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[External Plugin]: agentcouncil #5133
Description
Plugin name
agentcouncil
Short description
Multi-model council for Copilot CLI: three subagents on different model families (Anthropic, OpenAI, Google) answer independently, then build on each other or attack the leading answer before an orchestrator writes the final result from anonymized inputs.
GitHub repository
Sentry01/AgentCouncil
Plugin path inside the repository
No response
Ref to review
v2.0.0
Commit SHA to review
abad48ddd3117056bbda12f2f7e87142d8c43369
Version
2.0.0
License identifier
MIT
Author name
Shlomi Shaki
Author URL
Homepage URL
No response
Keywords
multi-agent, multi-model, council, debate, brainstorm, stress-test, adversarial, collaborative, orchestration, second-opinion
Additional notes for reviewers
Not a duplicate of agent-council. The existing agent-council listing (Avyayalaya/agent-council) is a different project: a 5-agent quality gate that returns SHIP / REVISE / HOLD on text artifacts. AgentCouncil has a separate codebase, purpose and protocol. It answers open questions (decisions, designs, reviews, research) by seating one model from each of three families and making them interact once. The agentcouncil name keeps it distinct from the existing listing's name.
What it adds beyond a single model:
- Copilot CLI's
tasktool can run each subagent on a different model. AgentCouncil uses this to seat Anthropic, OpenAI and Google models side by side, so mistakes specific to one model family get caught. - The orchestrator only sees anonymized, reordered responses, which removes the bias toward the seat that shares its family.
- Two modes: collaborative (draft, improve, synthesize) and adversarial (draft, triage, attack, verdict). A light path covers simple questions in 4 calls.
- If a model is unavailable, the seat falls back to the next one on its list without collapsing two seats into one family. Every answer ends with a footer listing the models that actually ran.
- Every subagent prompt tells it to work read-only.
Contents:
- An Agent Plugins 1.0
plugin.json. - One skill:
skills/agentcouncil/SKILL.md, a short router, with phase prompts inreferences/. - One thin custom agent in
com.github.copilot/agents/, which loads the skill. There are no MCP servers, hooks or scripts that run at install or runtime.
Checks run before submitting:
@microsoft/vally0.18.0 lint: passed, covering spec-compliance and valid-refs.- In a clean
COPILOT_HOME, these work against the GitHub repo:copilot plugin marketplace add Sentry01/AgentCouncil,copilot plugin install agentcouncil@agentcouncilandcopilot plugin update.--agent agentcouncil:agentcouncilresponds. - Routing evals (22 cases, in the repo under
evals/): 44/44 over 2 runs on claude-sonnet-5.5, and 22/22 each on gpt-5.5 and gemini-3.8-flash. - End-to-end light and full adversarial councils ran on the default roster.
Releases are published from CHANGELOG.md. The repo is also its own marketplace (.github/plugin/marketplace.json).
Submission checklist
- The plugin lives in a public GitHub repository.
- The ref and/or sha I provided is immutable (release tag and/or full 40-character commit SHA), not a branch.
- This submission follows this repository's contribution, security, and responsible AI policies.
- This plugin is not already listed in the Awesome Copilot marketplace.
Activity
github-actions commentedon Oct 17, 2026
✅ External plugin intake passed
This submission passed automated intake validation and quality checks and is ready for maintainer review.
View workflow run · Download full quality gate logs
v2.0.0abad48ddd3117056bbda12f2f7e87142d8c43369Reviewer signals
These are non-blocking heuristics for maintainer review; they are not evidence of misconduct or low quality.
Quality gate summary
Legend: ✅ pass ·⚠️ warning · 🛑 fail
spec compliance output (✅ pass)
vally lint output (✅ pass)
install smoke test output (✅ pass)
Version match output
Ref/SHA consistency output
Canvas structure output
Canonical external.json payload
{ "name": "agentcouncil", "description": "Multi-model council for Copilot CLI: three subagents on different model families (Anthropic, OpenAI, Google) answer independently, then build on each other or attack the leading answer before an orchestrator writes the final result from anonymized inputs.", "version": "2.0.0", "author": { "name": "Shlomi Shaki", "url": "https://github.com/Sentry01" }, "repository": "https://github.com/Sentry01/AgentCouncil", "license": "MIT", "keywords": [ "multi-agent", "multi-model", "council", "debate", "brainstorm", "stress-test", "adversarial", "collaborative", "orchestration", "second-opinion" ], "source": { "source": "github", "repo": "Sentry01/AgentCouncil", "ref": "v2.0.0", "sha": "abad48ddd3117056bbda12f2f7e87142d8c43369" } }