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Agent Pattern Mining

作者 cndbot · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install agent-pattern-mining
功能描述
Study another AI agent, coding CLI, or assistant codebase and extract transferable patterns into concrete local improvements. Use when asked to learn from Cl...
使用说明 (SKILL.md)

Agent Pattern Mining

Mine reusable agent patterns from real codebases and turn them into concrete upgrades instead of vague inspiration.

Workflow

  1. Scope the source repo and the upgrade target.

    • Clarify whether the goal is analysis only, a new skill, workflow changes, implementation work, or all of them.
    • Prefer targeted reading over exhaustive reading.
  2. Map the architecture quickly.

    • Inspect entrypoints, command/tool registries, memory/context code, planning/tasking, permissions, plugin/extensibility, remote/background execution, and observability.
    • Ignore vendor-specific infrastructure unless it carries a transferable pattern.
  3. Build a transfer matrix.

    • For each promising pattern, capture:
      • source feature
      • user benefit
      • portability/risk
      • nearest OpenClaw equivalent
      • concrete local action
  4. Classify findings.

    • Adopt now: can be captured as a skill, workspace rule, reference note, or small implementation.
    • Prototype later: good idea, but needs tool/runtime changes.
    • Do not copy: tightly vendor-locked, high-complexity, or low-value for the local setup.
  5. Apply improvements in this order.

    • Create or improve a skill when the pattern is reusable.
    • Update AGENTS.md for stable workflow changes.
    • Update TOOLS.md for environment-specific operational notes.
    • Update daily memory or MEMORY.md for durable lessons.
    • Propose config/runtime changes only when the user explicitly asks for them.
  6. Report the result concretely.

    • Summarize what the source system does well.
    • List what was adopted locally.
    • List what still requires deeper implementation work.
    • Name the exact files created or changed.

What to Look For

Prioritize patterns that reduce operator burden or improve agent reliability:

  • explicit planning modes
  • visible task tracking
  • context budget awareness
  • selective memory recall
  • permission boundaries and review gates
  • multi-agent orchestration
  • deferred tool discovery
  • plugin/skill hot reload
  • change/diff observability
  • session recovery and history search

Deprioritize patterns that are mostly infrastructure-specific:

  • first-party SaaS integrations
  • internal telemetry wiring
  • organization-specific policy systems
  • vendor-only remote services without a local analogue

Output Shape

Use this structure when delivering results:

  1. Architecture sweep — the subsystems reviewed
  2. Best transferable ideas — ranked by impact
  3. Concrete local upgrades — skill/doc/workflow changes already made
  4. Gaps left open — good ideas not yet implemented
  5. Files changed — exact paths

References

  • Read references/claude-code-patterns.md when the source is Claude Code or when the user wants proven patterns from agentic coding CLIs.

Guardrails

  • Do not claim an upgrade happened unless a file, workflow, or configuration actually changed.
  • Prefer lean skills: keep SKILL.md procedural and move detailed analysis to references/.
  • When the repo is large, read representative files first, then deepen only where the pattern is promising.
  • If the user asks for self-upgrade, translate findings into persistent artifacts, not just commentary.
安全使用建议
Install only if you want ClawHub/Convex maintainer workflows. Review the moderation and autoreview instructions before use, especially commands that can ban users, send staff email, transfer packages, run migrations, or launch nested review with full access.
能力标签
crypto
能力评估
Purpose & Capability
The artifacts describe ClawHub staff moderation, PR maintenance, autoreview, and Convex development workflows; the powerful capabilities, such as admin moderation commands, GitHub operations, migrations, auth setup, and code review helpers, match those stated purposes.
Instruction Scope
The instructions generally require explicit targets, reasons, confirmation, verification, and audit-aware workflows for high-impact actions; no prompt-injection, role override, or concealed instruction pattern was found.
Install Mechanism
The skill artifacts are text instructions, UI metadata, SVG icons, references, and one explicitly invoked helper script; no skill-level install hook, auto-start behavior, or hidden package execution path was identified.
Credentials
The skills expect repo-local tools such as bun, gh, npx, Convex, Codex, and ClawHub admin CLI, which is proportionate for the stated developer and staff workflows; the autoreview helper may send diffs to configured fallback reviewers if Codex is unavailable, which is disclosed.
Persistence & Privilege
No background persistence or credential harvesting was found; sensitive authority depends on existing authenticated tools, and the autoreview helper's default full-access nested Codex mode is explicitly documented with an opt-out.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agent-pattern-mining
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agent-pattern-mining 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release of agent-pattern-mining. - Enables mining reusable patterns from agent and assistant codebases to drive concrete local improvements. - Outlines a workflow for analyzing, mapping, and transferring features into actionable upgrades. - Specifies prioritized patterns (e.g., planning modes, context awareness, permission boundaries) and deprioritized patterns (e.g., vendor-specific integrations). - Defines a consistent output structure for reporting findings and changes. - Includes guardrails to ensure actionable, reliable improvements and lean documentation practices.
元数据
Slug agent-pattern-mining
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Agent Pattern Mining 是什么?

Study another AI agent, coding CLI, or assistant codebase and extract transferable patterns into concrete local improvements. Use when asked to learn from Cl... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 10 次。

如何安装 Agent Pattern Mining?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install agent-pattern-mining」即可一键安装,无需额外配置。

Agent Pattern Mining 是免费的吗?

是的,Agent Pattern Mining 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Agent Pattern Mining 支持哪些平台?

Agent Pattern Mining 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Agent Pattern Mining?

由 cndbot(@cndbot)开发并维护,当前版本 v1.0.0。

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