← 返回
开发者工具 中文

Skill Feed

Scenario-driven skill recommendation engine for ClawHub. Detects failed or stuck workflows, builds targeted search queries, and returns ranked skill recommen...
场景驱动的 ClawHub 技能推荐引擎,检测失败或卡住的工作流,构建精准搜索查询,返回排序的技能推荐。
jackleeio jackleeio 来源
开发者工具 clawhub v0.2.1 1 版本 99892.2 Key: 无需
★ 0
Stars
📥 927
下载
💾 47
安装
1
版本
#latest

概述

SkillFeed

Automatically match the best skills to unblock a failed or stuck workflow.

Trigger Conditions (auto)

Run this skill when any of these signals appears:

  1. Command/API failure (non-zero exit, HTTP 4xx/5xx)
  2. Retry threshold exceeded (default >=2 retries)
  3. Expected output missing (for example no tweet id after post task)
  4. Execution timeout exceeded
  5. User explicitly asks for tool/skill recommendation
  6. User describes a goal but has no clear implementation path

Do not trigger for normal delay/noise or when the user already has a working solution.

Workflow

  1. Capture failure context (sanitize before any external use)
    • task name
    • platform (X/Twitter, Telegram, GitHub, etc.)
    • error message/code (generic type only — see sanitization rules below)
    • latest action log summary (stripped of secrets)
  2. Classify failure type
    • auth/permission
    • rate limit/quota
    • network/timeout
    • invalid params/payload
    • unknown
  3. Build layered search queries (broad -> scenario -> failure)
    • Q1 broad capability query
    • Q2 scenario-specific query
    • Q3 failure-specific query with error tokens
    • Sanitize all queries before searching — see Data Sanitization section
  4. Search ClawHub
    • Use https://clawhub.ai/skills?focus=search
    • Always perform a live search; use references/top-skills-*.md only as offline fallback
    • Prefer sorting by stars / recently updated when comparing candidates
  5. Rank candidates
    • match to goal (highest weight)
    • match to failure type
    • setup cost and risk
    • maintenance signals
  6. Return recovery plan
    • Top 1 primary skill
    • 2 alternatives
    • 3-5 concrete next actions
    • fallback path if primary fails
  7. Anti-noise guardrails
    • Avoid repeating the same recommendation for the same error within a single conversation
    • Avoid auto-running high-risk external actions without user confirmation

Query Construction Rules

Generate queries from context tokens:

  • Goal tokens: post, schedule, auto reply, daily report
  • Platform tokens: x, twitter, tweet, telegram, github
  • Failure tokens: 401, 403, 429, timeout, invalid token, permission denied

Example for failed tweet post:

  • Q1: tweet automation
  • Q2: x twitter schedule post cron
  • Q3: twitter post failed 401 invalid token rate limit

Data Sanitization

All failure context MUST be sanitized before it is included in any external search query or output. This prevents accidental leakage of secrets, credentials, and private data.

Strip before searching

  • API keys, tokens, passwords, secrets (e.g. sk-..., ghp_..., Bearer ...)
  • Personally identifiable information (emails, usernames, IPs, hostnames)
  • Internal URLs, file paths containing usernames or org names
  • Request/response bodies and headers containing auth data
  • Environment variable values (keep only the variable name)

Keep in queries (safe tokens)

  • Generic error codes: 401, 403, 429, 500, timeout
  • Generic error types: invalid token, rate limit, permission denied
  • Platform names: twitter, github, telegram
  • Action verbs: post, publish, schedule, fetch

Rules

  1. Never embed raw log lines in a search query — extract only the error type/code.
  2. If unsure whether a token is sensitive, omit it.
  3. Queries should read like generic capability descriptions, not contain project-specific data.

Example — BAD query: twitter post failed Bearer sk-abc123 user@company.com 401

Example — GOOD query: twitter post failed 401 invalid token

Provider Adaptation (Claude Code / ChatGPT / Gemini)

Format the recovery plan for the current provider by default. Only include multi-provider runbooks when the user explicitly requests cross-platform output.

  1. Keep core logic provider-neutral
    • Use the same goal, failure classification, query generation, and ranking flow.
  2. Detect current provider and use the matching execution style
    • Claude Code: terminal-first, exact command sequences, minimal commentary.
    • ChatGPT: compact checklist bullets, short "why" before steps.
    • Gemini: explicit sections, assumptions, deterministic validation criteria.
  3. Normalize outputs
    • Keep identical recommendation order across providers.
    • Only vary phrasing and action formatting.

Output Format

  • Goal:
  • Failure signal:
  • Primary recommendation: (/slug) —
  • Alternatives:
  • (/slug) —
  • (/slug) —
  • Immediate actions (3-5 steps)
  • Success check:
  • expected output present
  • no critical error in latest run
  • Fallback if still failing
  • Execution notes:

References

  • Search and ranking recipes: references/discovery-workflow.md
  • Scenario keyword map: references/query-templates.md
  • Claude Code / ChatGPT / Gemini adaptation: references/provider-adaptation.md

版本历史

共 1 个版本

  • v0.2.1 当前
    2026-03-29 07:19 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

ai-agent

Self-Improving + Proactive Agent

ivangdavila
自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。
★ 1,385 📥 320,993
ai-agent

Skill Vetter

spclaudehome
AI智能体技能安全预审工具。安装ClawdHub、GitHub等来源技能前,检查风险信号、权限范围及可疑模式。
★ 1,232 📥 268,277
ai-agent

self-improving agent

pskoett
捕获经验教训、错误及修正内容,以实现持续改进。适用于以下场景:(1)命令或操作意外失败;(2)用户纠正Claude(如“不,那不对……”“实际上……”);(3)用户请求的功能不存在;(4)外部API或工具出现故障;(5)Claude发现自身
★ 4,086 📥 814,593