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Agent Skill · 研究检索

skill-recommender(技能推荐员)

skill-recommender 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。

skill-recommender 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 41,143。

总安装 1,212周安装 51GitHub Stars 14下载 424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

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-recommender(技能推荐员)
来源仓库:https://github.com/vladm3105/aidoc-flow-framework
仓库路径:skills/skill-recommender
安装命令:
npx skills add https://github.com/vladm3105/aidoc-flow-framework --skill skill-recommender
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/vladm3105/aidoc-flow-framework --skill skill-recommender

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。

用于查找、检索和筛选相关信息,支持基于任务场景的智能推荐。适合在复杂需求下快速缩小候选范围并提供优先级排序。通过 GitHub 仓库安装,兼容主流 AI 开发工具链。推荐结果仅供参考,需人工判断最终适用性。skill-recommender 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

建议输入时明确限定领域或关键词以提升精度。

来源仓库内路径:skills/skill-recommender

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 Codex、Claude、Cursor、Gemini CLI;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 npx skills add https://github.com/vladm3105/aidoc-flow-framework --skill skill-r…;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-recommender 怎么安装?

在终端执行 npx skills add https://github.com/vladm3105/aidoc-flow-framework --skill skill-recommender。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-recommender 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。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-recommender 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-recommender 是免费的吗?

来源标注的许可证是 MIT。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-recommender 用的人多吗?

公开数据是累计安装 1,212 次,近一周安装 51 次,下载 424 次,GitHub 14 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-recommender 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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 Codex,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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-recommender

技能推荐员 · 本页

1,21214需要联网MIT通过

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

skill-recommender

Purpose

Analyze user requests and recommend appropriate documentation skills from the AI Dev Flow framework catalog.

Problem Solved: Users must know which of 25+ skills to invoke for their documentation task, requiring deep framework knowledge.

Solution: Parse user intent, match against skill catalog, and provide ranked recommendations with confidence scores and rationale.

When to Use This Skill

Use skill-recommender when:

  • User is unsure which skill to use for a documentation task
  • Starting a new documentation workflow and need guidance
  • Want to discover available skills for a specific intent
  • Need help navigating the skill catalog

Do NOT use when:

  • User explicitly requests a specific skill (e.g., "/skill doc-prd")
  • Performing non-documentation tasks
  • User is experienced and knows the target skill

Skill Inputs

InputTypeRequiredDescription
user_requeststringYesNatural language description of what user wants to do
project_contextobjectNoProject structure and existing artifacts (from context-analyzer)
max_recommendationsnumberNoMaximum recommendations to return (default: 3)

Skill Workflow

Step 1: Parse User Intent

Extract action verbs and targets from the user request:

Intent Categories:

CategorySignal KeywordsExample Request
createcreate, write, draft, new, add"Create a new PRD for user authentication"
updateupdate, modify, edit, change, revise"Update the traceability section of SPEC-01"
validatevalidate, check, verify, audit, review"Check if my artifacts have proper traceability"
analyzeanalyze, review, examine, inspect"Analyze the project documentation structure"
planplan, roadmap, schedule, organize"Create an implementation roadmap from ADRs"

Target Extraction:

TargetSignal KeywordsMaps To
business requirementsbusiness, brd, objectivesdoc-brd
product requirementsproduct, prd, features, user storiesdoc-prd
formal requirementsears, formal, when-the-shalldoc-ears
test scenariosbdd, tests, scenarios, gherkindoc-bdd
architecture decisionsadr, architecture, decisiondoc-adr
system requirementssys, system, technicaldoc-sys
requirementsreq, requirement, atomicdoc-req
implementation planimpl, implementation, plandoc-impl
contractsctr, contract, api, interfacedoc-ctr
specificationsspec, specification, yamldoc-spec
taskstasks, todo, implementation tasksdoc-tasks
traceabilitytrace, traceability, linkstrace-check
validationvalidate, quality, compliancedoc-validator
diagramsdiagram, mermaid, chart, flowcharts-flow, mermaid-gen
roadmaproadmap, adr implementationadr-roadmap
project managementmvp, mmp, release, planningproject-mngt

Step 2: Match Skills

Match parsed intent against skill catalog:

Skill Catalog (Core Documentation Skills):

Skill IDCategoryLayerDescription
doc-brdcore-workflow1Business Requirements Documents
doc-prdcore-workflow2Product Requirements Documents
doc-earscore-workflow3EARS Formal Requirements
doc-bddcore-workflow4BDD Test Scenarios
doc-adrcore-workflow5Architecture Decision Records
doc-syscore-workflow6System Requirements
doc-reqcore-workflow7Atomic Requirements
doc-implcore-workflow8Implementation Plans (optional)
doc-ctrcore-workflow9API Contracts (optional)
doc-speccore-workflow10Technical Specifications
doc-taskscore-workflow11Implementation Tasks

Quality Assurance Skills:

Skill IDCategoryDescription
trace-checkquality-assuranceValidate bidirectional traceability
doc-validatorquality-assuranceValidate documentation standards
code-reviewquality-assuranceCode quality review
contract-testerquality-assuranceTest API contracts

Utility Skills:

Skill IDCategoryDescription
charts-flowutilityMermaid architecture diagrams
mermaid-genutilityGenerate Mermaid diagrams
analytics-flowutilityAnalytics and data analysis
project-initutilityInitialize project structure

Planning Skills:

Skill IDCategoryDescription
adr-roadmapplanningADR implementation roadmaps
project-mngtplanningMVP/MMP/MMR release planning
doc-flowplanningSDD workflow orchestration

Step 3: Score and Rank

Calculate confidence scores based on:

FactorWeightDescription
Intent match40%How well request matches skill intent signals
Target match30%Explicit skill/artifact type mentioned
Context fit20%Project state and workflow position
Usage patterns10%Common skill sequences

Confidence Levels:

  • High (≥80%): Strong match, recommend with confidence
  • Medium (50-79%): Good match, include alternative options
  • Low (<50%): Weak match, suggest clarification

Step 4: Generate Recommendations

Format recommendations with rationale:

Output Format:

recommendations:
  - skill: doc-prd
    confidence: 92%
    rationale: "Request mentions 'product requirements' and 'features' - direct match for PRD creation"
    next_steps: "Run /skill doc-prd to create Product Requirements Document"

  - skill: doc-brd
    confidence: 65%
    rationale: "May need BRD first if business requirements not yet documented"
    condition: "Use if no BRD exists for this feature"

  - skill: doc-ears
    confidence: 45%
    rationale: "EARS follows PRD in workflow - consider after PRD completion"
    condition: "Use after PRD is complete"

clarification_needed: false
clarification_question: null

Example Usage

Example 1: Clear Intent

User Request: "I need to create a product requirements document for a new authentication feature"

Skill Analysis:

Intent: create (keywords: create, need)
Target: product requirements (keywords: product requirements document)
Domain: authentication feature

Recommendations:

recommendations:
  - skill: doc-prd
    confidence: 95%
    rationale: "Explicit request for Product Requirements Document"
    next_steps: "Run /skill doc-prd to create PRD for authentication feature"

  - skill: doc-brd
    confidence: 40%
    rationale: "BRD may be needed upstream if not already created"
    condition: "Check if BRD exists for authentication feature"

Example 2: Ambiguous Intent

User Request: "Help me document the system architecture"

Skill Analysis:

Intent: create (keywords: document)
Target: ambiguous - could be ADR, SYS, or diagrams

Recommendations:

recommendations:
  - skill: doc-adr
    confidence: 60%
    rationale: "Architecture decisions typically documented in ADRs"

  - skill: doc-sys
    confidence: 55%
    rationale: "System requirements capture technical architecture"

  - skill: charts-flow
    confidence: 50%
    rationale: "Architecture diagrams visualize system structure"

clarification_needed: true
clarification_question: "What aspect of architecture? (1) Decisions/rationale (ADR), (2) System specs (SYS), (3) Visual diagrams?"

Example 3: Validation Request

User Request: "Check if my documentation has proper links between artifacts"

Skill Analysis:

Intent: validate (keywords: check)
Target: traceability (keywords: links between artifacts)

Recommendations:

recommendations:
  - skill: trace-check
    confidence: 98%
    rationale: "Direct request for traceability validation"
    next_steps: "Run /skill trace-check to validate bidirectional links"

Integration with Other Skills

IntegrationDescription
context-analyzerReceives project context for better recommendations
doc-flowCan be invoked by doc-flow for skill discovery
workflow-optimizerShares workflow position awareness

Quality Gates

Definition of Done

  • User request parsed successfully
  • At least one skill recommendation provided
  • Confidence scores calculated for all recommendations
  • Rationale included for each recommendation
  • Clarification question generated when ambiguous

Performance Targets

MetricTarget
Response latency<500ms
Recommendation accuracy≥85%
User acceptance rate≥70%

Traceability

Required Tags:

@prd: PRD.000.001
@adr: ADR-000

Upstream Sources

SourceTypeReference
PRD-00Product RequirementsPRD-00
ADR-000Architecture DecisionADR-000

Downstream Artifacts

ArtifactTypeReference
Selected doc-* skillSkill ExecutionInvoked based on recommendation

Version Information

Version: 1.0.0 Created: 2025-11-29 Status: Active Author: AI Dev Flow Framework Team

平台分布

Gemini CLI

29.26%
按下载量换算124

Claude Code

22.68%
按下载量换算96

OpenCode

15.86%
按下载量换算67

Antigravity

11.02%
按下载量换算47

Codex

8.1%
按下载量换算34

windsurf

3.18%
按下载量换算13

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。

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