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recommend-evolution推荐进化

Agent Skill

recommend-evolution 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 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。

总安装

1,188

周安装

50

GitHub Stars

25

下载量

416
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:recommend-evolution(推荐进化)
来源仓库:https://github.com/oimiragieo/agent-studio
仓库路径:skills/recommend-evolution
安装命令:
npx skills add https://github.com/oimiragieo/agent-studio --skill recommend-evolution
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/oimiragieo/agent-studio --skill recommend-evolution

简介

recommend-evolution 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词快速定位候选结果。
  • 通过 npx skills add 命令从指定仓库安装并使用该技能。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网或命令执行。
  • 建议结合来源仓库和原始 README 核验具体用法。

SKILL.md

Recommend Evolution

Overview

Recommend ecosystem evolution when repeated evidence indicates missing capability, and record the recommendation in a standard machine-readable format.

When to Use

  • Reflection identifies recurring delivery failures with the same root cause
  • Router/analysis signals no suitable agent or skill for recurring requests
  • Repeated integration gaps imply missing artifact type or policy
  • User explicitly requests a new capability path

Iron Laws

  1. NEVER spawn evolution-orchestrator directly from this skill — this skill records recommendations only; execution decisions belong to the orchestrator and approval pipeline.
  2. ALWAYS validate trigger type against defined thresholds before recording a recommendation — vague observations are not triggers; require concrete failure counts or routing misses.
  3. NEVER create a new evolution request when artifact-integrator or skill-updater would address the gap — reserve evolution for net-new capabilities, not integration or update gaps.
  4. ALWAYS append the recommendation to the JSONL queue AND include the required report block in the current output — dual recording ensures the recommendation is discoverable at both runtime and review time.
  5. NEVER proceed with a recommendation without evidence — single failures are noise; trigger thresholds exist for a reason.

Trigger Taxonomy Note

recommend-evolution uses a cause-oriented trigger taxonomy (repeated_error, no_agent, integration_gap, user_request, rubric_regression, stale_skill, other).

This intentionally differs from skill-updater, which uses a caller-oriented trigger taxonomy (reflection, evolve, manual, stale_skill) to describe who/what initiated the update path.

Step 0: Validate Trigger Type

Use these thresholds:

  • repeated_error: same class of failure in 5+ tasks
  • rubric_regression: repeated score drop below threshold for same class of task
  • no_agent: recurring need with no valid routing match
  • integration_gap: existing artifact integration missing (prefer artifact-integrator)
  • user_request: explicit request for capability not available
  • stale_skill: audit pipeline reports verified artifact older than 6 months or invalid lastVerifiedAt

Step 1: Decide Recommendation Path

  • If gap is integration of existing artifact, prefer: Skill({skill: 'artifact-integrator'})
  • If gap is stale/underperforming existing skill, prefer: Skill({skill: 'skill-updater'})
  • If gap requires net-new capability/artifact, continue with evolution recommendation
  • If no artifact change needed, update memory only and exit

Step 2: Create Standard Recommendation Payload

Build one JSON object with required fields:

{
  "timestamp": "2026-02-14T00:00:00.000Z",
  "source": "reflection-agent",
  "trigger": "repeated_error",
  "evidence": "Same routing failure observed in 6 tasks over 2 days.",
  "suggestedArtifactType": "skill",
  "summary": "Create a new routing-context skill for reflection-time grounding.",
  "status": "proposed"
}

Schema reference: .claude/schemas/evolution-request.schema.json

Step 3: Record Recommendation

  1. Append JSON line to: .claude/context/runtime/evolution-requests.jsonl
  2. Add required report block:
## Evolution Recommendation

- Trigger: <trigger>
- Evidence: <evidence>
- Suggested Artifact Type: <type|null>
- Summary: <1-2 sentences>
- Queue Record: `.claude/context/runtime/evolution-requests.jsonl`

Step 3: Output

Return recommendation summary and what was recorded.

</execution_process>

// Repeated failure pattern -> recommend skill creation
Skill({
  skill: 'recommend-evolution',
  args: '--trigger repeated_error --suggestedArtifactType skill',
});

// Routing miss -> recommend new agent/workflow discussion
Skill({ skill: 'recommend-evolution', args: '--trigger no_agent --suggestedArtifactType agent' });

</usage_example>

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Spawning evolution-orchestrator directly from this skillViolates single-responsibility; bypasses approval and resource gatesRecord recommendation to JSONL queue only; let the orchestrator decide on execution
Recording an evolution request for an integration gap that already has artifactsCreates unnecessary new artifacts when an integration fix would sufficeCheck artifact-integrator path first; escalate only if gap requires net-new capability
Submitting a recommendation without trigger evidenceUninformed evolution wastes resources and pollutes the queue with noiseRequire concrete evidence: failure counts, routing miss logs, or explicit user request
Routing stale-skill triggers through this skill instead of skill-updaterWrong escalation path; creates evolution requests for work that belongs in an update cycleRoute stale_skill triggers directly to skill-updater; only escalate if the skill cannot be updated
Triggering evolution after a single failure instanceSingle failures are noise; premature evolution wastes build capacityApply defined thresholds: 5+ repeated errors, consistent routing misses across sessions

Memory Protocol (MANDATORY)

Before starting:

Read .claude/context/memory/learnings.md using Read or Node fs.readFileSync (cross-platform).

After completing:

  • Recommendation pattern -> .claude/context/memory/learnings.md
  • Ambiguous trigger logic -> .claude/context/memory/issues.md
  • Evolution policy decision -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

35.04%
按下载量换算146

Claude

29.13%
按下载量换算121

Cursor

19.23%
按下载量换算80

Gemini CLI

8.77%
按下载量换算36

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

可写文件

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安装前确认

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来源信息

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