agent-fanout
Skill0 starsChanged 4 months agoMITView on GitHub
Use when a task requires reading or processing more than you need to retain — multi-file audits, repo-wide reviews, sweeps across many modules, several independent investigations. Triggers: "audit", "review everything", "find all", "check every", "išaudituok", "peržiūrėk visus", "surask visus", "patikrink visus". Also invoke when your context is filling with raw file dumps mid-task. Not for: single-fact lookups in a known location — do those directly, delegation would be slower.
1 other project uses a version of this.
Adopted by
Most recently taken up by
- SynapseGrid-Labs/AgentFanouttheir own version, first seen Oct 18, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 1
- SynapseGrid-Labs/AgentFanout★ 0same name
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 repository9 other files it gives its agents.
- adversarial-verifySkill
- compounding-engineeringSkill
- deployment-checkSkill
- goal-contractSkill
- karpathy-guidelinesSkill
- recon-firstSkill
- two-strike-debugSkill
- universal-loopSkill
- writing-claude-mdSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
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 60
Keep conclusions, discard raw material.
- Distinctive: line 61
Never cap silently: if you sampled or limited coverage, say what was dropped.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Fan-out
Triggers automatically when relevant; invoke directly with /agent-fanout or ask, e.g. "audit all modules in parallel".
Context is your scarcest resource; quality degrades as it fills with noise. Delegation is a context firewall: the subagent burns ITS context reading forty files and returns you a ten-line conclusion.
Decision tree
Does answering require reading more than you need to retain?
├─ NO (one fact, known location) → do it yourself, directly
└─ YES
├─ one independent investigation → one agent
├─ several INDEPENDENT ones → several agents IN PARALLEL,
│ launched in one message
└─ unknown-size discovery → waves until 2 consecutive waves
find nothing new (loop-until-dry)Delegation contract — every agent prompt contains three things
- TASK with boundaries — "examine X, ignore Y". Unbounded agents wander.
- RETURN SHAPE — structured data, not prose: "return JSON {files, risk, evidence}". The agent's final text is a return value, not a message to a human.
- DONE CRITERION — when to stop. Without it agents stop too early or never.
Flow rules
- Scout inline first (cheap listing), then fan out over the discovered work-list. You need the list's shape, not the whole task's shape.
- Pipeline by default: items flow through stages independently. Synchronize all agents only when a stage truly needs ALL prior results — dedup across the full set, early-exit on zero findings, cross-comparison.
- Loop-until-dry convergence: dedupe new findings against everything SEEN, not just accepted ones — otherwise rejected findings reappear every wave and the loop never terminates.
- Scale to the ask: "check" → 2–3 agents; "thorough audit" → a large fleet plus an adversarial-verify layer.
Discipline after delegating
- Delegated a search → never redo it yourself. Wait.
- Relay agent conclusions to the user — reports come to you, not them.
- Keep conclusions, discard raw material; that's what the firewall is for.
- Never cap silently: if you sampled or limited coverage, say what was dropped.
Relationship to other skills
- recon-first produces the work-list this skill fans out over.
- adversarial-verify filters what the fleet finds before anyone acts on it.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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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.

