check-answer-consistencylisted

# Check answer consistency A model that knows the answer repeats it across re-samples; a hallucinating model wanders. This skill ships a small consistency scorer that turns that idea into a number, with no reference answer required. ## Use the bundled script [`scripts/self_consistency.py`](scripts/self_consistency.py) is pure Python, no install needed: ```python from self_consistency import consistency_score, is_likely_hallucination answers = [call_model(prompt, temperature=0.7) for _ in range(5)] score = consistency_score(answers) # 1.0 = all agree, ~0 = all differ if is_likely_hallucination(answers, threshold=0.5): route_to_review() ``` Run it directly to see confident vs unsure examples: `python scripts/self_consistency.py`. ## How to apply it 1. **Sample with temperature > 0** (e.g. 5 samples). Consistency methods need diversity; a single deterministic answer tells you nothing. 2. **Score, then act:** flag/block/route-to-review when the score is below your threshold, or attach it to the trace as a reliability signal. 3. **Reserve it for high-stakes answers** if latency matters, sampling N times multiplies cost and latency by ~N. This is a lightweight SelfCheckGPT-style check (normalized-string agreement). For meaning-level robustness (wording differs but the fact is the same), move to entailment clustering / semantic entropy, see the `detect-hallucinations` skill. ## Validation Run the tests: `pytest skills/check-answer-consistency/tests/`. The