Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Council Builder
Build a personalized team of AI agent personas for OpenClaw. Interviews the user, analyzes their workflow, then creates specialized agents with distinct pers...
skill openclawclawhub Free
Build a personalized team of AI agent personas for OpenClaw. Interviews the user, analyzes their workflow, then creates specialized agents with distinct pers...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, assets/ADAPTIVE-ROUTING-LEARNING-TEMPLATE.md, assets/AGENT-AGENTS-TEMPLATE.md, assets/LEARNING-METRICS-TEMPLATE.json, assets/LEARNINGS-TEMPLATE.md, assets/ROOT-AGENTS-TEMPLATE.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.1.1
Provenance
- Publisher
- Abdullah4AI
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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.
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: Determine agent count: 3-7 agents. Fewer is better. Each agent must earn its existence. Define each agent: Name, role, specialties, personality angle Map coordination: Which agents feed data to which Present the plan to the user in a clear table: | Agent | Role | Specialties | Personality | |-------|------|-------------|-------------| | [Name] | [One-line role] | [Key areas] | [Personality angle] | 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]/ βββ SOUL.md # Personality, role, rules (see soul-philosophy.md) βββ AGENTS.md # Agent-specific coordination rules βββ memory/ # Agent's memory directory βββ .learnings/ # Self-improvement logs β βββ LEARNINGS.md β βββ ERRORS.md β βββ FEATURE_REQUESTS.md βββ [workspace dirs] # Role-specific output directories For each agent's SOUL.md: Read references/soul-philosophy.md for the writing guide Read assets/SOUL-TEMPLATE.md for the structure Customize deeply for this agent's role and personality Every SOUL must be unique. No copy-paste between agents. For each agent's AGENTS.md: Use assets/AGENT-AGENTS-TEMPLATE.md as base Define what this agent reads from and writes to Define handoff rules with other agents For .learnings/ files: Copy structure from assets/LEARNINGS-TEMPLATE.md Initialize empty log files For the root AGENTS.md: Use assets/ROOT-AGENTS-TEMPLATE.md as base Create the routing table for all agents Define file coordination map Set up enforcement rules 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 β 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: List all created files for the user Show the routing table Show the coordination map Confirm everything is in place
Phase 7: Expansion (On-Demand)
When the user asks to add, modify, or remove agents: Adding an agent: Mini-discovery: What does this agent need to do? Create full agent structure (same as Phase 3) Update root AGENTS.md routing table Update coordination map Modifying an agent: Read the current SOUL.md Apply changes while preserving personality consistency Update related coordination rules if needed Removing an agent: Ask for confirmation Reassign the agent's responsibilities to other agents Update routing table and coordination map Move agent files to trash (never delete)
Key Principles
Each agent is a character, not a template. Different personality, different voice, different strengths. If two agents sound the same, one shouldn't exist. No corporate language in any SOUL. See references/soul-philosophy.md. This is non-negotiable. Self-improvement is mandatory. Every agent logs mistakes and learns. See references/self-improvement.md. Coordination through files. Agents communicate via shared directories, not direct messaging. Each agent has clear read/write boundaries. Brevity in everything. SOULs, AGENTS files, templates. Respect the context window. The user's main assistant is the coordinator. It routes tasks, not the agents themselves. Language-adaptive. Write SOULs in whatever language the user works in. Arabic, English, bilingual, whatever fits their world. Adaptive routing by default. Every generated council should include Fast/Think/Deep/Strategic model routing thresholds. Metrics over vibes. Weekly learning review must be measured in memory/learning-metrics.json. Architecture must be visual. Generate a concise architecture doc at docs/architecture/ADAPTIVE-ROUTING-LEARNING.md for training and onboarding.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs1 Config
- SKILL.md
- assets/ADAPTIVE-ROUTING-LEARNING-TEMPLATE.md
- assets/AGENT-AGENTS-TEMPLATE.md
- assets/LEARNINGS-TEMPLATE.md
- assets/ROOT-AGENTS-TEMPLATE.md
- assets/LEARNING-METRICS-TEMPLATE.json