Study And Port

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.

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):

text

"[framework name] features capabilities 2026"
"[framework name] vs OpenAI agent differences"
"[framework name] Hermes Agent comparison"

Template B (AI Coding Assistants):

text

"[framework name] features capabilities 2026"
"[framework name] self-improving memory workflow"

Template C (Chinese Frameworks):

text

"[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:

text

"[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.

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Decision Tree: Should I Port?

text

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