
Council
- 15k installs
- 148 repo stars
- Updated July 24, 2026
- warpdotdev/common-skills
A structured recommendation from a diverse council of agents on whether to keep current approach, pursue an alternative, or use a hybrid; includes consensus, disagreements, risks, and confidence.
About
Council orchestrates multiple subagents with different models and assigned perspectives to investigate the same question in parallel, then synthesizes their independent reports into a final recommendation. Developers use it for high-stakes judgment calls: architecture tradeoffs, risky bug fixes, code review red-teaming, rollout safety decisions, and incident analysis. The workflow frames the question, selects model-diverse council members, briefs them with shared context, collects structured reports covering evidence/risks/confidence, and produces a concise decision memo that compares findings by quality rather than vote.
- Coordinates parallel subagents with model diversity (Claude Opus for architecture, GPT for implementation, OSS for contr
- Enforces structured reporting: evidence inspected, current behavior, alternative, risks, cost, recommendation, and confi
- Synthesizes findings into a decision memo that leads with the recommendation and explains why it wins against stated cri
- Supports both read-only investigations and implementation councils with isolated worktrees to prevent edit collisions
- Reuses council members for follow-up questions rather than launching replacements, preserving context and reducing redun
Council by the numbers
- 14,968 all-time installs (skills.sh)
- +2,003 installs in the week ending Jul 28, 2026 (Skillselion tracking)
- Ranked #55 of 16,659 AI & Agent Building skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Jul 28, 2026 (Skillselion catalog sync)
council capabilities & compatibility
- Capabilities
- launch multiple subagents with different models · assign model and perspective to each council mem · isolate local edits with git worktrees for imple · collect and compare structured reports from inde · synthesize findings into a decision memo with ri
- Works with
- github · gitlab
- Use cases
- code review · debugging · refactoring · security audit
- Platforms
- macOS · Windows · Linux · WSL
- Runs
- Remote server
- Pricing
- Bring your own API key
What council says it does
Run a model-diverse subagent council to investigate the same problem from multiple perspectives, compare findings, and produce a final recommendation.
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| Installs | 15k |
|---|---|
| repo stars | ★ 148 |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 24, 2026 |
| Repository | warpdotdev/common-skills ↗ |
What it does
Coordinate multiple AI models to investigate a decision or problem from different angles, then synthesize findings into a single recommendation.
Who is it for?
Architecture decisions, bug-fix risk assessment, code review red-teaming, rollout/incident analysis, competing technical approach evaluation, and any judgment-heavy call where blind spots matter.
Skip if: Straightforward fact lookups, simple code generation, tasks that do not require deliberation or competing perspectives.
When should I use this skill?
User asks for a council, second opinions, multiple models/agents to evaluate one question, parallel investigation, red-team/blue-team comparison, or help deciding between competing approaches.
What you get
Developer makes a well-informed decision backed by evidence from multiple models and angles, with explicit risk and unknowns called out.
- Synthesized council recommendation
- Per-subagent investigation reports
By the numbers
- Default roster: three council members (Opus for architecture, GPT for implementation, OSS for contrarian critique)
- Structured report format: 7 required elements (evidence, behavior, alternative, risks, cost, recommendation, confidence)
- Follow-up model: reuse existing children for follow-ups to preserve context rather than launching replacements
Files
Council
Use this skill to coordinate multiple subagents investigating the same question, with different models first and different assigned perspectives second, then synthesize their reports into one recommendation.
This skill is best for judgment-heavy tasks: architecture tradeoffs, risky bug fixes, code review red-teaming, rollout decisions, incident analysis, and “is this alternative worth pursuing?” questions.
Workflow
1. Frame the council question
State the decision the council should answer in one sentence. Identify:
- the competing options or hypothesis under review;
- the codebase, branch, PR, issue, design, or artifact to inspect;
- whether agents should be read-only or may make code changes;
- the final decision criteria, such as correctness, risk, implementation cost, testability, rollout safety, or product behavior.
If the user’s request is ambiguous, ask only the minimum clarification needed. Otherwise choose sensible defaults and proceed.
2. Choose council members
Prioritize model diversity. A council should not default to three agents on the same model with different angles; use that only when the available launch configuration cannot provide multiple useful models, or when the user explicitly asks for one model. If model diversity is unavailable, say so briefly before falling back to perspective-only diversity.
Preferred default roster for a three-member council:
- Opus 4.7 or the strongest available Claude/Opus reasoning model: architecture, correctness, and edge-case analysis.
- GPT 5.5 or the strongest available GPT/Codex model: implementation-grounded review, feasibility, and test strategy.
- An open-source model such as Kimi 2.6, GLM 5.1, or the strongest available OSS/local model: contrarian critique, hidden assumptions, and alternative framing.
If one of these exact models is unavailable in the active harness, use the closest available model from that family and note the substitution. If no open-source model is available, use a third distinct frontier model if possible; otherwise use the strongest remaining model with a deliberately adversarial or specialist angle.
Assign both a model and an angle to each member. Avoid making the angles redundant with the models; for example, do not ask all members to do general architecture review. Useful angle combinations include:
- architect/correctness reviewer;
- implementation/testability reviewer;
- red-team, security, performance, or product-risk reviewer;
- contrarian “argue against the obvious solution” reviewer.
When different children need different models, launch them in separate run_agents calls because model selection is run-wide. If the requested model resolves differently than expected, treat the resolved launch settings as authoritative and continue unless they make the task infeasible.
When using non-default harnesses, choose valid model IDs for that harness. For example, Claude Code may expose claude-opus-4-7, Codex may expose gpt-5.5, and open-source models depend on the currently configured local or remote provider. Do not invent unsupported model IDs; if a desired model is not available, select the closest supported substitute and preserve the intended angle diversity.
For read-only investigations, keep all children in the same checkout and explicitly tell them not to edit files. For implementation or prototyping councils, give each local child its own git worktree and branch so they cannot collide.
3. Brief before launching
For explicit orchestration requests, briefly tell the user which council members you plan to launch and what each will investigate, then wait for approval before calling run_agents.
The shared brief should include:
- repository path or artifact location;
- current branch or base context;
- the exact question to answer;
- relevant background and known concerns;
- required files/symbols to inspect, if known;
- constraints, especially read-only/no commits/no PRs;
- expected report format.
Keep launch prompts short enough that task titles stay compact. If a long brief causes launch validation issues, launch with a minimal prompt and send the full brief to the child agents immediately afterward.
4. Ask for structured reports
Ask every council member to return:
1. exact file paths, symbols, docs, or evidence inspected; 2. the current behavior or current implementation; 3. the alternative being evaluated; 4. correctness risks and edge cases; 5. implementation and testing cost; 6. recommendation: keep current approach, pursue alternative, or use a hybrid; 7. confidence level and unknowns.
Encourage independence. Do not share one child’s findings with the others unless you are intentionally doing a second-round critique.
5. Collect reports
Read completion messages as they arrive. Do not rely on lifecycle success alone; the useful output is in the child’s report.
If a report is missing key evidence or makes an unsupported claim, send a focused follow-up question to that same child rather than launching a replacement. Reuse existing children for follow-ups because they retain context.
6. Synthesize the recommendation
Compare the reports by evidence quality, not by vote count. In the final answer:
- lead with the recommendation;
- call out consensus and disagreements;
- explain why the recommended option wins against the decision criteria;
- explicitly address the user’s stated concern;
- include relevant file paths/symbols without overloading the answer;
- distinguish “do now” from optional future hardening;
- mention confidence and material unknowns.
Prefer a concise decision memo over a transcript summary. The user needs the distilled recommendation, not every intermediate detail.
Final answer template
Use this shape unless the task calls for something different:
## Recommendation
[One or two sentences with the decision.]
## Why
- [Key reason 1]
- [Key reason 2]
- [Key reason 3]
## Tradeoffs and risks
- [Risk or caveat]
- [Testing/rollout implication]
## Final call
[Concrete next step: merge current change, pursue alternative, hybrid, run tests, etc.]Practical notes
- If the council is read-only, tell children not to modify files, commit, create branches, or open PRs.
- If the council involves PR or branch work, follow the repository’s normal version-control rules and use isolated worktrees for parallel local edits.
- If the council is about code review feedback, mark review comments resolved only after the underlying issue is actually addressed.
- Do not expose internal child agent IDs in user-facing summaries unless the user explicitly asks for them.
Related skills
How it compares
Choose council when a technical decision benefits from parallel multi-model perspectives rather than a single-agent answer.
FAQ
Do all council members need to be different models?
Prioritize model diversity first; only default to perspective-only diversity if the launch harness cannot provide multiple useful models or the user explicitly asks for one model.
Can council members edit code, or are they read-only?
Frame the question to specify whether agents are read-only or may make code changes. For implementation councils, give each local child its own git worktree and branch to prevent collisions.
How do I synthesize the reports?
Compare by evidence quality, not vote count. Lead with the recommendation, call out consensus and disagreements, explain why it wins against decision criteria, and explicitly address the user's stated concern.
Is Council safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.