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council-builder议会建设者

Agent Skill

council-builder 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

23,472

周安装

978

GitHub Stars

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下载量

7,824
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:council-builder(议会建设者)
来源仓库:https://github.com/abdullah4ai/council-builder
安装命令:
openclaw skills install council-builder
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install council-builder

简介

council-builder用于为OpenClaw构建个性化的AI Agent团队。

  • 通过用户访谈和工作流程分析,创建具有不同专业角色的代理。
  • 适合复杂任务分解和多角色协作的场景需求。council-builder 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 需要用户提供详细的工作流程信息以进行精准配置。
  • 建议提前准备相关业务流程文档以便更好适配。

SKILL.md

name
council-builder
description
Build a personalized team of AI agent personas for OpenClaw. Interviews the user, analyzes their workflow, then creates specialized agents with distinct personalities, adaptive model routing (Fast/Think/Deep/Strategic), weekly learning metrics, visual architecture docs, and inter-agent coordination. USE WHEN: user wants to create an agent team/council, build specialized AI personas, set up multi-agent workflows, 'build me a team of agents', 'create agents for my workflow', 'set up an agent council', 'I want specialized AI assistants', 'build me a crew'. DON'T USE WHEN: user wants a single skill (use skill-creator), wants to install existing skills (use clawhub), or wants to chat with existing agents (just route to them).

Council Builder

Build a team of specialized AI agent personas tailored to the user's actual needs. Each agent gets a distinct personality, self-improvement capability, and clear coordination rules.

Workflow

Phase 1: Discovery

Interview the user to understand their world. Ask in batches of 2-3 questions max.

Round 1 - Identity:

  • What do you do? (profession, main activities, industry)
  • What tools and platforms do you use daily?

Round 2 - Pain Points:

  • What tasks eat most of your time?
  • Where do you feel you need the most help?

Round 3 - Preferences:

  • What language(s) do you work in? (for agent communication style)
  • Any specific domains you want covered? (coding, content, finance, research, scheduling, etc.)

Optional - History Analysis: If the user has existing OpenClaw history, scan it for patterns:

  • Check memory/ files for recurring tasks
  • Check existing workspace structure for active projects
  • Check installed skills for current capabilities

Do NOT proceed to Phase 2 until confident you understand the user's needs. Ask follow-up questions if anything is unclear.

Phase 2: Planning

Based on discovery, design the council:

  1. Determine agent count: 3-7 agents. Fewer is better. Each agent must earn its existence.
  2. Define each agent: Name, role, specialties, personality angle
  3. Map coordination: Which agents feed data to which
  4. Present the plan to the user in a clear table:
| Agent | Role | Specialties | Personality |
|-------|------|-------------|-------------|
| [Name] | [One-line role] | [Key areas] | [Personality angle] |
  1. Get explicit approval before building. Allow adjustments.

Naming agents:

  • Give them memorable, short names (not generic like "Agent 1")
  • Names should hint at their role but feel like characters
  • Can be inspired by any theme the user likes, or choose strong standalone names
  • See references/example-councils.md for naming patterns and complete council examples across different industries

Phase 3: Building

Run the initialization script first to create the directory skeleton:

./scripts/init-council.sh <workspace-path> <agent-name-1> <agent-name-2> ...

Then, for each approved agent, populate the files. Read references/soul-philosophy.md before writing any SOUL.md.

Directory structure per agent:

agents/[agent-name]/
├── SOUL.md           # Personality, role, rules (see soul-philosophy.md)
├── AGENTS.md         # Agent-specific coordination rules
├── memory/           # Agent's memory directory
├── .learnings/       # Self-improvement logs
│   ├── LEARNINGS.md
│   ├── ERRORS.md
│   └── FEATURE_REQUESTS.md
└── [workspace dirs]  # Role-specific output directories

For each agent's SOUL.md:

  1. Read references/soul-philosophy.md for the writing guide
  2. Read assets/SOUL-TEMPLATE.md for the structure
  3. Customize deeply for this agent's role and personality
  4. Every SOUL must be unique. No copy-paste between agents.

For each agent's AGENTS.md:

  1. Use assets/AGENT-AGENTS-TEMPLATE.md as base
  2. Define what this agent reads from and writes to
  3. Define handoff rules with other agents

For gotchas.md:

  1. Use assets/GOTCHAS-TEMPLATE.md as base
  2. Populate with 1-2 known pitfalls specific to this agent's domain
  3. See references/gotchas-patterns.md for examples

For config.json:

  1. Use assets/CONFIG-TEMPLATE.json as base
  2. Set agent_name, leave setup_complete as false
  3. See references/config-patterns.md for role-specific examples

For scripts/:

  1. Create role-specific starter scripts (see references/agent-scripts-patterns.md)
  2. At minimum, create a verification script for the agent's output type
  3. Include a README.md listing what each script does

For references/:

  1. Create verification-checklist.md using assets/VERIFICATION-CHECKLIST-TEMPLATE.md
  2. Optionally create domain-guide.md and common-patterns.md with role-specific content

For hooks/ (optional):

  1. See references/hooks-patterns.md for the pattern
  2. Create hooks relevant to the agent's risk profile
  3. Not every agent needs hooks; focus on agents with destructive capabilities

For .learnings/ files:

  1. Copy structure from assets/LEARNINGS-TEMPLATE.md
  2. Initialize empty log files

For the root AGENTS.md:

  1. Use assets/ROOT-AGENTS-TEMPLATE.md as base
  2. Create the routing table for all agents
  3. Define file coordination map
  4. Set up enforcement rules
  5. Add adaptive model routing thresholds (Fast, Think, Deep, Strategic)

Phase 4: Adaptive Routing Setup

Read references/adaptive-routing.md.

Set up an adaptive routing section in root AGENTS.md:

  • Default to Fast
  • Escalation thresholds for Think, Deep, Strategic
  • De-escalation rule back to Fast after heavy reasoning
  • High-tier model rate-limit fallback behavior

Also create visual architecture doc:

  • docs/architecture/ADAPTIVE-ROUTING-LEARNING.md using assets/ADAPTIVE-ROUTING-LEARNING-TEMPLATE.md

Phase 5: Self-Improvement Setup

Read references/self-improvement.md for the complete system.

Each agent gets built-in self-improvement:

  • .learnings/ directory with proper templates
  • Detection triggers in SOUL.md (corrections, errors, gaps)
  • Promotion rules (learning → SOUL.md / AGENTS.md / TOOLS.md)
  • Cross-agent learning sharing via shared/learnings/CROSS-AGENT.md
  • Periodic review instructions
  • Weekly learning metrics file at memory/learning-metrics.json (use assets/LEARNING-METRICS-TEMPLATE.json)

Phase 6: Verification

After building everything:

  1. List all created files for the user
  2. Show the routing table
  3. Show the coordination map
  4. Confirm everything is in place

Phase 7: Expansion (On-Demand)

When the user asks to add, modify, or remove agents:

Adding an agent:

  1. Mini-discovery: What does this agent need to do?
  2. Create full agent structure (same as Phase 3)
  3. Update root AGENTS.md routing table
  4. Update coordination map

Modifying an agent:

  1. Read the current SOUL.md
  2. Apply changes while preserving personality consistency
  3. Update related coordination rules if needed

Removing an agent:

  1. Ask for confirmation
  2. Reassign the agent's responsibilities to other agents
  3. Update routing table and coordination map
  4. Move agent files to trash (never delete)

Key Principles

  1. Each agent is a character, not a template. Different personality, different voice, different strengths. If two agents sound the same, one shouldn't exist.
  1. No corporate language in any SOUL. See references/soul-philosophy.md. This is non-negotiable.
  1. Self-improvement is mandatory. Every agent logs mistakes and learns. See references/self-improvement.md.
  1. Coordination through files. Agents communicate via shared directories, not direct messaging. Each agent has clear read/write boundaries.
  1. Brevity in everything. SOULs, AGENTS files, templates. Respect the context window.
  1. The user's main assistant is the coordinator. It routes tasks, not the agents themselves.
  1. Language-adaptive. Write SOULs in whatever language the user works in. Arabic, English, bilingual, whatever fits their world.
  1. Adaptive routing by default. Every generated council should include Fast/Think/Deep/Strategic model routing thresholds.
  1. Metrics over vibes. Weekly learning review must be measured in memory/learning-metrics.json.
  1. Architecture must be visual. Generate a concise architecture doc at docs/architecture/ADAPTIVE-ROUTING-LEARNING.md for training and onboarding.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

77.6%
按下载量换算6,071

安全审计

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通过

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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