Skill: chenjin113/study-and-port (version 1.2.0)

--- name: study-and-port description: "Research new AI frameworks or technologies, extract their best features, evaluate feasibility, and implement as OpenClaw skills. Triggered when: (1) user mentions a new framework, (2) discovers features other frameworks have that OpenClaw lacks, (3) wants to learn from other AI assistants." metadata: requires: bins: [node] permissions: - write: ~/.openclaw/workspace/skills/ - write: ~/self-improving/ --- ## Safety & Boundaries - **Always ask user before creating new skills or scripts** - **Never auto-execute created scripts without confirmation** - **Research only — do NOT implement features that require core OpenClaw changes** - **Report findings to user, let them decide what to keep** # Study and Port — Research &移植框架优点 > _"Don't just use other AI frameworks — learn from them."_ When you discover an AI framework or tool with features worth learning, execute this skill. --- ## Trigger Conditions - User mentions a new AI framework or tool - Discover features in other frameworks that OpenClaw doesn't have - Want to learn from other AI assistants' strengths --- ## Workflow ### Step 1 — Quick Overview (10 min) **Search endpoint:** Use MiniMax web search (configured in TOOLS.md) Use search to understand: **Template A (General Framework):** ``` "[framework name] features capabilities 2026" "[framework name] vs OpenAI agent differences" "[framework name] Hermes Agent comparison" ``` **Template B (AI Coding Assistants):** ``` "[framework name] features capabilities 2026" "[framework name] self-improving memory workflow" ``` **Template C (Chinese Frameworks):** ``` "[framework name] 特点 功能 优势" "[framework name] 和 OpenClaw 对比" ``` Collect: - Framework name and version - Core features (3-5) - Design philosophy - How it differs from OpenClaw ### Step 2 — Deep Dive (as needed) For each core feature: ``` "[framework name]" "[specific feature]" "how it works" "[framework name]" "implementation" "architecture" ``` Extract the 3-5 most valuable learnings. ### Step 3 — Feasibility Evaluation For each feature, answer: | Question | ✅ Yes | ❌ No | |----------|--------|-------| | Can this be implemented as SKILL.md? | Mark "portable" | Mark "requires core change" | | Does it need OpenClaw source changes? | Mark "requires core" | Mark "portable" | | Does it need external APIs? | Check API availability | Mark "API dependent" | | Would users benefit from this? | Keep | Mark "low value" | **Portability Rating:** - 🌟 **Portable (skill)** — implementable as SKILL.md ± scripts - 🔧 **Partially Portable** — core part doable, limitations exist - 🏗️ **Requires Core Change** — needs OpenClaw source modification - ⏳ **Wait & Watch** — tech not mature enough ### Step 4 — Create Skill (if portable) **User confirmation required:** Before creating any new skill or script, ALWAYS ask user: - "这个框架的 [功能] 值得移植，我要创建新 skill，可以吗？" For each "portable" feature: 1. **Write SKILL.md:** - Clear trigger conditions - Detailed execution steps - Usage boundaries - Script location if needed 2. **Write Scripts (if needed):** - Scripts must be independently runnable - Must handle errors (network failure, file not found, etc.) - Node.js runtime must be available (declare in metadata) 3. **Validate:** ```bash ls ~/.openclaw/workspace/skills/[skill-name]/ node ~/.openclaw/workspace/skills/[skill-name]/scripts/[script].js --help ``` **Permissions required:** - Write access to `~/.openclaw/workspace/skills/` (for new skill files) - Write access to `~/self-improving/` (for log files) - Node.js runtime (pre-installed with OpenClaw) ### Step 5 — Log to Procedural Memory Append to `~/self-improving/procedural-memory-log.md`: ```markdown ## YYYY-MM-DD ### [Framework] Research - **Research subject**: [framework name] - **Features extracted**: [list portable features] - **Deemed non-portable**: [list with reasons] - **Skill created**: [skill-name] - **Portability rating**: 🌟 Fully portable / 🔧 Partially / 🏗️ Core required ``` ### Step 6 — Report to User Tell user: - What interesting features were found - Which are portable, which aren't - What skill was created --- ## Multi-Session Research Tracking If research spans multiple sessions: Create `~/self-improving/study-progress.md`: ```markdown # [Framework] Research Progress ## Started: YYYY-MM-DD ## Status: 🔄 In Progress / ✅ Complete ## Completed - [ ] Quick overview - [ ] Core feature analysis ## Pending - [ ] Feasibility evaluation - [ ] Skill creation ## Key Findings (update anytime) - ``` Read this file when resuming research. --- ## Decision Tree: Should I Port? ``` Discover new framework │ ▼ Does OpenClaw already have this feature? │ ├─ Yes → Skip, not worth researching │ └─ No or Not Sure → Continue │ ▼ Would users benefit from this? │ ├─ Not sure → Ask user: "Should I research [framework]?" │ └─ Yes → Continue │ ▼ Can it be implemented as a skill? │ ├─ Yes → Create skill │ ├─ Partial → Create core part, note limitations │ └─ No (requires core change) → Log to future features ``` --- ## References - Procedural Memory System: `../procedural-memory/SKILL.md` - Skill Creator Guide: `../skill-creator/SKILL.md` - Self-Improving Memory: `~/self-improving/memory.md`


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)