coding-trace-analyze

coding-trace-analyze is an LLM evaluation & observability skill for AI agents, published by uw-syfi in the TraceLab repository on GitHub. Skillspace rates coding-trace-analyze D (64/100) and it passed the safety scan; the rating is based on static analysis only.

Run, summarize, plot, validate, and export normalized coding-trace JSONL files. Use when computing aggregate session/provider/model/token counts, normalized decoding-speed proxies, exact-reasoning TPOT/TTFT estimates, prefix versus append token distributions, tool latency/count summaries, generation-time and human-wait CDFs, KV-cache active ratio, cache-hit ratios, timing-fit CSV analyses, multi-round CSV traces, self-contained PNG artifacts, validator audits under validators/, or interpreting per-experiment artifacts under artifacts/.

Grade
D
Quality
64 / 100
Safety
Passed
GitHub stars
44

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubuw-syfi/TraceLab

Our review

Architecture
100 / 100
Maturity
Basic: a single SKILL.md
Security scan
Low-risk patterns
License
Apache-2.0
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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