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research-patterns研究模式

Agent Skill

research-patterns 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

480

周安装

20

GitHub Stars

23

下载量

160
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:research-patterns(研究模式)
来源仓库:https://github.com/akaszubski/autonomous-dev
仓库路径:skills/research-patterns
安装命令:
npx skills add https://github.com/akaszubski/autonomous-dev --skill research-patterns
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/akaszubski/autonomous-dev --skill research-patterns

简介

建立基于证据的研究方法论,包含代码库探查、网络搜索与结论归纳。

  • 适用于技术调研、竞品分析和解决方案筛选等任务。
  • 使用时需分四阶段推进:代码基线扫描、精准搜索、模式比对与结论输出。
  • 安装命令:npx skills add https://github.com/akaszubski/autonomous-dev --skill research-patterns
  • 建议优先利用现有代码模式,减少重复造轮子。

SKILL.md

Research Patterns Enforcement Skill

Ensures every research task follows a consistent, evidence-based methodology. Used by the researcher and researcher-local agents.

4-Phase Research Methodology

Every research task MUST follow these phases in order.

Phase 1: Codebase Recon

  • Grep/Glob for existing patterns that relate to the task
  • Identify what the codebase already does (avoid reinventing)
  • Note file locations, naming conventions, architectural patterns
  • Document existing test patterns for the area

Phase 2: Targeted Web Search

  • Formulate 2-3 specific search queries
  • Include the current year in queries for freshness (e.g., "JWT best practices 2026")
  • Search for official documentation first
  • Search for known issues or CVEs if security-related

Phase 3: Deep Fetch Top Sources

  • Fetch the top 2-3 most relevant results
  • Extract specific code examples, configuration snippets, or API references
  • Note version numbers and compatibility requirements
  • Record URLs for citation

Phase 4: Synthesis with Gap Analysis

  • Compare findings against existing codebase patterns
  • Identify gaps between current implementation and best practices
  • Produce structured recommendations with tradeoffs
  • Flag risks and unknowns explicitly

Source Hierarchy

When sources conflict, prefer in this order:

  1. Official documentation — language docs, framework docs, RFCs
  2. Authoritative GitHub repos — reference implementations, official examples
  3. Stack Overflow — accepted answers with high votes, verify currency
  4. Blog posts / tutorials — cross-reference with official docs

Every recommendation MUST include at least one URL source.


HARD GATE: Research Output

FORBIDDEN:

  • Recommending an approach without citing sources
  • Using "I think" or "I believe" without supporting evidence
  • Skipping codebase search (Phase 1) and jumping straight to web
  • Single-source recommendations — minimum 2 sources for any recommendation
  • Presenting opinions as facts
  • Ignoring existing codebase patterns in favor of greenfield approaches

REQUIRED:

  • Minimum 3 sources cited across the research output
  • Existing codebase patterns identified first (Phase 1 before Phase 2)
  • Tradeoffs stated for every recommendation (pros AND cons)
  • Structured output in the format below
  • Version/date noted for all external sources
  • Risks section with at least one identified risk

Required Output Format

{
  "findings": "Summary of what was discovered",
  "sources": [
    {"url": "https://...", "title": "...", "relevance": "..."},
    {"url": "https://...", "title": "...", "relevance": "..."},
    {"url": "https://...", "title": "...", "relevance": "..."}
  ],
  "existing_patterns": [
    {"file": "path/to/file.py", "pattern": "description", "reusable": true}
  ],
  "recommendations": [
    {
      "approach": "description",
      "pros": ["..."],
      "cons": ["..."],
      "effort": "low/medium/high"
    }
  ],
  "risks": [
    {"risk": "description", "mitigation": "how to handle", "severity": "low/medium/high"}
  ]
}

Anti-Patterns

BAD: Vague "best practice" without citation

"The best practice is to use dependency injection."

No source, no context, no tradeoffs. Useless as research output.

GOOD: Cited recommendation with tradeoffs

"Dependency injection is recommended by the Python Packaging Guide
(https://packaging.python.org/...) for testability. Tradeoff: adds
indirection that can make debugging harder for small projects."

BAD: Ignoring existing codebase patterns

Recommending a completely new auth library when the codebase already uses a working pattern. Always check what exists first.

BAD: Single-source echo chamber

Reading one blog post and presenting its opinion as the definitive answer. Cross-reference with at least one other source.

GOOD: Multiple sources with synthesis

"Three sources agree on token rotation (RFC 6749 Section 10.4,
OWASP Cheat Sheet, and the existing auth.py pattern at line 42).
The codebase already implements refresh tokens; recommend extending
rather than replacing."

Cross-References

  • security-patterns: Security-specific research requirements
  • architecture-patterns: How research feeds into architecture planning

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

37.39%
按下载量换算60

Claude

30.04%
按下载量换算48

Cursor

19.86%
按下载量换算32

Gemini CLI

9.3%
按下载量换算15

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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