world-model-diagnostic
world-model-diagnostic is a Structured reasoning & decision frameworks skill for AI agents, published by NateBJones-Projects in the OB1 repository on GitHub. Skillspace rates world-model-diagnostic D (63/100) and it passed the safety scan; the rating is based on static analysis only.
Twenty-minute conversational diagnostic for assessing a company's world-model readiness. Use when the user wants to map their company to the right world-model paradigm, identify where the highest-fidelity signal lives, audit the boundary layer between facts and interpretation, flag simulated-judgment exposure, and leave with a first/second/third build sequence. Works in plain chat and compounds when Open Brain search/capture tools are present.
- Grade
- D
- Quality
- 63 / 100
- Safety
- Passed
- GitHub stars
- 4,614
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
- 80 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill 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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