aod-learning-accuracy-campaign
aod-learning-accuracy-campaign is a Model training & evaluation skill for AI agents, published by Hankanman in the Area-Occupancy-Detection repository on GitHub. Skillspace rates aod-learning-accuracy-campaign D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Executable, decision-gated campaign for making learned priors and likelihoods (global prior, 168 time-priors, per-sensor P(E|H) from correlation analysis, decay half-life resolution) trustworthy on real homes. Load this when a user reports "prior stuck at 0.99/0.01", "occupancy probability doesn't match reality", "false transitions", "decay clears too fast/slow", "correlation/likelihood looks wrong", or when asked to touch data/prior.py, data/analysis.py's PriorAnalyzer, db/correlation.py, or data/decay.py half-life resolution. This is the flagship operation order for the project's hardest live problem — treat it as a runbook, not background reading.
- 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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