agent-orchestration
Skill1 starsChanged 50 days agoMITView on GitHub
Decide whether to delegate work to subagents or a scripted workflow, and run the delegation well — topology from real data dependencies, fan-out width, minimal context packets, disjoint write ownership, model and effort per node, monitoring, failure handling and handoff validation. Use when a task is about to be split across subagents, parallel workers or a workflow graph, or when a running delegation needs steering. Not for work done directly in one session, and not for reviewing a finished change.
24 other projects use a version of this.
Adopted by
Most recently taken up by
- CassetteTapeCrackle/claude-starterstheir own version, first seen Nov 8, 2026
- juninmd/skillstheir own version, first seen Nov 8, 2026
- scoobydrew83/skillstheir own version, first seen Nov 3, 2026
- krema/agent-skillstheir own version, first seen Nov 1, 2026
- black-yt/skillstheir own version, first seen Nov 1, 2026
- Wiserax/wisecrafttheir own version, first seen Oct 25, 2026
Used with
Projects with it also use mcp-builder and code-review.
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.
MicrosoftSkillmcp-builder
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Also in 3 projects that use this
GoogleSkillcode-review
Adversarial systems, concurrency, memory-model, and architecture code review protocol. Use this skill whenever asked to review code, PRs, diffs, or branches in Filament.
Also in 3 projects that use this
Other versions24 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 24
- parcadei/Continuous-Claude★ 3.9ksame name
- parcadei/Continuous-Claude-v2★ 3.9ksame name
- parcadei/Continuous-Claude-v3★ 3.9ksame name
- codeaholicguy/ai-devkit★ 1.7ksame name
- GTMify/aigtm★ 25same name
- black-yt/skills★ 16same name
- Sathvikar01/Agent-engineering-skills★ 3same name
- juninmd/skills★ 2same name
- CassetteTapeCrackle/claude-starters★ 1same name
- hongdp/deskd★ 1same name
14 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 repository10 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-instructionsSkill
- backlog-maintenanceSkill
- estimate-timeSkill
- fabSkill
- independent-reviewSkill
- memory-hygieneSkill
- open-project-terminalSkill
- project-docsSkill
- testingSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.
0
Common
Most of the 23 edited versions we read keep these.
1
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 23
If it buys none of these, do the work directly.
“Common” is measured: kept by at least 6 in 10 of the 23 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
Agent orchestration
The session that owns the task is the manager. It owns decomposition, topology, allocation, integration, final verification and all communication with the user. Delegated agents are nodes: bounded work with an explicit input and an explicit output. This skill decides whether to delegate, what shape the work takes, and what each node receives. It does not choose an API — use whatever delegation, subagent or workflow mechanism the harness itself provides.
First, the decision
Delegation is a tool, not a default. A node must buy at least one of:
- parallelism — independent work finishing sooner side by side;
- independent judgment — a reviewer or adjudicator that must not share the author's context;
- specialization — a capability, tier or configuration the manager does not have;
- context isolation — a large read whose raw output would crowd out the manager's context.
If it buys none of these, do the work directly. Size is not a reason: a single-threaded task handed to one subagent is the manager's own work plus a briefing, a handoff and a validation. An explicit instruction from the user not to delegate ends the question.
A harness capability that already runs in its own context — a native review command, a built-in research agent — is a node in its own right. Prefer it to a hand-briefed subagent doing the same job when it fits, and brief it with the same discipline.
Invariants
These hold for every delegation, whichever reference is loaded.
- Dependencies come from data, not from the order things were mentioned. An edge exists only where a node consumes an upstream result.
- Concurrent writers never interfere. Each writing node gets a disjoint writable boundary as an absolute path, and its own worktree whenever scopes could overlap, a formatter, codegen or test run may touch shared files, or disjointness cannot be guaranteed. One owner per writable file at a time. An investigation node is read-only.
- Stay inside the concurrency cap the user's instructions set — nested agents included — and use 5 active subagents if none is set. A node asks before spawning its own. Read-only fan-out may go wider than write work.
- A scripted workflow needs the user's explicit opt-in. Without one, propose it with its rough width, loop bound and cost instead of launching it. Fix width and bounds before the run, and do not open a second fan-out beside a running one.
- Independent judgment gets evidence, never verdicts. A reviewer, adjudicator or competing approach receives the facts and none of another agent's conclusions about them.
- Deterministic plumbing never costs a model call — flattening, filtering, deduplication, sorting, routing on known fields, counters, retry and round bookkeeping belong in a script or in the manager.
- Never duplicate work except as a deliberately independent review or competing approaches the user asked for.
- The manager owns the completion claim. Final integration and verification are never delegated away, and a graph with an unfinished path is never reported as complete.
Process
| Step | Do | Read |
| 1. Shape | Pick the smallest topology that expresses the real dependencies, and size each fan-out | references/topology.md |
| 2. Brief | Build each node's context packet and brief: persona, mission, inputs, boundary, deliverable, verification duty, tier and effort | references/nodes.md |
| 3. Run | Monitor on events plus a heartbeat, answer escalations, validate each handoff | references/context-and-handoffs.md |
| 4. Recover | Resolve a failed node against the graph, escalating one step per redispatch | references/nodes.md |
| 5. Integrate | Merge the results, run the final verification yourself, report what was dropped or blocked | — |
Trivial or well-understood work gets no topology and no ceremony. Load only the reference the current step needs.
Monitoring
Completion notifications arrive on their own, so never poll to learn whether a node is done. Check in on an event — a handoff lands, a node escalates, or a long-running node has been quiet long enough that stalling or drift is likelier than progress. On top of that, run the same check every 5 minutes while ordinary subagents are active, so a node that went quiet without emitting an event is still caught; never tighter. A scripted workflow keeps its own deterministic monitoring instead.
At each check weigh progress, blockers, scope adherence, duplicated effort and stale work, then nudge, redirect, cancel or reassign. Answer an escalation promptly, and answer the same agent rather than opening a fresh one — a follow-up to an agent that already holds the context beats one that must rebuild it.
Output contract
The manager's report to the user states the outcome; what each node produced and how it was verified; everything dropped, blocked or left unresolved, by name; and the final verification it ran itself, with the command and its actual result.
Does it work?
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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.
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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.

