coordinator
Skill0 starsChanged 42 days agoMITView on GitHub
Use when an agent is dispatched as the coordinator of a batch of tasks - a high-capability model that reads, designs, decides and reviews, and hands implementation, browser automation, data imports, build/test loops and other long or menial work to worker agents on a cheaper model, run in parallel. Also use when the dispatch prompt says "coordinator", "design agent" or "hand off to workers".
15 other projects use a version of this.
Adopted by
Most recently taken up by
- Ubuntu-Buddha/claude-code-stranger-dangertheir own version, first seen Nov 8, 2026
- christofferbergj/agent-skillstheir own version, first seen Nov 1, 2026
- magicpro97/vibeflowtheir own version, first seen Oct 25, 2026
- 3176467770-jpg/orca-coordinator-skilltheir own version, first seen Oct 20, 2026
- langwatch/langwatchtheir own version, first seen Oct 17, 2026
- steig/worktendertheir own version, first seen Oct 17, 2026
Other versions15 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 15
- langwatch/langwatch★ 5ksame name
- raine/workmux★ 3ksame name
- Fullive-AI/Anima★ 1ksame name
- magicpro97/vibeflow★ 9same name
- Ubuntu-Buddha/claude-code-stranger-danger★ 8same name
- alphazede/bearing-lite★ 3same name
- SoFarSoGoodya/mathmodel-weave★ 2same name
- christofferbergj/agent-skills★ 1same name
- steig/worktender★ 1same name
- summelon/skills★ 1same name
5 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository34 other files it gives its agents.
- afkSkill
- apple-contactsSkill
- apple-devSkill
- apple-notesSkill
- apple-photosSkill
- bug-huntSkill
- catch-upSkill
- cherriSkill
- chrome-controlSkill
- claude-sessionsSkill
- discountsSkill
- forge-skillSkill
- groupmeSkill
- herdr-crewSkill
- iconsSkill
- imsgSkill
- language-selectionSkill
- laterSkill
- mac-controlSkill
- nix-mechanicsSkill
- notion-cliSkill
- posthogSkill
- quotaSkill
- redditSkill
- rustSkill
- scoutSkill
- semverSkill
- social-contentSkill
- spotifySkill
- travelSkill
- validate-ideaSkill
- verdictSkill
- web-reconSkill
- whatsappSkill
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 12 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
- Distinctive: line 17
Read every task in the batch in full: title, notes, links, the handoff lines in any session field, linked notes pages, and the notes/docs …
- Distinctive: line 34
Do it yourself only when all of these hold: one file, no browser, no build or test loop, no data import, under roughly ten minutes.
- Distinctive: line 88
Read the diff or the evidence it reported.
- Distinctive: line 91
Check the spec's constraints held (nothing outside scope, pacing rules respected, no leaked personal data in code or commits).
“Common” is measured: kept by at least 6 in 10 of the 12 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.
Files
Coordinator: design here, implement there
You are the expensive model in this batch. Your rate limit is the scarce
resource; every minute you spend watching a test run or a browser is a
minute of design the batch does not get. You read, decide, specify and
review. Workers implement. herdr-crew (or the dispatcher's crew skill) is
the command-syntax authority for launching and reading agents; this skill
is the division of labor.
Start
- Read every task in the batch in full: title, notes, links, the handoff lines in any session field, linked notes pages, and the notes/docs of each project the batch touches. Many tasks are partly done; the notes say how far. A task that is mostly done gets a "close with evidence" proposal, not a rebuild.
- Load the skills that cover the batch's repos and systems.
- Find overlaps inside the batch (same system, same data, same file) and with the neighbor batches the dispatch prompt names. Overlapping tasks are one unit of work with one worker, or a stated dependency, never two workers on the same files.
- Propose before changing anything when the dispatch says so: one line per task (do / merge into X / close as done / needs the user), the units of work with their worker count, and the numbered decisions the user must make. Wait for approval in your pane.
The threshold
Do it yourself only when all of these hold: one file, no browser, no build or test loop, no data import, under roughly ten minutes. Everything else is a worker. Handing off a two-minute edit costs more than the edit; babysitting a twenty-minute scrape costs more than the handoff.
Workers
- Model: the worker model the dispatch prompt names. Never start a worker on your own tier.
- Where:
- A Herdr tab for anything long, browser-driven, interactive with the user's accounts, or that the user may want to watch or step into. The user sees it beside your tab.
- A native subagent (the harness's Agent tool with the worker model) for a bounded chore with a clear deliverable and no browser: a test suite run, a refactor in one repo, a research read. Its output comes back summarized; ask for the facts you need in its brief.
- Parallelize liberally. Independent units launch in the same breath, one worker each. Only a shared file or a data dependency serializes work.
Launch (Herdr tab):
T=$(herdr --session <server> tab create --workspace <ws> --cwd "$HOME" --label "<unit>" --no-focus) P=$(echo "$T" | jq -r .result.root_pane.pane_id) herdr --session <server> agent start <batch>-<unit> --kind claude --pane "$P" --timeout 60000 -- --model <worker-model> herdr --session <server> agent wait <batch>-<unit> --until idle --timeout 60000 herdr --session <server> agent prompt <batch>-<unit> "$(cat "$SPEC")" --wait --until working --timeout 30000Worker names:
<batch>-<unit>,[a-z][a-z0-9_-]{0,31}, unique on the machine. Record name, tab, pane and session id in your crew table.
The spec
One file per unit of work, in your scratch directory. A worker has none of your context; the spec is all it knows.
- Goal in one sentence, and the acceptance check that proves it (a command, a screenshot, a row count).
- The task ids it closes and the repo path to work in (the session itself starts in the home folder).
- The decisions already made, so it does not re-open them, and the constraints (what not to touch, what needs the user, rate or pacing rules for anything that touches an external account).
- The skills to load first.
- What to report back: evidence, not narrative - diff summary, test output, counts, links. "Ends every turn with status and what it needs."
- What it must NOT do: no task-status writes (you own the task records), no deploys, no pushes to a deploying branch unless the spec says so.
Review
A worker's "done" is a claim. Before closing a task:
- Read the diff or the evidence it reported; run or spot-check the acceptance check yourself when it is cheap, or have a second worker verify when it is not.
- Check the spec's constraints held (nothing outside scope, pacing rules respected, no leaked personal data in code or commits).
- Then the task bookkeeping: status, completion flags, project relation, per the workspace's task rules. Status writes are yours alone.
A blocked worker is at an approval or a question: read its pane, relay it
to the user with your recommendation, never answer an approval yourself.
Report
To the user, one page per batch (the verdict skill, if available; else
the message itself), updated in place each round: one table unit | worker |
status | needs user, then the numbered decisions. No per-worker essays. Chat
gets the link and one sentence; the user reads neither tool output nor
files you leave behind.
Finish
Workers preserve their result, then close their own tabs after verified completion when nothing needs the user; a worker awaiting review stays open. You close your tab last, after every task in the batch is closed or handed back with its reason.
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
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.

