stuck-detection

name	stuck-detection
version	1.2.0
model	haiku
model-rationale	cost. The 'haiku' rolling alias resolves via the platform model_tiers map to a tier priced below the sonnet-tier harness default; this unit is routing/mechanical work where the cheaper tier suffices (ADR-080 rule 3).
description	Detect agent conversation loops via topic-signature similarity and emit a self-reflection nudge. Use as an orchestrator guard against repetitive responses and token-burning loops. Use when you say "check stuck loop", "detect repetition", "agent looping", or "reset stuck history". Do NOT use for semantic similarity checks or multi-turn intent tracking; use a richer evaluation tool for those.
license	MIT

Stuck Detection Guard

Detect when an agent is repeating the same topics across turns and surface a self-reflection nudge so the orchestrator can break the loop. Lightweight, deterministic, no external services.

Triggers

Trigger Phrase	Operation
check stuck loop	Score current response against recent history
detect repetition	Flag repeating topic signatures
agent looping	Emit a loop-breaking nudge
reset stuck history	Clear history after a confirmed topic change

Quick Reference

Symptom	Cause	Fix
Agent re-states the same status every turn	Topic signature unchanged across turns	Inject the nudge payload before the next turn
False positive after a real topic change	Stale history kept after the user redirected	Run reset to clear history
Stuck never triggers despite obvious repetition	Responses below MIN_TEXT_LENGTH (50 chars)	Lower threshold or pass full response text
Unrelated turns flagged as stuck	Generic vocabulary inflates Jaccard score	Raise DEFAULT_SIMILARITY_THRESHOLD toward 0.75

When to Use

Use this skill when:

• Building an orchestrator that risks repeating tool calls or status updates
• A long-running session shows degraded variety in responses
• You want a cheap pre-flight guard before sending a response

Use a richer evaluation instead when:

• You need semantic similarity (this uses lexical Jaccard only)
• You need multi-turn intent tracking
• You need cross-session coherence checks

How It Works

1. Topic signature. Strip stopwords, take the top N significant words by frequency, sort them, and join with commas. This is the response's signature.
2. History. Append each signature with a UTC timestamp to a JSON file. Keep only the most recent entries.
3. Jaccard similarity. Compare the current signature against the most recent K entries. Count how many exceed the similarity threshold.
4. Trigger. When K consecutive recent entries are similar, emit a nudge payload telling the orchestrator to ask the user a question or change topic.

Process

1. Run python3 .claude/skills/stuck-detection/stuck_detection.py check "<text>"
2. If the JSON result has "stuck": true, inject the nudge field into the orchestrator's next system prompt so the model self-corrects on its next turn. The nudge is internal control text wrapped in <stuck-detection> tags. Do not render it directly to the end user.
3. After a confirmed topic change, call reset to clear stale history.

Usage

# Check a response for stuck pattern (text via arg or stdin)
python3 .claude/skills/stuck-detection/stuck_detection.py check "your response text"
echo "your response" | python3 .claude/skills/stuck-detection/stuck_detection.py check

# Show current history state
python3 .claude/skills/stuck-detection/stuck_detection.py status

# Reset history after a topic change
python3 .claude/skills/stuck-detection/stuck_detection.py reset

# Extract signature only (debugging)
python3 .claude/skills/stuck-detection/stuck_detection.py extract "your text"

Output

{
  "stuck": true,
  "signature": "deploy,error,pipeline,retry,timeout",
  "similar_count": 3,
  "nudge": "<stuck-detection>\nSELF-REFLECTION: Loop detected.\n..."
}

Configuration

Defaults are conservative. Override at the function call site or via constants in stuck_detection.py:

Constant	Default	Purpose
DEFAULT_MAX_HISTORY	10	Entries retained on disk
DEFAULT_STUCK_THRESHOLD	3	Consecutive similar turns to trigger
DEFAULT_SIMILARITY_THRESHOLD	0.6	Jaccard cutoff for "similar"
MIN_TEXT_LENGTH	50	Skip short responses
SIGNATURE_SIZE	5	Top words per signature

History Path

Resolution order:

1. --history <path> CLI flag
2. STUCK_DETECTION_HISTORY environment variable (full path)
3. STUCK_DETECTION_SESSION environment variable (per-session file under the XDG state dir, e.g. history-<session>.json); use this when running multiple concurrent sessions to prevent cross-session contamination
4. $XDG_STATE_HOME/claude-stuck-detection/history.json
5. ~/.local/state/claude-stuck-detection/history.json

Paths are expanded and resolved before use. Avoid relative .. segments in --history or STUCK_DETECTION_HISTORY if you need the resolved location to match the input.

Verification

After integrating, confirm the following before relying on the guard:

• python3 .claude/skills/stuck-detection/stuck_detection.py status returns valid JSON with the configured history_length
• Three calls to check with the same long input return "stuck": true on the third call
• A call to reset zeroes history_length on the next status check
• The history file lives outside the repository working tree (XDG state dir or env-overridden path)
• Generated nudge text is forwarded into the orchestrator system prompt, not rendered to the end user
• Concurrent check calls never produce truncated JSON (atomic write via temp file + replace)
• A malformed history file (non-list, missing keys, non-string values) is treated as empty and not propagated to callers
• Tests in tests/skills/stuck-detection/ pass under uv run pytest

Anti-Patterns

Avoid	Why	Instead
Calling check on every token	High overhead, noisy signatures	Call once per agent turn
Treating stuck: false as proof of progress	Lexical similarity misses semantic loops	Pair with task-completion checks
Hardcoding the history path in callers	Breaks portability across environments	Use the env var or CLI flag
Ignoring reset after topic changes	History stays polluted	Reset when the user redirects

Integration

Call from a hook or orchestrator wrapper. When stuck is true, prepend the nudge to the system prompt for the next turn so the model self-corrects.

Testing

uv run pytest tests/skills/stuck-detection/ -v