night-market-research-methodology

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Turn hunches into accepted results: worthiness score, evidence bar, research-to-rules. Use when vetting ideas. Not for QA; use night-market-validation-and-qa.

Code & Development 323 stars 29 forks Updated today MIT

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Skill Content

# Night Market Research Methodology The discipline that turns a hunch into an accepted result in this repo. An "accepted result" is a change that survived the evidence bar and landed through change control as a rule, a skill module, a config gate, or an ADR. Everything else is either a local working note or a documented retirement. This skill covers the full path: score the idea, experiment behind a default-off flag, meet the evidence bar, land the durable artifact, or retire the idea on the record. ## The evidence bar A claim graduates from hunch to result only when it passes all four tests. 1. **One mechanism explains all observations, including negatives.** If the hypothesis explains the three failing cases but not why the fourth case passed, it is incomplete. Keep digging until a single mechanism accounts for everything you saw. 2. **Predict numbers before running.** Write down the expected measurement first, then measure. In-repo anchor: the forced-eval harness labels expected activations in `prototypes/forced-eval/activation_cases.json` before any run, then compares baseline against treatment with a McNemar paired test (a significance test for paired binary outcomes). 3. **Survive assigned adversarial refutation.** Assign a reviewer or agent whose explicit job is to break the claim. Use `Skill(attune:war-room)` for hard-to-reverse decisions and `Skill(imbue:rigorous-reasoning)` to counter agreement bias. A claim nobody tried to ...

Details

Author
athola
Repository
athola/claude-night-market
Created
8 months ago
Last Updated
today
Language
Python
License
MIT

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