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council

Convene a four-voice council for ambiguous decisions, tradeoffs, and go/no-go calls. Use when multiple valid paths exist and you need structured disagreement before choosing.

council 是什麼?

council is a Claude Code agent skill that convene a four-voice council for ambiguous decisions, tradeoffs, and go/no-go calls. Use when multiple valid paths exist and you need structured disagreement before choosing.

相容平台Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/council

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說明文件

council 是做什麼的?

Convene four advisors for ambiguous decisions:

  • the in-context Claude voice
  • a Skeptic subagent
  • a Pragmatist subagent
  • a Critic subagent

This is for decision-making under ambiguity, not code review, implementation planning, or architecture design.

When to Use

Use council when:

  • a decision has multiple credible paths and no obvious winner
  • you need explicit tradeoff surfacing
  • the user asks for second opinions, dissent, or multiple perspectives
  • conversational anchoring is a real risk
  • a go / no-go call would benefit from adversarial challenge

Examples:

  • monorepo vs polyrepo
  • ship now vs hold for polish
  • feature flag vs full rollout
  • simplify scope vs keep strategic breadth

When NOT to Use

Instead of councilUse
Verifying whether output is correctsanta-method
Breaking a feature into implementation stepsplanner
Designing system architecturearchitect
Reviewing code for bugs or securitycode-reviewer or santa-method
Straight factual questionsjust answer directly
Obvious execution tasksjust do the task

Roles

VoiceLens
Architectcorrectness, maintainability, long-term implications
Skepticpremise challenge, simplification, assumption breaking
Pragmatistshipping speed, user impact, operational reality
Criticedge cases, downside risk, failure modes

The three external voices should be launched as fresh subagents with only the question and relevant context, not the full ongoing conversation. That is the anti-anchoring mechanism.

Workflow

1. Extract the real question

Reduce the decision to one explicit prompt:

  • what are we deciding?
  • what constraints matter?
  • what counts as success?

If the question is vague, ask one clarifying question before convening the council.

2. Gather only the necessary context

If the decision is codebase-specific:

  • collect the relevant files, snippets, issue text, or metrics
  • keep it compact
  • include only the context needed to make the decision

If the decision is strategic/general:

  • skip repo snippets unless they materially change the answer

3. Form the Architect position first

Before reading other voices, write down:

  • your initial position
  • the three strongest reasons for it
  • the main risk in your preferred path

Do this first so the synthesis does not simply mirror the external voices.

4. Launch three independent voices in parallel

Each subagent gets:

  • the decision question
  • compact context if needed
  • a strict role
  • no unnecessary conversation history

Prompt shape:

You are the [ROLE] on a four-voice decision council.

Question:
[decision question]

Context:
[only the relevant snippets or constraints]

Respond with:
1. Position — 1-2 sentences
2. Reasoning — 3 concise bullets
3. Risk — biggest risk in your recommendation
4. Surprise — one thing the other voices may miss

Be direct. No hedging. Keep it under 300 words.

Role emphasis:

  • Skeptic: challenge framing, question assumptions, propose the simplest credible alternative
  • Pragmatist: optimize for speed, simplicity, and real-world execution
  • Critic: surface downside risk, edge cases, and reasons the plan could fail

5. Synthesize with bias guardrails

You are both a participant and the synthesizer, so use these rules:

  • do not dismiss an external view without explaining why
  • if an external voice changed your recommendation, say so explicitly
  • always include the strongest dissent, even if you reject it
  • if two voices align against your initial position, treat that as a real signal
  • keep the raw positions visible before the verdict

6. Present a compact verdict

Use this output shape:

## Council: [short decision title]

**Architect:** [1-2 sentence position]
[1 line on why]

**Skeptic:** [1-2 sentence position]
[1 line on why]

**Pragmatist:** [1-2 sentence position]
[1 line on why]

**Critic:** [1-2 sentence position]
[1 line on why]

### Verdict
- **Consensus:** [where they align]
- **Strongest dissent:** [most important disagreement]
- **Premise check:** [did the Skeptic challenge the question itself?]
- **Recommendation:** [the synthesized path]

Keep it scannable on a phone screen.

Persistence Rule

Do not write ad-hoc notes to ~/.claude/notes or other shadow paths from this skill.

If the council materially changes the recommendation:

  • use knowledge-ops to store the lesson in the right durable location
  • or use /save-session if the outcome belongs in session memory
  • or update the relevant GitHub / Linear issue directly if the decision changes active execution truth

Only persist a decision when it changes something real.

Multi-Round Follow-up

Default is one round.

If the user wants another round:

  • keep the new question focused
  • include the previous verdict only if it is necessary
  • keep the Skeptic as clean as possible to preserve anti-anchoring value

Anti-Patterns

  • using council for code review
  • using council when the task is just implementation work
  • feeding the subagents the entire conversation transcript
  • hiding disagreement in the final verdict
  • persisting every decision as a note regardless of importance

Related Skills

  • santa-method — adversarial verification
  • knowledge-ops — persist durable decision deltas correctly
  • search-first — gather external reference material before the council if needed
  • architecture-decision-records — formalize the outcome when the decision becomes long-lived system policy

Example

Question:

Should we ship ECC 2.0 as alpha now, or hold until the control-plane UI is more complete?

Likely council shape:

  • Architect pushes for structural integrity and avoiding a confused surface
  • Skeptic questions whether the UI is actually the gating factor
  • Pragmatist asks what can be shipped now without harming trust
  • Critic focuses on support burden, expectation debt, and rollout confusion

The value is not unanimity. The value is making the disagreement legible before choosing.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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