agentleFS
christofferbergjchristofferbergj/agent-skills/skills/agents/swarm/SKILL.md

swarm

Skill1 starsChanged 24 days agoMITView on GitHub

Use for Pstack delegation or independent review; organize coverage slices, races, or parallel exploration.

87 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use poteto-help, setup-pstack and 1 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

    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

  • 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 23 projects that use this

  • jaydubya818Skill

    poteto-mode

    Use when non-trivial work should follow the smallest rigorous Jstack workflow.

    Also in 27 projects that use this

The file

Files
SKILL.md

Swarm and host execution

For every Pstack delegate or cross-vendor review, read the execution contract. It owns native host APIs, model settings, isolation, review restrictions, and disclosed fallback. A caller can use that contract for one delegate without starting a coverage swarm.

To hand a check that needs Computer Use to Codex, follow the Computer Use route.

1. Frame

Open a task list with one entry per phase before dispatching anything. Name the done predicate, report or artifact, and required coverage. Choose slices, an identical-brief race, or a mix. Declare a race rule before dispatch: first passing result, rank all, or best-of. Workers take the swarm workers role from the execution contract; for a model race, name each arm's model before dispatch instead. Choose the smallest useful worker count within the user's budget and host capacity; total jobs can exceed concurrent slots.

Give each writer its own worktree or output directory. A verification or measurement brief names the exact revisions, sample definition, method, and acceptance predicate. Complete when every required slice or arm has an independent brief and output location.

2. Dispatch

Use the host's native delegation tools and actual schema. Start independent jobs before waiting, respecting the available concurrency window. In T3 Code, a job that needs another machine's tools, or would load this one for long, runs there through the T3 route's On another machine. Each brief supplies the goal, boundary, repository guidance, resolved rubric or reference paths, evidence to return, and allowed writes. A worker that can prove a defect reports ISSUES and lists every issue it can prove, not only the first. Prefer cross-vendor critics through the execution contract when available.

Complete when each job has a handle or an explicit dispatch failure. A dropped worker is a gap, never an implicit success.

3. Aggregate

Collect terminal results. Accept PASS, ISSUES, or BLOCKED only with the brief's required revisions, method, and evidence. Retry a missing or malformed result once when recoverable; after another miss, record the gap. Inspect each finding rather than treating agreement as proof. Coverage requires every mandatory slice; a race uses the declared selection rule.

Complete when every dispatched job is accounted for and every required slice has evidence or a named gap.

4. Report

Return one compact table of jobs, verdicts, revisions, and evidence, followed by actionable issues, dropouts, and the race result, without pasting raw worker output. Disclose sequential execution, same-vendor review, or unavailable delegation. Complete when the report distinguishes actual independent passes from local fallback work.

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.