skill-activator（技能激活器）

Skill 激活器

"你的 Skill 该醒了。"

Core Workflow

Three-layer process: Discover → Match → Fuse.

Layer 1: Discover Needs (需求发现)

Choose the appropriate discovery mode based on context:

Mode A — Environment Scan (数字生活体检)

1. Run bash scripts/scan_environment.sh to collect:

- Installed skills list with descriptions - User identity from SOUL.md / USER.md / IDENTITY.md - Connected channels (feishu, wecom, telegram, etc.) - Workspace state (memory files, heartbeat config)

1. Analyze scan results to identify:

- 🟢 Skills actively in use - 🟡 Skills installed but underutilized (have the skill, not using it) - 🔴 Capability gaps (needs not covered by any installed skill)

1. Generate personalized activation report with specific, actionable recommendations

Mode B — Role-Based Recommendations (角色推荐)

1. Identify user's role from SOUL.md or conversation
2. Read references/role-templates.md for role-specific pain points and automation ideas
3. Cross-reference with installed skills
4. Present top 3-5 recommendations ranked by: pain severity × ease of implementation

Mode C — Pain Point Interview (痛点挖掘对话)

1. Ask user to walk through their daily workflow
2. Listen for time-wasting patterns, repetitive tasks, context-switching pain
3. Quantify: "You spend ~X minutes/day on Y — automatable?"
4. Map each pain point to a concrete Skill combination
5. Prioritize by time saved per week

Layer 2: Match Skills (智能匹配)

After discovering needs:

1. Check installed skills that match the need
2. Search ClawHub for additional skills: clawhub search "<need description>"
3. Identify gaps — needs no existing skill covers
4. Present matching plan:

- ✅ Already have: installed skills that apply - 📦 Recommend install: available on ClawHub via clawhub install <name> - 🔧 Can fuse: combine existing skills to cover this need - 🆕 Need to create: no skill exists, suggest building one

Layer 3: Fuse & Generate (融合生成)

When combining skills into a new workflow:

1. Read references/fusion-guide.md for fusion patterns and templates
2. Read each source skill's SKILL.md to understand inputs, outputs, dependencies
3. Design the pipeline (sequential, fan-out, aggregation, or enhancement)
4. Generate new fused SKILL.md with proper frontmatter, workflow steps, and error handling
5. Add any glue scripts to scripts/ if data format conversion is needed
6. Package with package_skill.py for distribution

Output Format

Activation Report

🔍 Skill 激活报告
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

👤 身份：{role from SOUL.md}
🔌 已连接：{channels}
📦 已安装：{N} 个 Skill

💡 发现 {N} 个自动化机会：

1️⃣ {pain point description}
   ⏱ 预计每周节省：{X} 分钟
   🧩 需要：{Skill A} + {Skill B}
   📊 可行度：⭐⭐⭐⭐⭐
   → [一键激活]

2️⃣ {pain point description}
   ...

🟡 沉睡的 Skill（装了没用）：
   - {skill name}: 它能帮你 {what it does}

📦 推荐安装：
   - {skill name}: 解决 {what problem}
   - 安装命令：clawhub install {name}

Fused Skill Output

When generating a fused skill, output:

1. The complete SKILL.md content
2. Any required glue scripts
3. Installation/usage instructions
4. Packaging command: package_skill.py <path>

Key Principles

• Be specific, not generic: "你每周花2小时做周报" > "你可以自动化一些任务"
• Quantify savings: Always estimate time/effort saved
• One-click actionable: Every recommendation should be immediately executable
• Respect what's installed: Prioritize solutions using skills the user already has
• Progressive: Start with quick wins, then suggest more advanced automations