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whalesong-annotate

Attach review scores to recorded Whalesong traces and build comparison pairs — mark a session good/bad/regressed, then diff two runs deterministically. Use when evaluating agent changes, reviewing a run after the fact, or building a small eval set.

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包含的文件(1)

  • SKILL.md1.7 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Whalesong: annotate and compare

Two jobs: leave durable review marks on traces, and diff runs.

Scoring

whalesong_score writes a Langfuse-style score row onto a trace. Suggested names stay consistent so later queries aggregate cleanly:

  • review — categorical: accepted / needs-work / failed
  • rating — numeric 1–5
  • regression — boolean: true means this run was worse than its baseline

Always pass comment with the one-line reason; a bare score is a label nobody can act on later. Scores are the only write this plugin performs — confirm the trace id before writing.

Comparing runs

whalesong_compare takes two trace ids (or file paths) and returns a deterministic diff: overall similarity, per-channel and temporal similarity, deltas in duration / tokens / errors / p95 latency / finding counts, and the timeline points where the two runs diverged most.

Good pairs:

  • same task, before/after a prompt or model change,
  • a passing run vs. its scored regression pair,
  • the same workflow across two different agent CLIs.

Similarity near 1.0 means the runs' shape matched; it says nothing about output quality — inspect the differing span names the diff reports.

Typical flow

  1. Triage or list traces → pick the pair.
  2. whalesong_compare → report the deltas that matter.
  3. whalesong_score both runs so the verdict is durable.

Keep the write side boring: one score, one comment, correct trace id.

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