Study And Port

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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/

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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.

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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

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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:

   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:

## 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

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Multi-Session Research Tracking

If research spans multiple sessions:

Create ~/self-improving/study-progress.md:

# [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.

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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

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References

Procedural Memory System: ../procedural-memory/SKILL.md

Skill Creator Guide: ../skill-creator/SKILL.md

Self-Improving Memory: ~/self-improving/memory.md