multi-agent-analysis
Skill4 starsChanged 7 months agoMITView on GitHub
Dispatch 1–3 subagents to analyze or verify something, then judge their findings yourself. TRIGGER when: user asks for 'multi-agent analysis', 'use subagents to analyze/verify', or 'use parallel subagents' to investigate something.
In this repository42 other files it gives its agents.
- AGENTS.mdAGENTS.md
- agent-soulSkill
- audit-transcripts-for-learningsSkill
- best-answerSkill
- check-coherenceSkill
- compress-fileSkill
- deep-modulesSkill
- design-critiqueSkill
- durable-docs-updateSkill
- execute-chatSkill
- execute-planSkill
- explain-deeplySkill
- explain-simplySkill
- find-gapsSkill
- fix-verify-loopSkill
- generate-mockupsSkill
- grill-meSkill
- handoffSkill
- interview-meSkill
- jtbdSkill
- map-design-languageSkill
- memory-pruneSkill
- place-factSkill
- post-purchase-ui-extensionSkill
- product-interviewSkill
- prove-behaviorSkill
- refine-fileSkill
- second-opinionSkill
- sentry-analysisSkill
- shopify-dev-mcpSkill
- structure-proseSkill
- tech-designSkill
- test-completed-planSkill
- tighten-fileSkill
- tighten-instructionSkill
- tldrSkill
- triageSkill
- trim-specSkill
- two-pass-reviewSkill
- validate-answerSkill
- vet-factSkill
- write-planSkill
The file
We split this skill into the rules it gives an agent
3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
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 13
Task list: if the ask has independent parts, list them first (TaskCreate) so none is dropped.
- Distinctive: line 17
Size: Use 1 subagent by default for a focused question or when scope and complexity are limited.
- Distinctive: line 44
Add a tree / ASCII map or table where it aids understanding — current vs proposed, file map, or flow.
≈ “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
Multi-Agent Analysis
Steps
Step 1 — Frame the problem
- Frame: restate the problem in a line or two and name its type — verify a claim/fix · which answer is correct · architecturally correct + simplest · how something works.
- Task list: if the ask has independent parts, list them first (TaskCreate) so none is dropped.
Step 2 — Size and dispatch
- Size: Use 1 subagent by default for a focused question or when scope and complexity are limited. Use 2–3 subagents only when the problem's scope, size, or complexity is large enough to create independent angles worth analyzing in parallel.
- Dispatch: send all selected
Agentcalls in one message. Model per task —opusfor architectural/design judgment,sonnetfor mapping, code-tracing, or doc/web research. - Brief each:
- What to pass: the problem statement, its own angle, and file paths (not pasted contents).
- Ground every claim in source it read.
- Return the Findings schema, not a raw dump.
- Flag anything material it notices outside the question.
- Don't fan out — no subagents of its own; keep the tree one level deep.
Step 3 — Ground and apply your verdict
Collapse duplicates first — the same finding from 2+ subagents is one, keep its highest confidence. Then judge every finding at c ≥ 0.70 yourself: read the cited code or doc, and don't act on a summary you haven't verified. List anything below 0.70 without acting on it.
Then apply your own verdict on top: agree, correct, or overrule each with reasoning — never relay verbatim. Where findings conflict, resolve deliberately against source; don't average.
Step 4 — Present for decision
Lead with the answer, then report in this shape:
**Analysis result:**
- Answer: [one-line plain-language answer]
- Findings: [claim — your verdict (agree / correct / overrule) — confidence 0.00–1.00, one per line]
- Observations: [material finding outside scope | none]
- Open questions: [unresolved item | none]Add a tree / ASCII map or table where it aids understanding — current vs proposed, file map, or flow. Stop there — analyze and recommend only; the user or a build skill decides what to apply.
Output Schema
Findings {
problem: string, // the framed problem statement
findings: [ {
claim: string, // discovery or recommended action, one line
reasoning: string, // why — or pro/con — as appropriate
evidence: string, // file:line, or doc/URL it's grounded in
confidence: number // 0.00–1.00
} ],
observations: string[], // material findings outside the framed question
open_questions: string[] // what could not be resolved from source
}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.

