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ScinoScino/fstack/skills/principle-prove-it-works/SKILL.md

principle-prove-it-works

Skill18 starsChanged 54 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.'

49 other projects use a version of this, 46 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use principle-explain-the-number, setup-pstack and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ianclearySkill

    principle-explain-the-number

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    Also in 30 projects that use this

  • bnemaSkill

    setup-pstack

    Configure which Pi models pstack uses per role. Detects available provider/model specs and writes pstack's user configuration. Use for /setup-pstack, "configure pstack models", or changing pstack model choices.

    Also in 25 projects that use this

  • ianclearySkill

    poteto-help

    Guides users through pstack setup, /poteto-mode, and picking the skill, playbook, or principle for a task. Type /poteto-help with a question.

    Also in 14 projects that use this

  • eryzerzSkill

    make-bot-ui

    Use when building a custom UI (page, dashboard, buttons) that should wake a Grok Bot over a webhook, when the user must provide a webhook sender key, or when exposing that UI on Tailscale.

    Also in 19 projects that use this

  • tristanphvnSkill

    ketchup-video

    Catch the user up like /keel:ketchup, with a short silent video of slides for what happened and the waiting calls as text under it. Use for /keel:ketchup-video.

    Also in 11 projects that use this

  • drafaelSkill

    in-your-own-words

    Restate the user's goals and the problem they are trying to solve to check understanding. Manual invocation only; use when the user explicitly invokes in-your-own-words or requests this understanding check. Do not solve the task or rewrite the assistant's last answer.

    Also in 20 projects that use this

The file

We split this skill into the rules it gives an agent

10 rules. 6 are common, 4 are distinctive, and 0 are very specific to this project.

  • 6

    Common

    Most of the 49 edited versions we read keep these.

  • 4

    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 10
  2. Distinctive:

    Pattern: After completing any task, ask: "how do I prove this actually works?"

    line 14
  3. Common:

    Check the real thing, not a proxy:

    line 16
  4. Common:

    Check process liveness directly, not indirectly through derived state

    line 17
  5. Common:

    Read the actual value, not a cached or derived representation

    line 18
  6. Common:

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

    line 19
  7. Distinctive:

    Run it and exercise the actual feature path

    line 23
  8. Distinctive:

    Check the full chain: does data flow from input to output?

    line 24
  9. Distinctive:

    When verifying delegated work, inspect the actual output artifact (git diff, file contents, runtime behavior), not the delegate's summary.

    line 28
  10. Common:

    Keep the artifact visible for the human.

    line 34

“Common” is measured: kept by at least 6 in 10 of the 49 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.

Pattern: After completing any task, ask: "how do I prove this actually works?"

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

Code and features:

  1. Build it (necessary but not sufficient)
  2. Run it and exercise the actual feature path
  3. Check the full chain: does data flow from input to output?
  4. For integrations, test the full communication path end-to-end

Delegation: trust artifacts, not self-reports. When verifying delegated work, inspect the actual output artifact (git diff, file contents, runtime behavior), not the delegate's summary. Agents report what they intended, not always what happened.

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. A script comparing the old and new compiled output catches what a glance misses.

Keep the artifact visible for the human. Commit it only for large or complex work where the trail has to be auditable later, like a big port or migration (the show-me-your-work skill). Most work just needs it visible, not committed.

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