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Study And Port

该 Skill 用于在发现新的 AI 框架或工具时开展研究:先通过网络检索了解其功能与设计理念,深入分析核心特性,逐项评估能否以 SKILL.md 或脚本形式移植,并在获得用户确认后创建新 skill、校验文件,再把研究结论写入本地记忆日志并向用户汇报。

chenjin113
chenjin113
热度 46.93
最近更新 2026-05-11 17:44:36
分类:开发依赖:读写文件 / 第三方 API / 代码执行应用场景:研究 / 代码生成 / 笔记整理

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add study-and-port
Metadata
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:

    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

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.


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