iknowkungfu 🥋
Skill discovery in 3 phases:
- 1. Profile 🔍 — Analyze your workflow (memory, skills, crons, logs)
- Match 🎯 — Cross-reference against curated ClawHub index
- Recommend 📋 — Prioritized suggestions with trust scores
100% local. No data leaves your machine.
Commands
INLINECODE0 full scan | /kungfu-scan profile only | /kungfu-gaps uncovered areas | /kungfu-update refresh index
Phase 1: Profile
See references/workflow-analysis.md for full procedure.
Read these sources to build a Workflow Profile:
- - MEMORY.md + daily logs — recurring topics, tools, domains
- Installed skills — list from BOTH
~/.openclaw/skills/ AND system paths (e.g. /opt/homebrew/lib/node_modules/openclaw/skills/). Check ALL install locations. Map to categories via INLINECODE7 - AGENTS.md + config — user role, tool preferences, model budget signals
- HEARTBEAT.md + crons — automated/scheduled responsibilities
- Recent logs (7 days) — dominant task types, frequent commands
Quick security check while reading skills: scan for base64, curl/wget, eval/exec, env var harvesting. Flag warnings. For deep scanning, recommend ClawSpa.
Output the Workflow Profile (template in references/workflow-analysis.md).
Phase 2: Match
See references/recommendation-engine.md for full procedure.
Load data/skills-catalogue.json. For each gap in the profile:
- 1. Find matching skills by category
- Score candidates (see
references/scoring.md) - Filter already-installed skills (check ALL install paths: user, system, workspace)
- Filter skills whose functionality is already covered by existing config (e.g. memoryFlush covers session wrap-up, gog covers Gmail)
- Rank by score, deduplicate overlaps
Phase 3: Validate Before Recommending
Before presenting, run each candidate through a relevance check:
- - Does the user actually use this tool/service? (e.g. don't recommend Slack if they never mention it)
- Is equivalent functionality already covered by a system skill, config setting, or existing workflow?
- Would this realistically fit the user's setup? (solo builder vs team, macOS vs Linux, budget signals)
Drop candidates that fail. Better 2 genuinely useful than 5 with 3 irrelevant. If all fail: "gap detected but no relevant match for your setup."
Phase 4: Recommend
Present top 5:
CODEBLOCK0
Trust Scoring
See references/scoring.md. Factors: downloads (25%), stars (20%), author rep (15%), recency (15%), permissions (15%), security (10%). Never recommend: <50 downloads, VirusTotal flags, no author, excessive unjustified permissions.
Safeguards
- - READ-ONLY. Never installs, modifies, or removes anything. Zero network calls.
- Only recommends skills passing trust AND relevance thresholds.
- Honest about confidence. If no good match exists, says so.
- NEVER include full file contents in output. Only summarize patterns and categories.
- NEVER print API keys, tokens, passwords, SSH keys, or any credential-like strings found in any file.
- When reporting security flags, describe the PATTERN found (e.g. "env var reference in script"), never quote the actual value.
- Redact any file paths that contain usernames or home directories in output.
Limitations
Catalogue is bundled (may lag). Trust scores are heuristic. <7 days history = less accurate.
iknowkungfu 🥋
技能发现分为三个阶段:
- 1. 画像 🔍 — 分析你的工作流程(记忆、技能、定时任务、日志)
- 匹配 🎯 — 与精心策划的 ClawHub 索引进行交叉比对
- 推荐 📋 — 带有信任评分的优先级建议
100% 本地运行。数据不会离开你的设备。
命令
/kungfu 完整扫描 | /kungfu-scan 仅画像 | /kungfu-gaps 未覆盖领域 | /kungfu-update 刷新索引
第一阶段:画像
完整流程请参见 references/workflow-analysis.md。
通过读取以下来源构建工作流程画像:
- - MEMORY.md + 日常日志 — 重复出现的主题、工具、领域
- 已安装技能 — 同时来自 ~/.openclaw/skills/ 和系统路径(例如 /opt/homebrew/lib/node_modules/openclaw/skills/)的列表。检查所有安装位置。通过 data/workflow-patterns.json 映射到类别
- AGENTS.md + 配置 — 用户角色、工具偏好、模型预算信号
- HEARTBEAT.md + 定时任务 — 自动化/定期职责
- 近期日志(7天) — 主要任务类型、常用命令
读取技能时的快速安全检查:扫描 base64、curl/wget、eval/exec、环境变量收集。标记警告。如需深度扫描,推荐 ClawSpa。
输出工作流程画像(模板见 references/workflow-analysis.md)。
第二阶段:匹配
完整流程请参见 references/recommendation-engine.md。
加载 data/skills-catalogue.json。针对画像中的每个缺口:
- 1. 按类别查找匹配技能
- 对候选技能评分(参见 references/scoring.md)
- 过滤已安装技能(检查所有安装路径:用户、系统、工作区)
- 过滤功能已被现有配置覆盖的技能(例如 memoryFlush 覆盖会话总结,gog 覆盖 Gmail)
- 按评分排序,去重
第三阶段:推荐前验证
在呈现之前,对每个候选技能进行相关性检查:
- - 用户是否实际使用该工具/服务?(例如,如果用户从未提及 Slack,则不推荐)
- 等效功能是否已被系统技能、配置设置或现有工作流程覆盖?
- 该技能是否实际适合用户的设置?(独立开发者 vs 团队,macOS vs Linux,预算信号)
淘汰不合格的候选技能。2个真正有用的技能胜过5个其中3个无关的技能。如果全部不合格:检测到缺口,但未找到适合你设置的相关匹配。
第四阶段:推荐
呈现前5名:
🥋 我懂功夫 — 推荐
═══════════════════════════════════════
- 1. 🟢 技能名称 (★ 4.5)
类别:[类别] | 作者:[作者]
原因:[1-2句与你的工作流程相关的说明]
安装:clawhub install 技能名称
─────────────────────────────────
[最多5个]
═══════════════════════════════════════
💡 /kungfu-gaps 查看所有未覆盖领域
═══════════════════════════════════════
信任评分
参见 references/scoring.md。评分因素:下载量(25%)、星标数(20%)、作者声誉(15%)、时效性(15%)、权限(15%)、安全性(10%)。绝不推荐:下载量<50、VirusTotal 标记、无作者、过多不合理权限。
安全措施
- - 只读。绝不安装、修改或删除任何内容。零网络调用。
- 仅推荐通过信任和相关性阈值的技能。
- 诚实地表达置信度。如果没有好的匹配,如实说明。
- 输出中绝不包含完整的文件内容。仅总结模式和类别。
- 绝不打印在任何文件中发现的 API 密钥、令牌、密码、SSH 密钥或任何类似凭据的字符串。
- 报告安全标记时,描述发现的模式(例如脚本中的环境变量引用),绝不引用实际值。
- 在输出中隐去任何包含用户名或主目录的文件路径。
局限性
目录是捆绑的(可能滞后)。信任评分基于启发式。少于7天的历史记录=准确度较低。