council
Skill1 starsChanged 2 months agoMITView on GitHub
Run a structured decision council across five perspectives - strategy, execution, finance, risk, and contrarian - then anonymise the outputs and synthesise them into a single verdict with consensus level, key tension, and flagged blind spots. Use for consequential decisions where one perspective is not enough.
143 other projects use a version of this.
Adopted by
Most recently taken up by
- Beskars-Code/Skillstheir own version, first seen Nov 8, 2026
- Gabriel-Lafrance/Skillstheir own version, first seen Nov 8, 2026
- ong6/skillstheir own version, first seen Nov 8, 2026
- lbussell/agent-skillstheir own version, first seen Nov 8, 2026
- scottwater/skillstheir own version, first seen Nov 8, 2026
- T0mSIlver/skillstheir own version, first seen Nov 8, 2026
Used with
Projects with it also use agent-payment-x402, motion-advanced and 10 more.
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- +1 more from scottwater
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Other versions143 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 143
- simstudioai/sim★ 30ksame name
- 0xNyk/council-of-high-intelligence★ 4.8ksame name
- t3dotgg/tstack★ 2.5ksame name
- danieleteti/delphimvcframework★ 1.4ksame name
- warpdotdev/common-skills★ 630same name
- QuixiAI/agi-memory★ 623same name
- QuixiAI/Hexis★ 623same name
- zaxbysauce/opencode-swarm★ 519same name
- ZaxbyHub/opencode-swarm★ 518same name
- boshu2/agentops★ 453same name
133 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.
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- github-publishSkill
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- jd-analyserSkill
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The file
We split this skill into the rules it gives an agent
2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 128 edited versions we read keep these.
2
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 69
"Divided" is a real and useful outcome: Do not manufacture consensus that is not there.
- Distinctive: line 84
Use the blind spot section as the reality check: If the contrarian read lands, reconsider — that section is the highest-value output and …
“Common” is measured: kept by at least 6 in 10 of the 128 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
Council
Five advisors evaluate your question independently and in parallel. Their outputs are stripped of identity and shuffled. An anonymous judge then synthesises them into one verdict.
The anonymisation is the point. A judge that knows which advisor said what will weight by persona rather than by argument.
Usage
/council "Should we launch in France before India?"
/council "What price point maximises adoption?"
/council --quick "Is this partnership worth pursuing?"--quick runs three advisors instead of five (strategy, finance, risk). Use it when the
decision is real but not existential.
The five advisors
| Advisor | Lens | Asks |
| Vishwamitra | Strategy | Long-range positioning, moats, what this makes possible in three years |
| Chanakya | Execution | Tactical feasibility, stakeholder dynamics, resource reality |
| Lakshmi | Finance | Unit economics, ROI, pricing power, capital efficiency |
| Kali | Risk | Downside scenarios, failure modes, what kills this |
| Narada | Contrarian | Blind spots, the unfashionable read, what everyone in the room is missing |
Each is spawned as a parallel subagent receiving the exact question, its persona brief, and one instruction: output a 150-word perspective, do not reference other advisors, do not identify yourself.
How to run it
- Spawn all advisors in parallel. Independence is the source of the signal — an advisor who has read another's output is no longer a second opinion.
- Anonymise before judging. Strip every identifier, relabel as "Advisor A", "Advisor B", and shuffle the order randomly.
- Judge the anonymised set. The judge receives the original question and the shuffled perspectives, and nothing else.
Output format
## Council Verdict
**Question:** [restated]
**Consensus:** Strong Proceed | Proceed with Caution | Divided | Do Not Proceed
### Where advisors agreed
- …
### Key tension
[The main disagreement, and what it implies for the decision]
### Blind spot flagged
[The most unexpected or contrarian point raised]
### Strategic next step
[One concrete action]"Divided" is a real and useful outcome. Do not manufacture consensus that is not there. A council that always converges is not deliberating, and forcing agreement destroys exactly the information you convened it to get.
Reading the verdict
The council informs. It does not decide, and it does not override your judgement.
What it reliably surfaces:
- Unstated assumptions in how the question was framed
- Priorities that compete across domains, which one person rarely holds simultaneously
- Risk scenarios that optimism skipped
- Genuine consensus, where it exists
Use the blind spot section as the reality check. If the contrarian read lands, reconsider — that section is the highest-value output and the easiest to skim past.
Limits, stated honestly
- Five perspectives from one model are five framings, not five independent minds. Correlated blind spots stay blind. This widens the aperture; it does not eliminate the shared bias.
- The council is only as good as the question. A leading question produces a leading verdict. State the decision neutrally.
- Everything stays local. Subagents only, no external services, and the full deliberation is visible in the transcript.
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.

