| name | check-answer-consistency |
| description | Use this to get a cheap, reference-free signal that an LLM answer might be made up, by sampling the same prompt a few times and measuring agreement. Trigger on "is this answer reliable", "flag low-confidence answers", "cheap hallucination check", "confidence score without a ground truth", "self-consistency check". Ships a runnable, tested scorer you can put inline or on sampled traffic. |
| license | CC0-1.0 |
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 is pure Python, no install needed:
from self_consistency import consistency_score, is_likely_hallucination
answers = [call_model(prompt, temperature=0.7) for _ in range(5)]
score = consistency_score(answers)
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
- Sample with temperature > 0 (e.g. 5 samples). Consistency methods need diversity; a single deterministic answer tells you nothing.
- 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.
- 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/. They confirm identical answers score 1.0 (ignoring case/punctuation/whitespace), all-different answers score low and flag, a 3-of-4 majority scores 0.75 and does not flag, and that fewer than two answers returns 0.0.
Grounding
Consistency-based hallucination detection: SelfCheckGPT, Manakul et al. 2023 (arXiv:2303.08896). Meaning-level variant: semantic entropy, Farquhar et al., Nature 2024.
Anti-patterns
- Running it at temperature 0 (no diversity, the score is meaningless).
- Treating a high consistency score as proof of correctness (a model can be confidently and consistently wrong).
- Sampling N times inline on every request when latency/cost matter (sample offline or only on risky answers).
