal]:
+    """Collect all stress signals from the system."""
+    config = _load_stress_config()
+    default_config = get_default_config()
+    signals_config = config.get("signals", default_config["signals"])
+
+    signals = []
+
+    # Define signal collectors
+    collectors = {
+        "flaky_test_rate": _calculate_flaky_test_rate,
+        "p1_backlog_growth": _calculate_p1_backlog_growth,
+        "ci_failure_rate": _calculate_ci_failure_rate,
+        "open_bug_count": _calculate_open_bug_count,
+    }
+
+    for signal_name, collector in collectors.items():
+        signal_cfg = signals_config.get(signal_name, {})
+        default_cfg = default_config["signals"].get(signal_name, {})
+
+        try:
+            value = collector()
+            threshold = signal_cfg.get("threshold", default_cfg.get("threshold", 1.0))
+            weight = signal_cfg.get("weight", default_cfg.get("weight", 0.25))
+
+            signals.append(
+                StressSignal(
+                    name=signal_name,
+                    value=value,
+                    threshold=threshold,
+                    weight=weight,
+                )
+            )
+        except Exception as exc:
+            logger.debug("Failed to collect signal %s: %s", signal_name, exc)
+
+    return signals
+
+
+def _calculate_stress_score(signals: list[StressSignal]) -> float:
+    """Calculate overall stress score from signals.
+
+    Score is weighted sum of triggered signal contributions,
+    normalized to 0-1 range.
+    """
+    if not signals:
+        return 0.0
+
+    total_weight = sum(s.weight for s in signals)
+    if total_weight == 0:
+        return 0.0
+
+    triggered_contribution = sum(s.contribution for s in signals)
+    return min(1.0, triggered_contribution / total_weight)
+
+
+def _get_multipliers_for_mode(mode: StressMode) -> dict[str, float]:
+    """Get token multipliers for a specific stress mode."""
+    config = _load_stress_config()
+    default_config = get_default_config()
+
+    multipliers = config.get("multipliers", default_config["multipliers"])
+    mode_multipliers = multipliers.get(mode.value, {})
+    default_mode_multipliers = default_config["multipliers"].get(mode.value, {})
+
+    # Merge with defaults
+    result = default_mode_multipliers.copy()
+    result.update(mode_multipliers)
+
+    return result
+
+
+def detect_stress_mode(
+    force_refresh: bool = False,
+    min_check_interval_seconds: int = 60,
+) -> StressSnapshot:
+    """Detect current system stress mode.
+
+    Args:
+        force_refresh: Force a new check even if recently checked
+        min_check_interval_seconds: Minimum seconds between checks
+
+    Returns:
+        StressSnapshot with mode, score, signals, and multipliers
+    """
+    global _current_snapshot, _last_check_time
+
+    now = datetime.now(UTC)
+
+    # Return cached snapshot if recent and not forced
+    if not force_refresh and _current_snapshot is not None and _last_check_time is not None:
+        elapsed = (now - _last_check_time).total_seconds()
+        if elapsed < min_check_interval_seconds:
+            return _current_snapshot
+
+    # Collect signals and calculate stress
+    signals = _collect_stress_signals()
+    score = _calculate_stress_score(signals)
+
+    # Determine mode from score
+    config = _load_stress_config()
+    default_config = get_default_config()
+    thresholds_cfg = config.get("thresholds", default_config["thresholds"])
+    thresholds = StressThresholds(
+        elevated_min=thresholds_cfg.get("elevated_min", 0.3),
+        high_min=thresholds_cfg.get("high_min", 0.6),
+    )
+    mode = thresholds.get_mode_for_score(score)
+
+    # Get multipliers for this mode
+    multipliers = _get_multipliers_for_mode(mode)
+
+    # Create snapshot
+    snapshot = StressSnapshot(
+        mode=mode,
+        score=score,
+        signals=signals,
+        multipliers=multipliers,
+        timestamp=now.isoformat(),
+    )
+
+    # Cache result
+    _current_snapshot = snapshot
+    _last_check_time = now
+
+    # Log mode changes
+    if _current_snapshot is not None and _current_snapshot.mode != mode:
+        logger.info(
+            "Stress mode changed: %s -> %s (score: %.2f)",
+            _current_snapshot.mode.value if _current_snapshot else "none",
+            mode.value,
+            score,
+        )
+
+    return snapshot
+
+
+def get_current_stress_mode() -> StressMode:
+    """Get current stress mode (uses cached or fresh detection)."""
+    snapshot = detect_stress_mode()
+    return snapshot.mode
+
+
+def get_multiplier(quest_type: str, mode: StressMode | None = None) -> float:
+    """Get token multiplier for a quest type.
+
+    Args:
+        quest_type: Type of quest (test_improve, issue_count, etc.)
+        mode: Specific mode to get multiplier for, or None for current
+
+    Returns:
+        Multiplier value (1.0 = normal, 1.5 = 50% bonus, etc.)
+    """
+    if mode is None:
+        mode = get_current_stress_mode()
+
+    multipliers = _get_multipliers_for_mode(mode)
+    return multipliers.get(quest_type, 1.0)
+
+
+def apply_multiplier(base_reward: int, quest_type: str) -> int:
+    """Apply stress-based multiplier to a base reward.
+
+    Args:
+        base_reward: Base token reward amount
+        quest_type: Type of quest for multiplier lookup
+
+    Returns:
+        Adjusted reward amount (always >= 1)
+    """
+    multiplier = get_multiplier(quest_type)
+    adjusted = int(base_reward * multiplier)
+    return max(1, adjusted)
+
+
+def get_stress_summary() -> dict[str, Any]:
+    """Get a human-readable summary of current stress state."""
+    snapshot = detect_stress_mode()
+
+    # Generate explanation
+    explanations = {
+        StressMode.CALM: "System is calm. Good time for exploration and refactoring.",
+        StressMode.ELEVATED: "Elevated stress detected. Focus on stability and tests.",
+        StressMode.HIGH: "HIGH STRESS MODE. Prioritize bug fixes and test hardening.",
+    }
+
+    triggered_signals = [s for s in snapshot.signals if s.is_triggered]
+
+    return {
+        "mode": snapshot.mode.value,
+        "score": round(snapshot.score, 3),
+        "explanation": explanations.get(snapshot.mode, "Unknown mode"),
+        "active_signals": [
+            {
+                "name": s.name,
+                "value": round(s.value, 3),
+                "threshold": s.threshold,
+            }
+            for s in triggered_signals
+        ],
+        "current_multipliers": snapshot.multipliers,
+        "last_updated": snapshot.timestamp,
+    }
+
+
+def reset_stress_state() -> None:
+    """Reset stress state cache (useful for testing)."""
+    global _current_snapshot, _last_check_time, _config_cache, _config_mtime
+    _current_snapshot = None
+    _last_check_time = None
+    _config_cache = None
+    _config_mtime = 0.0

tests/unit/test_stress_detector.py

@@ -0,0 +1,294 @@
+"""Unit tests for the stress detector module.
+
+Tests stress signal calculation, mode detection, multipliers,
+and integration with the quest system.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from timmy.stress_detector import (
+    StressMode,
+    StressSignal,
+    StressSnapshot,
+    StressThresholds,
+    _calculate_stress_score,
+    _get_multipliers_for_mode,
+    apply_multiplier,
+    get_default_config,
+    reset_stress_state,
+)
+
+
+@pytest.fixture(autouse=True)
+def clean_stress_state():
+    """Reset stress state between tests."""
+    reset_stress_state()
+    yield
+    reset_stress_state()
+
+
+# ── Stress Mode Tests ──────────────────────────────────────────────────────
+
+
+class TestStressMode:
+    def test_stress_mode_values(self):
+        """StressMode enum has expected values."""
+        assert StressMode.CALM.value == "calm"
+        assert StressMode.ELEVATED.value == "elevated"
+        assert StressMode.HIGH.value == "high"
+
+
+# ── Stress Signal Tests ────────────────────────────────────────────────────
+
+
+class TestStressSignal:
+    def test_signal_not_triggered(self):
+        """Signal with value below threshold is not triggered."""
+        signal = StressSignal(
+            name="test_signal",
+            value=5.0,
+            threshold=10.0,
+            weight=0.5,
+        )
+        assert not signal.is_triggered
+        assert signal.contribution == 0.0
+
+    def test_signal_triggered(self):
+        """Signal with value at threshold is triggered."""
+        signal = StressSignal(
+            name="test_signal",
+            value=10.0,
+            threshold=10.0,
+            weight=0.5,
+        )
+        assert signal.is_triggered
+        assert signal.contribution == 0.5  # weight * min(1, value/threshold)
+
+    def test_signal_contribution_capped(self):
+        """Signal contribution is capped at weight when value >> threshold."""
+        signal = StressSignal(
+            name="test_signal",
+            value=100.0,
+            threshold=10.0,
+            weight=0.5,
+        )
+        assert signal.is_triggered
+        assert signal.contribution == 0.5  # Capped at weight
+
+    def test_signal_partial_contribution(self):
+        """Signal contribution scales with value/threshold ratio."""
+        signal = StressSignal(
+            name="test_signal",
+            value=15.0,
+            threshold=10.0,
+            weight=0.5,
+        )
+        assert signal.is_triggered
+        # contribution = min(1, 15/10) * 0.5 = 0.5 (capped)
+        assert signal.contribution == 0.5
+
+
+# ── Stress Thresholds Tests ────────────────────────────────────────────────
+
+
+class TestStressThresholds:
+    def test_calm_mode(self):
+        """Score below elevated_min returns CALM mode."""
+        thresholds = StressThresholds(elevated_min=0.3, high_min=0.6)
+        assert thresholds.get_mode_for_score(0.0) == StressMode.CALM
+        assert thresholds.get_mode_for_score(0.1) == StressMode.CALM
+        assert thresholds.get_mode_for_score(0.29) == StressMode.CALM
+
+    def test_elevated_mode(self):
+        """Score between elevated_min and high_min returns ELEVATED mode."""
+        thresholds = StressThresholds(elevated_min=0.3, high_min=0.6)
+        assert thresholds.get_mode_for_score(0.3) == StressMode.ELEVATED
+        assert thresholds.get_mode_for_score(0.5) == StressMode.ELEVATED
+        assert thresholds.get_mode_for_score(0.59) == StressMode.ELEVATED
+
+    def test_high_mode(self):
+        """Score at or above high_min returns HIGH mode."""
+        thresholds = StressThresholds(elevated_min=0.3, high_min=0.6)
+        assert thresholds.get_mode_for_score(0.6) == StressMode.HIGH
+        assert thresholds.get_mode_for_score(0.8) == StressMode.HIGH
+        assert thresholds.get_mode_for_score(1.0) == StressMode.HIGH
+
+
+# ── Stress Score Calculation Tests ─────────────────────────────────────────
+
+
+class TestStressScoreCalculation:
+    def test_empty_signals(self):
+        """Empty signal list returns zero stress score."""
+        score = _calculate_stress_score([])
+        assert score == 0.0
+
+    def test_no_triggered_signals(self):
+        """No triggered signals means zero stress score."""
+        signals = [
+            StressSignal(name="s1", value=1.0, threshold=10.0, weight=0.5),
+            StressSignal(name="s2", value=2.0, threshold=10.0, weight=0.5),
+        ]
+        score = _calculate_stress_score(signals)
+        assert score == 0.0
+
+    def test_single_triggered_signal(self):
+        """Single triggered signal contributes its weight."""
+        signals = [
+            StressSignal(name="s1", value=10.0, threshold=10.0, weight=0.5),
+        ]
+        score = _calculate_stress_score(signals)
+        # contribution = 0.5, total_weight = 0.5, score = 0.5/0.5 = 1.0
+        assert score == 1.0
+
+    def test_mixed_signals(self):
+        """Mix of triggered and non-triggered signals."""
+        signals = [
+            StressSignal(name="s1", value=10.0, threshold=10.0, weight=0.3),
+            StressSignal(name="s2", value=1.0, threshold=10.0, weight=0.3),
+            StressSignal(name="s3", value=10.0, threshold=10.0, weight=0.4),
+        ]
+        score = _calculate_stress_score(signals)
+        # triggered contributions: 0.3 + 0.4 = 0.7
+        # total_weight: 0.3 + 0.3 + 0.4 = 1.0
+        # score = 0.7 / 1.0 = 0.7
+        assert score == 0.7
+
+    def test_score_capped_at_one(self):
+        """Stress score is capped at 1.0."""
+        signals = [
+            StressSignal(name="s1", value=100.0, threshold=10.0, weight=1.0),
+            StressSignal(name="s2", value=100.0, threshold=10.0, weight=1.0),
+        ]
+        score = _calculate_stress_score(signals)
+        assert score == 1.0  # Capped
+
+
+# ── Multiplier Tests ───────────────────────────────────────────────────────
+
+
+class TestMultipliers:
+    def test_default_config_structure(self):
+        """Default config has expected structure."""
+        config = get_default_config()
+        assert "thresholds" in config
+        assert "signals" in config
+        assert "multipliers" in config
+
+    def test_calm_mode_multipliers(self):
+        """Calm mode has expected multipliers."""
+        multipliers = _get_multipliers_for_mode(StressMode.CALM)
+        assert multipliers["test_improve"] == 1.0
+        assert multipliers["docs_update"] == 1.2
+        assert multipliers["exploration"] == 1.3
+        assert multipliers["refactor"] == 1.2
+
+    def test_elevated_mode_multipliers(self):
+        """Elevated mode has expected multipliers."""
+        multipliers = _get_multipliers_for_mode(StressMode.ELEVATED)
+        assert multipliers["test_improve"] == 1.2
+        assert multipliers["issue_reduce"] == 1.1
+        assert multipliers["refactor"] == 0.9
+
+    def test_high_mode_multipliers(self):
+        """High stress mode has expected multipliers."""
+        multipliers = _get_multipliers_for_mode(StressMode.HIGH)
+        assert multipliers["test_improve"] == 1.5
+        assert multipliers["issue_reduce"] == 1.4
+        assert multipliers["exploration"] == 0.7
+        assert multipliers["refactor"] == 0.6
+
+    def test_multiplier_fallback_for_unknown_type(self):
+        """Unknown quest types return default multiplier of 1.0."""
+        multipliers = _get_multipliers_for_mode(StressMode.CALM)
+        assert multipliers.get("unknown_type", 1.0) == 1.0
+
+
+# ── Apply Multiplier Tests ─────────────────────────────────────────────────
+
+
+class TestApplyMultiplier:
+    def test_apply_multiplier_calm(self):
+        """Multiplier applies correctly in calm mode."""
+        # This test uses get_multiplier which reads from current stress mode
+        # Since we can't easily mock the stress mode, we test the apply_multiplier logic
+        base = 100
+        # In calm mode with test_improve = 1.0
+        result = apply_multiplier(base, "unknown_type")
+        assert result >= 1  # At least 1 token
+
+    def test_apply_multiplier_minimum_one(self):
+        """Applied reward is at least 1 token."""
+        # Even with very low multiplier, result should be >= 1
+        result = apply_multiplier(1, "any_type")
+        assert result >= 1
+
+
+# ── Stress Snapshot Tests ──────────────────────────────────────────────────
+
+
+class TestStressSnapshot:
+    def test_snapshot_to_dict(self):
+        """Snapshot can be converted to dictionary."""
+        signals = [
+            StressSignal(name="test", value=10.0, threshold=5.0, weight=0.5),
+        ]
+        snapshot = StressSnapshot(
+            mode=StressMode.ELEVATED,
+            score=0.5,
+            signals=signals,
+            multipliers={"test_improve": 1.2},
+        )
+
+        data = snapshot.to_dict()
+        assert data["mode"] == "elevated"
+        assert data["score"] == 0.5
+        assert len(data["signals"]) == 1
+        assert data["multipliers"]["test_improve"] == 1.2
+
+
+# ── Integration Tests ──────────────────────────────────────────────────────
+
+
+class TestStressDetectorIntegration:
+    def test_reset_stress_state(self):
+        """Reset clears internal state."""
+        # Just verify reset doesn't error
+        reset_stress_state()
+
+    def test_default_config_contains_all_signals(self):
+        """Default config defines all expected signals."""
+        config = get_default_config()
+        signals = config["signals"]
+
+        expected_signals = [
+            "flaky_test_rate",
+            "p1_backlog_growth",
+            "ci_failure_rate",
+            "open_bug_count",
+        ]
+
+        for signal in expected_signals:
+            assert signal in signals
+            assert "threshold" in signals[signal]
+            assert "weight" in signals[signal]
+
+    def test_default_config_contains_all_modes(self):
+        """Default config defines all stress modes."""
+        config = get_default_config()
+        multipliers = config["multipliers"]
+
+        assert "calm" in multipliers
+        assert "elevated" in multipliers
+        assert "high" in multipliers
+
+    def test_multiplier_weights_sum_approximately_one(self):
+        """Signal weights should approximately sum to 1.0."""
+        config = get_default_config()
+        signals = config["signals"]
+
+        total_weight = sum(s["weight"] for s in signals.values())
+        # Allow some flexibility but should be close to 1.0
+        assert 0.9 <= total_weight <= 1.1