aod-research-methodology
aod-research-methodology is an Engineering planning & specs skill for AI agents, published by Hankanman in the Area-Occupancy-Detection repository on GitHub. Skillspace rates aod-research-methodology D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Use when turning a hunch, user report, or measurement into an accepted change to AOD's Bayesian/learning math — deciding whether a root-cause theory is proven, designing the discriminating experiment (DB query, simulator run, or predicted-numbers test), shipping an experimental capability behind a config flag, or adversarially refuting a claimed bug/fix. Also load this before trusting any "verified" claim from a single home's data or a single refutation pass. NOT for routine bugfixes with an obvious, already-known mechanism (use aod-debugging-playbook) or for the state of specific frontier research questions (use aod-research-frontier).
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 309
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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