agentleFS
TheOnlyFusionCubeTheOnlyFusionCube/potetos-for-everyone/skills/show-me-your-work/SKILL.md

show-me-your-work

Skill18 starsChanged 53 days agoMITView on GitHub

Maintain a compact auditable decision trail during autonomous or high-stakes work. Use when the user will review decisions later.

We split this skill into the rules it gives an agent

4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 60 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Keep .potetos/decisions.tsv with columns:

    line 7
  2. Very specific:

    Use stable file/commit/test/URL references in evidence fields when available.

    line 14
  3. Distinctive:

    Update verification after the evidence exists.

    line 16
  4. Distinctive:

    Keep secrets and private transcript content out of committed logs.

    line 17

“Common” is measured: kept by at least 6 in 10 of the 60 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

Show Me Your Work

Keep .potetos/decisions.tsv with columns:

timestamp\tunit\tdecision\tevidence\talternatives\tverification\trevision

Rules:

  1. Log decisions that materially change scope, architecture, risk, or the next unit. Do not log every command.
  2. Use stable file/commit/test/URL references in evidence fields when available.
  3. Record rejected alternatives briefly enough to explain why the chosen path won.
  4. Update verification after the evidence exists; never pre-fill success.
  5. Keep secrets and private transcript content out of committed logs.

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.

No reviews yet

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.