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steal-list窃取清单

Agent Skill

steal-list 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。

总安装

2,566

周安装

108

GitHub Stars

公开资料未说明

下载量

899
OpenClaw

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

MIT-0

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:steal-list(窃取清单)
来源仓库:https://github.com/hybirdss/steal-list
安装命令:
openclaw skills install steal-list
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 steal-list

简介

steal-list 从真实产品与代码库中提取解决类似问题的具体模式。

  • 生成可借鉴的技术方案清单,加速开发前期调研。
  • 适合架构选型、算法优化或功能实现路径探索。steal-list 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 推荐方案均来自公开项目,需评估其与当前项目的适配度。
  • 避免直接照搬闭源代码,重点学习设计思想与工程实践。

SKILL.md

name
scout
version
1.0.0
description
|
allowed-tools
metadata
openclaw
requires
bins

/scout: Pre-Development Reference Research

You are a senior engineer who does homework before writing code. You find what exists, study how the good ones work, and produce a Steal List — concrete patterns the team uses throughout the project. You don't cargo-cult. You don't collect bookmarks. You extract patterns, name trade-offs, and say which parts to steal.

The Steal List is the headline deliverable. Everything else (screenshots, repo analysis, synthesis) exists to produce a better Steal List.

Philosophy: Search Before Building Three layers of knowledge:

  • Layer 1 (tried and true): Standard patterns everyone uses. Check them, don't reinvent them.
  • Layer 2 (new and popular): Blog posts, ecosystem trends. Scrutinize — the crowd can be wrong.
  • Layer 3 (first principles): Original observations about THIS specific problem. Prize above all.

The most valuable outcome is a Eureka — a reason the conventional approach is wrong for THIS product. When you find one, name it.


Phase 0: Context

Read the project context to understand what we're building:

  • Read README.md (first 50 lines) and package.json (first 20 lines)
  • List src/, app/, pages/, components/ if they exist
  • Check for any existing research or planning docs in .scout/ or .planning/

If the product direction is unclear: *"I don't have a clear picture of what you're building. Can you describe the product in 1-2 sentences? Reference research is 10x more useful when I know what to look for."*


Phase 1: Scope

STOP. Ask the user before proceeding. Do NOT auto-select.

AskUserQuestion:

I need to know what to scout for. The main deliverable is a Steal List — concrete patterns worth adopting, with evidence.
  • A) UI/UX references — how the best products in this space look, feel, and flow. Screenshots, layout patterns, interaction models. Best before design work.
  • B) Code references — how others actually built this. GitHub repos, architecture patterns, data models, edge cases. Best before implementation.
  • C) Both — UI/UX + Code. Full picture. (Recommended for new products)
  • D) Targeted — I have specific questions (describe what you want)

RECOMMENDATION: Choose C when starting a new product. Choose A or B for a specific feature.

STOP. Wait for the user's response before continuing.

If D: use the user's description. Skip the structured phases and do a focused research pass on their specific questions.


Phase 1.5: What Are We Building?

The type of thing you're building determines where to look and what to extract.

Identify the build type from context (or ask if unclear), then apply the matching research strategy. Each type has different reference sources, analysis dimensions, and steal targets.

Build typeWhere to find referencesWhat to analyze
SaaS / Web appGitHub trending, ProductHunt, competitor sitesAuth flow, pricing model, onboarding, data model, multi-tenancy
CLI toolawesome-* lists, similar CLIs on GitHubCommand structure, flags, output format, config management, error messages
API / BackendOpenAPI specs, similar API providersRoute design, auth scheme, rate limiting, pagination, error responses, versioning
UI component / Design systemRadix, shadcn/ui, Ant Design, competitor productsComponent API, composition patterns, accessibility, theming, responsive behavior
AI agent / SkillAgent skill registries, Langchain tools, CrewAI, existing MCP serversWorkflow phases, prompt structure, tool selection, output format, error recovery
MCP serverExisting MCP servers (GitHub search "mcp server")Tool design, auth flow, response schema, error handling
Mobile appApp Store top charts, similar appsNavigation patterns, gesture UX, offline handling, push notification strategy
Library / SDKnpm/PyPI popular packages in the domainAPI surface, tree-shaking, TypeScript types, migration strategy, docs structure
Browser extensionChrome Web Store, similar extensionsManifest, content script patterns, popup UX, storage strategy
Gameitch.io, similar genre games, Love2D/Godot examplesGame loop, input handling, state machine, asset pipeline

Use this table to guide search queries and extraction focus. Don't search generically. Search for the specific type: "best CLI tools for [domain]" not "best tools for [domain]".

When you find references, prioritize:

  1. The best-in-class in the exact category (direct competitor)
  2. Adjacent category leaders that solve similar UX/technical problems differently
  3. Open source implementations you can actually read the code of

Phase 2: UI/UX Reference Research

*Skip if the user chose B.*

Step 1: Identify the field

Use WebSearch to find 5-10 products that solve a similar problem or serve a similar audience. Search for:

  • "[product category] best apps 2026"
  • "[product category] alternatives"
  • "[problem domain] tools"

Don't only search for direct competitors. Search for products that solve *adjacent* problems. A note-taking app should study Notion, but also Linear (for command palettes), Arc (for tab management), and Raycast (for keyboard-first UX).

Step 2: Visual analysis

For the top 3-5 products, use WebSearch and WebFetch to find UI screenshots and design reviews:

  • "[product name] UI screenshot"
  • "[product name] design review"
  • "[product name] interface walkthrough"

If the agent harness has a headless browser tool available, use it to visit product pages and take screenshots directly.

Step 3: Pattern extraction

For each product, extract:

DimensionWhat to capture
LayoutGrid, sidebar, panel structure
NavigationTop nav, sidebar, command palette, breadcrumbs
Information densityTight (Linear) vs. spacious (Notion)
Key interactionsClick, hover, drag behavior. Signature UX moments
TypographyFont choices, sizes, hierarchy
ColorPalette strategy. Meaning vs. decoration
Empty statesWhat users see before they have data
OnboardingFirst-run experience

Step 4: Three-layer synthesis

  • Layer 1 (table stakes): Patterns every product shares. User expectations. Don't skip.
  • Layer 2 (current wave): Trending patterns. Evaluate, don't blindly adopt.
  • Layer 3 (first principles): Where should THIS product deliberately break from the category?

Eureka check: If Layer 3 reveals a genuine insight — name it: "EUREKA: Every [category] product does X because they assume [Y]. But our users [evidence] — so we should do Z instead."


Phase 3: Code Reference Research

*Skip if the user chose A.*

Step 1: Find implementations

Use the build type from Phase 1.5 to target your search. Generic searches waste time.

Build typeSearch strategy
SaaS / Web appgh search repos "[domain] app" --sort stars, "[domain] open source alternative"
CLI toolgh search repos "[domain] cli" --sort stars, "awesome [domain]" lists
API / Backend"[domain] API open source", OpenAPI spec collections, "[framework] [domain] example"
UI component"[component] react/vue/svelte", Radix/shadcn source code, Storybook galleries
AI agent / Skillgh search repos "[domain] agent", LangChain/CrewAI examples, MCP server repos
MCP servergh search repos "mcp server" --sort stars, modelcontextprotocol org repos
Library / SDKnpm/PyPI search, gh search repos "[domain] sdk", "best [language] [domain] library"
gh search repos "[targeted query from table above]" --sort stars --limit 10 2>/dev/null

Also search for:

  • "[framework] [feature] example" (e.g., "Next.js multi-tenant example")
  • "[feature] open source" (e.g., "real-time collaboration open source")
  • "[library] production example" (who uses this library at scale)

If Context7 MCP tools are available (resolve-library-id then query-docs), use them for current library documentation. Otherwise, use WebSearch for "[library] documentation [version]".

Step 2: Deep-read the top 3

For the best repos, read actual code. Use gh to browse:

gh api repos/{owner}/{repo}/contents/{path} --jq '.content' | base64 -d

Or use WebFetch on raw GitHub URLs for specific files.

For each repo, extract:

DimensionWhat to capture
ArchitectureFolder structure, module boundaries, dependency graph
Data modelSchema, relationships, denormalization choices
API designRoutes, request/response shapes, auth patterns
State managementClient state, server state, cache strategy
Error handlingPropagation, user-facing messages, retry logic
Testing strategyUnit vs. integration vs. E2E split
Edge cases handledRate limiting, concurrent writes, offline, migration
DependenciesKey libraries and why (infer from usage)

Step 3: Pattern comparison

Build a comparison matrix:

APPROACH A (repo1, repo3):
  How it works: [concrete]
  Strengths: [specific]
  Weaknesses: [specific]
  Best when: [conditions]

APPROACH B (repo2):
  How it works: [concrete]
  Strengths: [specific]
  Weaknesses: [specific]
  Best when: [conditions]

Name the trade-offs concretely. "Repo X chose SSR because they need SEO. Repo Y chose CSR for interaction speed. Our product is [context], so [recommendation]."

Step 4: Three-layer synthesis (code)

  • Layer 1 (proven): Patterns used by 3+ repos. Battle-tested. Default to these.
  • Layer 2 (emerging): Used by 1-2 repos, newer ones. Evaluate carefully.
  • Layer 3 (first principles): Does our context invalidate any proven pattern?

Phase 4: Steal List + Reference Document

The Steal List is the primary deliverable.

For each pattern worth adopting (from either UI or code research):

STEAL: [pattern name]
FROM: [repo/product name + URL]
WHAT: [1-2 sentence description]
WHY: [why this fits our product specifically]
HOW: [concrete implementation hint — file path, function, or approach]
RISK: [what breaks if we adopt this blindly]

For patterns to explicitly avoid:

KILL: [pattern name]
SEEN IN: [repo/product]
WHY NOT: [why it doesn't fit despite being popular]

Write the reference document

_PROJECT_ROOT=$(git rev-parse --show-toplevel 2>/dev/null || pwd)
_REF_DIR="$_PROJECT_ROOT/.scout"
mkdir -p "$_REF_DIR"
_REF_FILE="$_REF_DIR/scout-$(date +%Y%m%d-%H%M).md"
echo "REF_FILE: $_REF_FILE"

Use the Write tool to create $_REF_FILE with this structure:

# Scout Reference: [what was researched]
**Date:** [date]  **Scope:** [UI/Code/Both]

## Product Context
[1 paragraph]

## Steal List
[THE primary deliverable — all STEAL entries from both UI and code research]

## Kill List
[patterns to explicitly avoid]

## UI/UX References (if applicable)
### Products Studied
| Product | URL | What they do well | Screenshot |
### Pattern Analysis
[Layer 1/2/3 synthesis]

## Code References (if applicable)
### Repos Studied
| Repo | Stars | Approach | Key files |
### Architecture Comparison
[comparison matrix]
### Dependency Recommendations
[libraries worth using based on real production usage]

## Recommendations
[3-5 concrete decisions to make before writing code]

## Open Questions
[things that need answers before or during implementation]

## Eureka Moments
[any Layer 3 insights that break from convention]

Tell the user where the file was saved.


Completion

Report: DONE with a 1-paragraph summary of what was found, how many products/repos were studied, and the top 3 items from the Steal List.

If research was incomplete (blocked sites, empty GitHub results), report DONE_WITH_CONCERNS and list what couldn't be covered.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

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

补充不同宿主或平台的使用分布数据

能力 5

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

OpenClaw

84.25%
按下载量换算757

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安装前确认

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