aod-research-frontier
aod-research-frontier is a Product strategy & roadmaps skill for AI agents, published by Hankanman in the Area-Occupancy-Detection repository on GitHub. Skillspace rates aod-research-frontier D (66/100) and it passed the safety scan; the rating is based on static analysis only.
Use when scoping new research, a thesis/blog post, a grant-style pitch, or a "what should we work on next" planning session for Area Occupancy Detection — maps open, unproven "beyond state-of-the-art" problems (reliability vs raw sensors, per-home transition learning, predictive pre-heat/pre-light, Home-Assistant-core-grade quality) to concrete first steps in this repo. Do not use for day-to-day bugfixing, the active prior/likelihood accuracy campaign, or exact formula lookup — see "When NOT to use this" below.
- Grade
- D
- Quality
- 66 / 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
- 96 / 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
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.