agentleFS
isanchez-aguilarisanchez-aguilar/relayrace-ai/skills/work/SKILL.md

work

Skill0 starsChanged 12 days agoApache-2.0View on GitHub

Run new work in ANY repository or notes folder through the generic pipeline — planner scopes it, implementer (and designer/writer) do it, tester breaks it, another provider reviews it, then a draft PR. Use for anything bigger than a one-file change in places that do not define their own /feature skill (a repo with its own /feature keeps using it). The owner asks in plain language; route it here yourself.

42 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use authoring, done 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.
  • automagik-devSkill

    authoring

    Write or revise a Genie skill so it survives the shipped contract — frontmatter, house size, starter card, and runtime-neutral voice.

    Also in 3 projects that use this

  • robzolkosSkill

    done

    Reset the working directory for the next task by ensuring no uncommitted changes exist, then switching to the main branch and pulling latest.

    Also in 3 projects that use this

  • Google CloudSkill

    fix

    Plan a remediation strategy after a test failure.

    Also in 4 projects that use this

  • AlexPEClubSkill

    refine

    Always use when the user wants to discuss an exsiting feature or specification. Open an existing feature spec to improve, extend, or fundamentally challenge it. Pass the feature ID as argument (e.g. /refine PROJ-2).

    Also in 3 projects that use this

  • MicrosoftSkill

    merge

    Merge changes from the topic branch to the merge base branch. Use when the user wants to merge their session's work back to the base branch.

    Also in 3 projects that use this

  • Microsoft AzureSkill

    test

    Also in 6 projects that use this

The file

We split this skill into the rules it gives an agent

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

  • 0

    Common

    Most of the 37 edited versions we read keep these.

  • 2

    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. Distinctive:

    Follow the repository's own AGENTS.md/CLAUDE.md over anything here.

    line 9
  2. Distinctive:

    If the request reads two ways that lead to different work, ask with AskUserQuestion (options, your recommendation first) before planning.

    line 13

“Common” is measured: kept by at least 6 in 10 of the 37 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

/work — generic pipeline for new work

Say in one line that you are taking this route ("I am treating this as a size-M task"). Follow the repository's own AGENTS.md/CLAUDE.md over anything here.

0. Clarify

If the request reads two ways that lead to different work, ask with AskUserQuestion (options, your recommendation first) before planning. Do not ask what the code or docs already answer.

1. Plan

Size it. S (one file): do it in this session, skip to step 3. M/L: launch the planner agent with the request and the repo path. If it returns ## Blocking questions, ask the user and resume it with SendMessage. For L, show the user the plan and wait for their approval.

2. Do

Launch implementer lanes in parallel — one area per lane, never two lanes on one file. Add designer first for a visible change and writer for docs or notes. Pass model explicitly on every Agent call: haiku for searches and mechanical edits, sonnet for implementation and docs; opus only when a money or security judgement truly cannot be covered by another provider. Never fable without asking the owner first. Delegate in the order of the model-selection playbook (local Qwen, Jules, Gemini, Antigravity's Claude, Codex, and Claude subagents last; relayrace providers shows the owner's own order). Mechanical, verifiable chunks may go to the local model with relayrace qwen --optional --task … when the repo has the kit installed; SKIPPED means do it yourself.

3. Break it

Launch tester on the result. Fix what it finds (implementer), then re-run the failing checks.

4. Independent review

Branch, commit (in the language specified by AGENTS.md, with the attribution lines the session gives you), push and open a draft PR. Then have a DIFFERENT provider review it read-only with .ai/prompts/review.md, following the cross-review skill: probe the sandbox, list every path, verify every cited line. Fix confirmed findings and review again.

5. Close

Merge only when the owner has authorized it. Report what changed, the commands you ran with results, what was not verified, and the PR link. If the work touched the AI setup, record it in the project's setup docs or work log.

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.