Tencent SkillHub · AI

skill-router

Context-based skill auto-routing + federated skill composition. Analyzes user input to auto-select single or multiple skills and execute in order. First gate...

skill openclawclawhub Free

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High Signal

Context-based skill auto-routing + federated skill composition. Analyzes user input to auto-select single or multiple skills and execute in order. First gate...

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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.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, chains.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.

Open Send to Agent page Open JSON manifest Open Markdown brief

Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: mupengi-bot
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 13 sections Open source page

Skill Router

Meta system that analyzes natural language input to auto-select appropriate skill(s), determine order, and chain execution.

Execution Flow

1. Scan only skills/*/SKILL.md frontmatter (trigger matching) - Quick match with description + trigger fields - No full body reading → 83% token savings 2. Check run field of matched skill for script path - run: "./run.sh" → skills/{name}/run.sh - run: "./run.js" → skills/{name}/run.js 3. Direct script execution with exec WORKSPACE=$HOME/.openclaw/workspace \ EVENTS_DIR=$WORKSPACE/events \ MEMORY_DIR=$WORKSPACE/memory \ bash $WORKSPACE/skills/{name}/run.sh [args] 4. Agent processes stdout result - Parse if JSON - Pass through if text - Check stderr on error 5. Generate events based on events_out - Create events/{type}-{date}.json file - Subsequent skills consume via events_in 6. Check hooks → trigger subsequent skills - post: ["skill-a", "skill-b"] → auto-execute - on_error: ["notification-hub"] → notify on error

Skill Metadata Scan

# Extract only frontmatter from all skills for skill in skills/*/SKILL.md; do yq eval '.name, .description, .trigger, .run' "$skill" done

Execution Example

# User: "daily report" # → trigger match: daily-report # → Execute: cd $HOME/.openclaw/workspace WORKSPACE=$PWD \ EVENTS_DIR=$PWD/events \ MEMORY_DIR=$PWD/memory \ bash skills/daily-report/run.sh today # Agent formats stdout result and delivers to user

Token Savings Effect

Before: SKILL.md 3000 chars × 40 = 120KB (~30K tokens) v2: SKILL.md 500 chars × 40 = 20KB (~5K tokens) Savings: 83% token reduction

Core Concept

OpenClaw already selects 1 skill via description matching, but this skill: Detect complex intent: "Analyze competitors and make card news" → competitor-watch + copywriting + cardnews + insta-post Context-based auto-hooks: Auto-determine subsequent skills when a skill executes Skill chain templates: Pre-define frequently used combinations

Single Skill Mapping (1:1)

"commit/push/git" → git-auto "DM/instagram message" → auto-reply "cost/tokens/how much" → tokenmeter "translate/to English" → translate "invoice/quote" → invoice-gen "code review/PR" → code-review "system status/health" → health-monitor "trends" → trend-radar "performance/reactions/likes" → performance-tracker "daily report" → daily-report "SEO audit" → seo-audit "brand tone" → brand-voice

Complex Skill Chains (1:N) — Core Pipelines

Trigger PatternSkill ChainDescription"create content/post"seo-content-planner → copywriting → cardnews → insta-postFull content pipeline"analyze competitors and report"competitor-watch → daily-report → mailResearch→report"summarize this video as card news"yt-digest → content-recycler → cardnews → insta-postVideo→content conversion"weekly review"self-eval + tokenmeter + performance-tracker → daily-reportComprehensive review"recycle content"performance-tracker → content-recycler → cardnewsRepackage successful content"review idea and execute"think-tank(brainstorm) → decision-log → skill-composerIdeation→decision→execution"market research"competitor-watch + trend-radar + data-scraper → daily-reportFull research"release"code-review → git-auto → release-disciplineSafe deployment"morning routine"health-monitor → tokenmeter → notification-hub → daily-reportMorning auto-check

Context-based Auto-chain Rules

Skill A execution complete → analyze results → auto-determine next skill: Auto-chain Rules (if → then) IF competitor-watch detects important change → THEN notification-hub(urgent) + include in daily-report IF tokenmeter exceeds $500/month → THEN notification-hub(urgent) IF code-review detects HIGH severity → THEN block commit + notification-hub IF think-tank conclusion has "immediate execution" action → THEN auto-record in decision-log IF cardnews generation complete → THEN confirm "post with insta-post?" (approval required) IF self-eval detects repeated mistake → THEN trigger learning-engine IF performance-tracker finds successful content → THEN suggest content-recycler IF trend-radar detects hot trend → THEN auto-suggest seo-content-planner IF mail detects important email → THEN notification-hub(important) IF health-monitor detects anomaly → THEN attempt auto-recovery + notification-hub(urgent)

Execution Engine Protocol

1. Receive user input 2. Classify intent (single vs complex) 3. If single → execute skill immediately 4. If complex → compose skill chain a. Skills without dependencies execute in parallel (sessions_spawn) b. Skills with dependencies execute sequentially (pass previous results via events/) 5. Check auto-chain rules on each skill completion 6. Auto-trigger additional skills if needed (or request approval) 7. Synthesize final results and respond

Auto-hook Registration

When skill-router activates, for all skills: pre-hook: Input validation + security check post-hook: Generate events/ event + check chain rules on-error: Error log + notification-hub

Skill Dependency Graph

[User Input] ↓ [skill-router] ← Intent classification ↓ ┌─────────────────────────────────────────┐ │ TIER 1: Data Collection │ │ competitor-watch, data-scraper, │ │ trend-radar, tokenmeter, yt-digest │ └─────────────┬───────────────────────────┘ ↓ events/ ┌─────────────────────────────────────────┐ │ TIER 2: Analysis/Thinking │ │ think-tank, self-eval, seo-audit, │ │ code-review, performance-tracker │ └─────────────┬───────────────────────────┘ ↓ events/ ┌─────────────────────────────────────────┐ │ TIER 3: Production │ │ copywriting, cardnews, content-recycler,│ │ translate, invoice-gen │ └─────────────┬───────────────────────────┘ ↓ events/ ┌─────────────────────────────────────────┐ │ TIER 4: Deployment/Execution │ │ insta-post, mail, git-auto, │ │ release-discipline │ └─────────────┬───────────────────────────┘ ↓ events/ ┌─────────────────────────────────────────┐ │ TIER 5: Tracking/Learning │ │ daily-report, decision-log, │ │ learning-engine, notification-hub │ └─────────────────────────────────────────┘

Safety Mechanisms

Always require approval before external actions (email send, SNS post, payment) Prevent infinite loops: Stop after same skill chain repeats 3 times Cost limit: Max 5 subagents per chain Graceful stop on error + save partial results 🐧 Built by 무펭이 — Mupengism ecosystem skill

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

2 Docs

SKILL.md Primary doc
chains.md Docs