agentleFS
fullerzzfullerzz/agent-skills/skills/principle-prove-it-works/SKILL.md

principle-prove-it-works

Skill2 starsChanged 28 days agoMITView on GitHub

Apply after completing a task, before declaring done. Verify against the real artifact (run the feature, read the actual value, inspect the diff), not a proxy, self-report, or 'it compiles.'

We split this skill into the rules it gives an agent

6 rules. 5 are common, 1 is distinctive, and 0 are very specific to this project.

  • 5

    Common

    Most of the 46 edited versions we read keep these.

  • 1

    Distinctive

    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. Common:

    Verify every task output by checking the real thing directly.

    line 9
  2. Common:

    Check the real thing, not a proxy:

    line 13
  3. Common:

    Check process liveness directly, not indirectly through derived state

    line 14
  4. Common:

    Read the actual value, not a cached or derived representation

    line 15
  5. Common:

    When verification fails, suspect the observation method before suspecting the system

    line 16
  6. Distinctive:

    Keep the artifact visible for the human.

    line 22

“Common” is measured: kept by at least 6 in 10 of the 46 edited versions of this file we could read. “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

Prove It Works

Verify every task output by checking the real thing directly. Do not infer from proxies, self-reports, or "it compiles."

Why: Unverified work has unknown correctness. Indirect verification (file mtimes, output freshness, agent self-reports, cached screenshots) feels cheaper than direct observation. Acting on a wrong inference costs far more than checking the source.

Check the real thing, not a proxy:

  • Check process liveness directly, not indirectly through derived state
  • Read the actual value, not a cached or derived representation
  • When verification fails, suspect the observation method before suspecting the system

Script the check when you can

The strongest proof is a deterministic script that re-runs the same comparison, not a one-time eyeball. Write the script, run it, and keep its output as an artifact a reviewer can re-run instead of trusting your word.

Keep the artifact visible for the human. For large or complex work, keep an auditable trail using show-me-your-work. Commit it only when the user's request authorizes a commit; task size alone does not.

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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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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • 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.