agentleFS
joinwell52-AIjoinwell52-AI/CodeFlowMu-open/skills/eval-observation-writing/SKILL.md

eval-observation-writing

Skill2 starsChanged 4 months agoMITView on GitHub

Write CodeFlowMu EVAL observations with evidence, impact, confidence, and follow-up. Use when EVAL notices behavior, UX, governance, implementation, or documentation gaps without turning them into REPORTs.

The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use when recording observed behavior or gaps.

    line 10
  2. Distinctive:

    Do not treat observation as REPORT.

    line 25
  3. Distinctive:

    Do not change lifecycle.

    line 26
  4. Distinctive:

    Do not overclaim without evidence.

    line 27

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

EVAL Observation Writing

When to use

Use when recording observed behavior or gaps.

Rules

  • State what was seen.
  • Attach evidence.
  • Describe impact and confidence.
  • Suggest follow-up without changing lifecycle.

Required output

Use docs/skills/eval-playbook/observation-writing.md.

Forbidden actions

  • Do not treat observation as REPORT.
  • Do not change lifecycle.
  • Do not overclaim without evidence.

Minimal example

Observation: README lacks Chinese version
Impact: ADMIN discovery friction
Follow-up: local DEV task

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

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  • It worked
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  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.