world-model-diagnostic
world-model-diagnostic is a Business analysis & reporting skill for AI agents, published by rjmurillo in the ai-agents repository on GitHub. Skillspace rates world-model-diagnostic C (77/100) and it passed the safety scan; the rating is based on static analysis only.
Twenty-minute diagnostic mapping a team to a world-model paradigm (vector DB, structured ontology, signal-fidelity). Use when you say "run the world model diagnostic", "audit our world model", "which world model architecture fits us", or "audit where we automate judgment". Use for AI readiness assessments and boundary-layer audits where automated judgment is safe. Do NOT use for mapping team operating rhythms or decision flows; use work-operating-model instead.
- Grade
- C
- Quality
- 77 / 100
- Safety
- Passed
- GitHub stars
- 38
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
- 93 / 100
- Maturity
- Packaged: licensed, with a version or references
- 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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