A specialized formatting skill that restructures AI agent responses into concise, mobile-friendly layouts optimized for the QQ chat interface.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install qqbot-message-format
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install qqbot-message-format using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is QQBot Message Format?
The qqbot-message-format skill is designed to bridge the gap between technical AI outputs and the unique rendering constraints of the QQ messaging platform. Instead of delivering dense, document-like Markdown, this skill re-formats content into a lightweight project-update style that is easy to scan on both mobile and desktop devices. It prioritizes clarity by placing conclusions first and flattening complex nested structures.
By utilizing this skill within Openclaw Skills, developers can ensure their AI bots communicate like humans rather than machines. It manages heading hierarchies, restructures lists for better visibility, and applies functional emojis to highlight status updates, troubleshooting steps, or task completions without creating visual noise.
QQBot Message Format Use Cases
- Summarizing long technical task results into a quick conclusion for QQ group members.
- Formatting system troubleshooting diagnostics into actionable steps (Conclusion > Root Cause > Advice).
- Providing lightweight status updates and project milestones in a chat-friendly 'project report' feel.
- Converting raw tool outputs into human-readable Chinese messages that fit on a single mobile screen.
How QQBot Message Format Works
- The skill analyzes the message type to determine if it is a short answer, a completion update, or troubleshooting documentation.
- It moves the most critical conclusion or result to the very top of the message.
- Nested Markdown lists are flattened and rewritten using indent cues like functional arrows to ensure visibility in the QQ UI.
- Headings are simplified into a two-tier hierarchy (Large vs. Small) to match QQ's actual rendering perception.
- Bold text and functional emojis are strategically applied only to the most important keywords or status indicators.
- The final output is scrubbed of empty blocks and low-value content before being sent to the user.
QQBot Message Format Setup
To integrate this formatting logic into your agent environment using Openclaw Skills, ensure your skill directory includes the necessary reference templates:
# Create the references directory if it doesn't exist
mkdir -p skills/qqbot-message-format/references
# Ensure template and example files are present
touch skills/qqbot-message-format/references/templates.md
touch skills/qqbot-message-format/references/examples.md
QQBot Message Format Data Schema & Taxonomy
The skill organizes its formatting logic and style guides through a simple file-based taxonomy:
| Component | File Path | Description |
|---|---|---|
| Structural Templates | references/templates.md |
Contains reusable layouts for different message types (Conclusion, Update, etc.). |
| Style Examples | references/examples.md |
Provides 'Good vs Bad' comparison cases for the LLM to follow. |
| Emoji Map | Internal Logic | Standardized set of functional icons (✅, ⚠️, ❌, 📌, 🔍, 👉). |
QQBot Message Format Advanced Features
- Two-Tier Heading Logic: Automatically maps Markdown levels H1-H6 into two perceptual tiers optimized for the QQ mobile app.
- Nested Structure Flattening: Automatically converts deep Markdown nesting into a 'flat' list with indentation characters to prevent rendering issues.
- Pre-send Validation: Includes a checklist to ensure the 'First Screen' rule—making sure the answer is visible without scrolling.
- Functional Emoji Injection: Smartly places emojis based on content context (e.g., using 🔍 for diagnostics or ✅ for task success).
SKILL.md
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