worldos-latency-forensics
worldos-latency-forensics is an LLM evaluation & observability skill for AI agents, published by electricsheephq in the WorldOS repository on GitHub. Skillspace rates worldos-latency-forensics D (64/100) and it passed the safety scan; the rating is based on static analysis only.
Root-cause and fix per-beat claude -p DM latency in WorldOS (the living-world D&D 5e engine). Use when a slow DM beat is the binding release blocker (beats trip the gate / a persona gives up on the wait), when choosing a latency lever (effort / lean / alwaysLoad / streaming / scene_context), or when someone proposes a "speed" change (a helper model, a --fast flag, a model switch) you need to validate or refute. Encodes the SDK-instrumented measurement method, the quality-cost lever taxonomy, and the hypotheses already REFUTED so they are not re-chased. Companion to worldos-dev.
- Grade
- D
- Quality
- 64 / 100
- Safety
- Passed
- GitHub stars
- 22
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
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
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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