coding-trace-normalize

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

Explain and work with the normalized coding-trace JSONL row format produced by extract_claude_rounds.py, extract_codex_rounds.py, and collect_llm_traces.py. Use when interpreting normalized rows, provider differences, token semantics, Claude cache creation/read/uncached fields, prefix versus append accounting, input-event summaries, timing_events, tools metadata, trace_key identity, or converting raw Claude/Codex concepts into the common analysis schema.

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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