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Agent Skill · 研究检索

skill-combinator(技能组合器)

skill-combinator 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

skill-combinator 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 10,560。

总安装 12,419周安装 528GitHub Stars 1下载 4,351核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.2

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-combinator(技能组合器)
来源仓库:https://github.com/georges91560/skill-combinator
安装命令:
openclaw skills install skill-combinator
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install skill-combinator

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

skill-combinator 用于智能组合代理已安装的技能,以应对复杂任务场景。适合在 OpenClaw 中需要动态调用多个技能解决综合性问题时使用。通过非显式方式自动触发技能协同,无需手动指定执行顺序。安装前需确认是否涉及敏感权限或网络访问,建议检查技能依赖关系。

注意该技能可能消耗较多令牌,适用于紧急功能解锁场景。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install skill-combinator;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skill-combinator 怎么安装?

在终端执行 openclaw skills install skill-combinator。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-combinator 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

skill-combinator 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

skill-combinator 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

skill-combinator 用的人多吗?

公开数据是累计安装 12,419 次,近一周安装 528 次,下载 4,351 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

skill-combinator 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
skill-combinator

技能组合器 · 本页

12,4191需要联网MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
skill-combinator
description
>
version
1.0.1
author
georges91560
license
MIT
homepage
https://github.com/georges91560/skill-combinator
metadata
openclaw
emoji
🧬
primaryEnv
TELEGRAM_BOT_TOKEN
requires
env
paths
read
write
network_behavior
makes_requests
false
uses_agent_telegram
true
telegram_note
>

Skill Combinator — Emergent Capabilities Engine

The Core Idea

Your agent has a library of skills. Each skill alone does one thing well. But when 2 or more skills are combined on the same mission, emergent capabilities appear — abilities that no single skill could produce alone.

Think of how a human expert works: a trader who also understands geopolitics and social media can detect market moves before they happen. None of those 3 domains alone gives that edge. The combination does.

This skill teaches your agent to think that way.

Illustrative examples of emergent capabilities:

Skill ASkill BWhat Emerges
Trading executorPrediction marketsCross-market hedge: trade an asset AND bet on its price direction simultaneously
Market analyzerGeopolitics skillEvent anticipation: detect political signals before markets price them in
Price monitorSocial media skillSentiment trading: spot volume spikes before they move the chart
Self-improving-agentAny skillMeta-learning: any skill becomes self-optimizing over time
News aggregatorTrading executorMacro-driven entries: open positions when news catalysts are detected
Email/inbox skillCRM skillRelationship intelligence: detect deal signals from communication patterns

*These are illustrative patterns. Your agent will discover its own based on the specific skills it has installed.*


Two Modes of Operation

Mode 1 — Mission Activation (triggered on any complex task)

When your agent receives a mission, BEFORE planning execution:

  1. Inventory installed skills (names and descriptions only — not full file contents)
  2. Detect which combinations are relevant to this mission
  3. Check COMBINATIONS.md for known proven patterns
  4. Propose a multi-skill plan to operator if macro-level action is involved
  5. Execute within approved scope and observe the result
  6. Log the outcome to .learnings/LEARNINGS.md (metadata only — no secrets)

Mode 2 — Weekly Distillation (cron job, every Sunday)

Once per week, your agent:

  1. Reviews .learnings/LEARNINGS.md for emergent_capability entries
  2. Identifies proven combinations (3+ successful uses)
  3. Promotes them to COMBINATIONS.md
  4. Scans FEATURE_REQUESTS.md for recurring skill gaps
  5. Proposes new skills if a gap appears 3+ times
  6. Sends a structured report to your channel

Combination Detection Logic

STEP 1 — Inventory installed skills
  ls /workspace/skills/
  Read only: name + description fields from each SKILL.md
  Do NOT log or transmit full SKILL.md content

STEP 2 — Map skills to mission domains
  For each domain required by the mission:
    Which installed skill(s) cover this domain?
    → Build domain_map{}

STEP 3 — Detect intersection candidates
  For each pair (skill_A, skill_B) in domain_map:
    Ask: "If skill_A output feeds into skill_B input,
          what new capability emerges?"
    IF emergent_value > individual_value:
      → Add to active_combinations[]

STEP 4 — Check COMBINATIONS.md for proven patterns
  IF yes + proven: use it directly
  IF yes + failed: avoid or adjust
  IF new: mark as experimental, log result after

STEP 5 — Build multi-skill execution plan
  Order combinations by dependency and ROI multiplier
  For side-effecting combinations (trades, deployments, sends):
    → Respect the Autonomy Gate defined in AGENTS.md
    → Micro-actions within approved scope: execute autonomously
    → Macro-actions or unapproved scope: propose to operator first

COMBINATIONS.md Format

## [YYYY-MM-DD] Combination Name

**Skills involved**: skill-A + skill-B (+ skill-C if applicable)
**Mission context**: type of mission that triggered this discovery
**Emergent capability**: what new ability emerged
**Mechanism**: how the skills interact (output of A feeds B, parallel signals, etc.)
**Performance**: tested X times | success rate Y%
**Status**: experimental | proven | deprecated
**Confidence**: low | medium | high
**ROI multiplier**: Nx (how many times more effective than skills used separately)
**Logged by**: agent autonomous discovery | operator instruction
⚠️ Logging rules — what goes in entries and what never does: - ✅ Log: skill names, outcome descriptions, metrics, mechanism summaries - ❌ Never log: file contents, API keys, credentials, personal data, secrets - If an entry would require sensitive data to be meaningful — summarize in plain language instead

Confidence scale:

  • low → first discovery, 1-2 uses
  • medium → 3+ successful uses, pattern emerging
  • high → 10+ consistent uses, fully battle-tested

ROI multiplier: estimated effectiveness gain vs skills used separately. 3x means the combination produced 3x the result of either skill alone.


.learnings Integration

Every combination attempt — success or failure — logged as metadata only:

On success:

## [YYYY-MM-DD] Emergent capability: [name]
**Category**: emergent_capability
**Priority**: medium | high
**Status**: pending
**Skills combined**: skill-A + skill-B
**What emerged**: description (no secrets, no file contents)
**How it works**: mechanism explanation
**Evidence**: outcome metrics and observable results only
**Promotion**: → COMBINATIONS.md when proven 3+ times

On failure:

## [YYYY-MM-DD] Failed combination: [name]
**Category**: emergent_capability_failed
**Priority**: low
**Status**: resolved
**Skills combined**: skill-A + skill-B
**Why it failed**: root cause (no secrets, no file contents)
**Prevention**: what to avoid next time

Weekly Distillation Process (Mode 2)

STEP 1 — Read .learnings/LEARNINGS.md
  Filter: category = emergent_capability OR emergent_capability_failed
  Filter: status = pending

STEP 2 — Identify proven combinations
  proven = same skill pair with 3+ successful entries

STEP 3 — Update COMBINATIONS.md
  For each proven combination:
    → Add or update entry (metadata only — no file contents, no credentials)
    → Mark .learnings entries as status: resolved

STEP 4 — Scan .learnings/FEATURE_REQUESTS.md
  Count recurring gaps (same gap appearing 3+ times)
  → Formulate skill proposals for weekly report

STEP 5 — Read AGENTS.md (read only)
  Do proven combinations deserve mention in the startup ritual?
  IF yes → include as a PROPOSAL in the report
  NEVER write to AGENTS.md directly — operator decides

STEP 6 — Send weekly report

STEP 7 — Log distillation summary to memory/{date}.md
  Log: what was reviewed, promoted, proposed
  Never log: file contents, credentials, personal data

Constraints

❌ Never modify SOUL.md — it is immutable
❌ Never modify AGENTS.md directly — propose only via weekly report
❌ Never install new skills autonomously — only PROPOSE to operator
❌ Never bypass the Autonomy Gate defined in AGENTS.md
❌ Never fabricate combination results — log UNKNOWN if outcome unclear
❌ Never mark a combination as "proven" with fewer than 3 successful uses
❌ Never log file contents, credentials, or sensitive data in any entry
✅ Read AGENTS.md to understand context — never write to it
✅ Always check COMBINATIONS.md before calling something "new"
✅ Log every combination attempt — metadata only, no secrets
✅ New skill proposals → weekly report first, never direct creation
✅ Side-effecting combinations respect the Autonomy Gate from AGENTS.md

Weekly Report Format

🧬 SKILL COMBINATOR — Weekly Report
📅 Week of {YYYY-MM-DD}

📚 SKILLS INVENTORY
• Total installed skills: {N}
• Skills active this week: {list of names}
• New skills since last report: {list or "none"}

⚡ EMERGENT CAPABILITIES DISCOVERED
• New this week: {N}
  → {name}: {skill-A} + {skill-B} = {what emerged}
• Promoted to COMBINATIONS.md: {N}
• Failed combinations logged: {N}

🔥 TOP PROVEN COMBINATIONS
1. {name} — {skill-A + skill-B} — confidence: {low|medium|high} — ROI: {N}x — {N} uses
(or: "No proven combinations yet — accumulating data")

💡 NEW SKILL PROPOSALS
• {skill name}: {capability gap it would fill}
(or: "No proposals this week")

📝 AGENTS.MD UPDATE PROPOSALS
• {proposed addition} — operator decides whether to apply
(or: "No updates proposed this week")

📈 ECOSYSTEM HEALTH
• COMBINATIONS.md entries: {total} — Experimental: {N} | Proven: {N} | Deprecated: {N}
• .learnings pending review: {N} | Resolved this week: {N}

⏰ Next distillation: Sunday {date}

Files Summary

FileAccessWhat is written
/workspace/COMBINATIONS.mdread + writeCombination metadata — no secrets
/workspace/.learnings/LEARNINGS.mdread + writeOutcome metadata — no file contents
/workspace/.learnings/FEATURE_REQUESTS.mdread + writeSkill proposals
/workspace/memory/{date}.mdwriteRun summary — no credentials
/workspace/AGENTS.mdread onlyNever written — proposals via report only
/workspace/MEMORY.mdread onlyContext loading
/workspace/skills/*/SKILL.mdread onlyName + description fields only

平台分布

OpenClaw

89.77%
按下载量换算3,906

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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