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Agent Skill · 研究检索

bgskillz(bgskillz 搜索)

bgskillz 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。

bgskillz 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,权限为调用外部服务;许可证信息还不明确,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 72,863。

总安装 186周安装 8GitHub Stars 公开资料未说明下载 65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
unknown

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:bgskillz(bgskillz 搜索)
来源仓库:https://github.com/bjg4/bgskillz
仓库路径:skills/bgskillz
安装命令:
npx skills add https://github.com/bjg4/bgskillz --skill bgskillz
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/bjg4/bgskillz --skill bgskillz

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。

bgskillz 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于根据关键词、任务场景或来源线索进行信息搜索和筛选的场景。通过 npx skills add 命令从指定仓库安装,需确认权限范围及是否触发联网或文件操作。

使用前建议核实维护状态,避免在不支持的环境中运行导致意外行为。bgskillz 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/bgskillz

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/bjg4/bgskillz --skill bgskillz;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

    它需要调用外部服务,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

bgskillz 怎么安装?

在终端执行 npx skills add https://github.com/bjg4/bgskillz --skill bgskillz。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

bgskillz 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。

bgskillz 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

bgskillz 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

bgskillz 用的人多吗?

公开数据是累计安装 186 次,近一周安装 8 次,下载 65 次。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。

bgskillz 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

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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 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。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 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
bgskillz

bgskillz 搜索 · 本页

186—调用外部服务未明确有提醒

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

BGSkillz

Build high-quality, portable Claude skills that trigger reliably and deliver real value.

Core Philosophy

  1. Skills are prompts — SKILL.md is a prompt document. Everything in it shapes Claude's behavior when the skill activates.
  2. Explain the why, not just the what — LLMs are smart. They respond better to understood rationale than rigid rules. Instead of "ALWAYS use 4-space indentation," explain *why* consistent indentation matters. If you find yourself writing MUST or NEVER in all caps, that's a yellow flag — reframe as reasoning.
  3. Progressive disclosure — Keep SKILL.md lean (<500 lines). Put deep reference material in references/, agent instructions in agents/, and link to them. Claude reads these when referenced explicitly.
  4. Composability — Skills should do one thing well. Combine multiple skills for complex workflows rather than building monoliths.
  5. Portability — Skills work across Claude.ai, Claude Code, and the API. Write for all surfaces unless you have a reason not to.
  6. Specificity wins — Vague skills don't trigger. Specific skills with clear use cases and trigger phrases activate reliably. Make descriptions slightly "pushy" — Claude tends to undertrigger rather than overtrigger.
  7. Generalize, don't overfit — A skill that works only for your test examples is useless. It will be invoked by diverse users with diverse needs. When iterating, resist fiddly overfitty changes. Instead, try different metaphors and explain reasoning. Lean toward fewer, higher-impact improvements.

Skill Anatomy

my-skill/
├── SKILL.md              # Required. Main entry point. Contains frontmatter + instructions.
├── agents/               # Optional. Sub-agent instruction files for multi-agent workflows.
├── scripts/              # Optional. Executable helpers (Python, Bash, etc.)
├── references/           # Optional. Deep reference docs linked from SKILL.md.
└── assets/               # Optional. Templates, configs, examples bundled with the skill.

SKILL.md is the only required file. It must be exactly SKILL.md (not skill.md, not README.md).

Frontmatter fields:

  • name (required): kebab-case, no spaces, no capitals, no "claude" or "anthropic"
  • description (required): Under 1024 chars. Determines when the skill triggers.
  • license, metadata, compatibility: Optional but recommended for distribution.

Creation Workflow

Follow these 7 steps to build a skill from scratch.

Step 1: Define 2-3 Concrete Use Cases

Before writing anything, articulate exactly who will use this skill and for what. Pick a category:

  • Document/Asset Creation — Generate reports, code, configs, designs
  • Workflow Automation — Multi-step processes with tools and decisions
  • MCP Enhancement — Add intelligence on top of MCP server capabilities

Write 2-3 specific use case sentences: "A developer wants to... so they can..."

Step 2: Define Success Criteria

Set measurable goals before building:

  • Quantitative: Triggers correctly >90% of the time, reduces task time by X%, output matches template Y% of the time
  • Qualitative: Users find output useful without heavy editing, skill integrates naturally into existing workflow

Step 3: Choose Your Approach

  • Problem-first: You have a pain point. Design the skill around solving it.
  • Tool-first: You have an MCP server or API. Design the skill to make it more useful.

Problem-first skills tend to have better descriptions because the pain point *is* the trigger.

Step 4: Plan Reusable Contents

Decide what goes into each directory:

  • agents/: Sub-agent instruction files loaded only when spawning specialized agents (graders, comparators, analyzers). These keep SKILL.md lean while enabling multi-agent workflows.
  • scripts/: Anything Claude should execute (scaffolders, validators, eval runners, build tools)
  • references/: Deep knowledge Claude should read when needed (style guides, API docs, patterns, schemas)
  • assets/: Templates, example files, configs that get copied into user projects

Rule of thumb: If it's >50 lines and not needed on every invocation, it belongs in references/. If it's instructions for a sub-agent, it belongs in agents/.

Step 5: Initialize the Skill

Run the scaffolder to create your skill directory:

python ~/.claude/skills/bgskillz/scripts/init_skill.py my-skill-name --path ~/target/directory

This creates a well-structured starting point with TODO prompts to guide you.

Step 6: Write the Skill

This is where quality is made or lost. Follow these rules:

Description (most critical field): Use the formula: [What it does] + [When to use it] + [Key capabilities]

Good: "Generate production-ready database migrations from natural language descriptions. Use when adding tables, columns, indexes, or modifying schema. Handles rollbacks, data preservation, and index optimization."

Bad: "Helps with database stuff."

See references/description-crafting.md for 15+ examples and anti-patterns.

Naming rules:

  • kebab-case only: my-cool-skill not MyCoolSkill
  • No spaces or capital letters
  • Never include "claude" or "anthropic" in the name
  • Skill folder name must match the name: field in frontmatter

Writing instructions:

  • Use imperative voice: "Generate a report" not "You should generate a report"
  • Be specific and actionable: "Use 4-space indentation" not "Format code nicely"
  • Explain reasoning over rigid rules: "Use early returns because deeply nested code is harder to debug" is more effective than "ALWAYS use early returns"
  • Front-load critical instructions — Claude may skim long documents
  • Include examples of good output when possible — Claude mimics examples more reliably than it follows abstract rules
  • Use markdown headings (##, ###) to organize sections — NOT XML tags
  • Provide a "degrees of freedom" principle: tell Claude what it CAN vary, not just constraints
  • Set defaults with escape hatches: "Use TypeScript by default. If the user specifies another language, use that instead."
  • Calibrate your tone to your audience — users range from non-technical to expert developers. Use context cues from the user's message to adapt jargon level.

Error handling:

  • Tell Claude what to do when things go wrong
  • Include fallback behaviors for missing tools or failed API calls
  • Specify how to communicate errors to the user

Security rules:

  • Never instruct Claude to bypass safety measures
  • Don't hardcode credentials or API keys
  • Don't reference external URLs that could change or be compromised
  • Scripts should validate inputs before executing

Step 7: Package and Distribute

Run the packager to validate and create a distributable zip:

python ~/.claude/skills/bgskillz/scripts/package_skill.py /path/to/my-skill

This runs full validation, then creates a zip ready for upload or sharing. See references/distribution-guide.md for hosting and positioning guidance.

Critical Rules

These are hard requirements. Violating them causes failures.

  1. File must be named SKILL.md — Exact casing. Not skill.md, not Skill.md.
  2. No README.md inside the skill folder — It confuses the system. README goes in your GitHub repo root, outside the skill folder.
  3. Name must be kebab-case — my-skill not my_skill or MySkill or my skill
  4. No XML tags in frontmatter — No < or > characters anywhere in the YAML block.
  5. Name must match folder — If folder is my-skill/, frontmatter name must be my-skill.
  6. No "claude" or "anthropic" in name — Reserved terms.
  7. Description under 1024 characters — Hard limit.
  8. SKILL.md under 5000 words — Beyond this, Claude's attention degrades. Use references for depth.
  9. One level of nesting — One level deep is fine. Nested subdirectories like bar/baz/ inside references are not.
  10. Forward slashes only — Even on Windows. No backslash paths.

Best Practices

Be specific and actionable. Every instruction should pass the "what would Claude actually do?" test. "Write good code" fails. "Use early returns to reduce nesting. Limit functions to 20 lines. Name variables descriptively." passes.

Progressive disclosure. Put the 20% of instructions that cover 80% of use cases in SKILL.md. Put the remaining detail in references. Link clearly: "For advanced configuration patterns, see references/workflow-patterns.md."

Reference bundled resources clearly. When pointing to a reference file, use the exact relative path. Claude will read the file when you reference it this way.

Include error handling. Tell Claude what to do when: the user's request is ambiguous, a required tool is missing, an API call fails, the output doesn't match expectations.

Consistent terminology. Pick one term and stick with it. Don't alternate between "skill", "plugin", and "extension" in the same document.

Default + escape hatch. "Generate TypeScript by default. If the user requests JavaScript or another language, adapt accordingly." This gives Claude a clear default while preserving flexibility.

Show, don't just tell. Include 1-2 examples of ideal output in your SKILL.md. Claude mimics examples more reliably than it follows abstract rules.

Look for repeated work. If you run tests and notice Claude independently writes similar boilerplate or setup code each time, bundle that code into the skill as a script or template. Don't make Claude reinvent the wheel on every invocation.

Keep the prompt lean. After each iteration, review the full SKILL.md and remove instructions that aren't pulling their weight. Read transcripts to identify instructions that Claude ignores or that cause unproductive behavior. A shorter, focused skill outperforms a comprehensive but bloated one.

Testing and Iteration

Test your skill in three ways, from manual to fully automated:

  1. Trigger testing — Does it activate when it should? Does it stay quiet when it shouldn't?

- Test with exact phrases: "Create a new skill" - Test with paraphrases: "I want to build a plugin for Claude" - Test with non-triggers: "Write a Python script" (should NOT trigger a skill-building skill)

  1. Functional testing — When triggered, does it produce correct output?

- Test the happy path with a straightforward request - Test edge cases (empty input, unusual formats, missing context) - Test with different Claude models if possible (Haiku may need more explicit instructions)

  1. Baseline comparison — Is the skill actually better than Claude without it?

- Run the same task with and without the skill - The skill should produce noticeably better results

Automated Evaluation Pipeline

For rigorous testing, use the automated eval pipeline:

# Run evaluation with baseline comparison
python ~/.claude/skills/bgskillz/scripts/run_eval.py /path/to/skill --prompts tests/prompts.json

# Run automated improvement loop (eval -> grade -> analyze -> improve -> repeat)
python ~/.claude/skills/bgskillz/scripts/run_loop.py /path/to/skill --prompts tests/prompts.json --iterations 3 --auto-apply

# Optimize description triggering
python ~/.claude/skills/bgskillz/scripts/improve_description.py /path/to/skill

# Generate a self-contained HTML review page
python ~/.claude/skills/bgskillz/eval-viewer/generate_review.py /path/to/workspace/iteration-1/evals.json

The eval pipeline runs each test prompt through Claude with and without the skill, computing benchmark statistics (mean, stddev, min, max) and saving outputs for grading. Use the sub-agents in agents/ to grade outputs, blind-compare them, and analyze patterns:

  • agents/grader.md — Grades outputs against assertions with evidence and meta-evaluation
  • agents/comparator.md — Blind A/B comparison (doesn't know which output is skill vs. baseline)
  • agents/analyzer.md — Unblinded pattern analysis with prioritized improvement suggestions

The run_loop.py script automates the full cycle: eval → grade → analyze → apply suggestions → re-eval. Use --auto-apply to let it modify SKILL.md between iterations (backups are saved).

Review results visually with eval-viewer/viewer.html, or generate a self-contained review page with eval-viewer/generate_review.py. See references/schemas.md for all data formats.

Iteration Signals

Undertriggering: Users have to explicitly invoke the skill; paraphrased requests don't activate it. Fix: Add more trigger phrases to the description. Be more specific about use cases.

Overtriggering: Skill activates on unrelated tasks. Fix: Add negative triggers. Narrow the description scope. Use more specific terminology.

Anti-overfitting warning: When iterating, read the actual transcripts. Look for unproductive behavior the skill causes and look for repeated work across test runs (if all runs independently write similar scripts, bundle that script into the skill). Resist adding fiddly constraints — generalize from the feedback instead.

For comprehensive testing methodology, read references/testing-methodology.md.

Troubleshooting

Skill won't upload: Check that the file is named exactly SKILL.md. Verify frontmatter is valid YAML with name and description. Ensure the name is kebab-case with no spaces.

Skill doesn't trigger: Your description likely doesn't match how users phrase requests. Add more specific trigger phrases. Include the exact verbs and nouns users would say.

Instructions not followed: SKILL.md may be too long or instructions are buried. Front-load critical rules. Use bold for must-follow constraints. Reduce total word count.

For all troubleshooting scenarios, read references/troubleshooting.md.

Audit Checklist

Quick pre-flight check before publishing:

  • SKILL.md exists with exact casing
  • Frontmatter has name (kebab-case) and description (under 1024 chars)
  • Name matches folder name
  • Description follows [What] + [When] + [Capabilities] formula
  • No README.md inside the skill folder
  • SKILL.md is under 500 lines / 5000 words
  • All referenced files actually exist
  • Scripts are executable and handle errors
  • Tested with 3+ trigger phrases and 2+ non-trigger phrases
  • Baseline comparison shows improvement over vanilla Claude

For the full audit rubric with scoring, read references/quality-checklist.md.

What To Do

Choose what you need help with:

Create a New Skill

"I want to create a new skill" — Walk through the 7-step creation workflow. Start by defining use cases and end with a packaged, validated skill.

Audit an Existing Skill

"Audit my skill" or "Review this skill" — Run the full quality checklist against an existing skill. Identify issues and suggest fixes.

Improve a Description

"Help me write a better description" — Apply the description formula and test trigger phrases. Rewrite for maximum activation reliability.

Add a Component

"Add a script/reference/asset to my skill" — Help plan and implement a new component (validator, reference doc, template, etc.) for an existing skill.

Validate a Skill

"Validate my skill" — Run the validation script to check structural correctness:

python ~/.claude/skills/bgskillz/scripts/validate_skill.py /path/to/skill

Package for Distribution

"Package my skill" — Run validation + create a distributable zip:

python ~/.claude/skills/bgskillz/scripts/package_skill.py /path/to/skill

Evaluate a Skill

"Evaluate my skill" or "Run evals" — Run the automated evaluation pipeline with baseline comparison:

python ~/.claude/skills/bgskillz/scripts/run_eval.py /path/to/skill --prompts tests/prompts.json

Then grade and analyze results using the agents in agents/. Review visually with eval-viewer/viewer.html or generate a self-contained review page with eval-viewer/generate_review.py.

Run Improvement Loop

"Iterate on my skill" or "Auto-improve my skill" — Run the full automated cycle (eval → grade → analyze → improve → re-eval):

python ~/.claude/skills/bgskillz/scripts/run_loop.py /path/to/skill --prompts tests/prompts.json --iterations 3 --auto-apply

Optimize Triggering

"Improve my skill's triggering" — Run the description optimization pipeline:

python ~/.claude/skills/bgskillz/scripts/improve_description.py /path/to/skill

Get Guidance

"How do I..." — Answer questions about skill building using the reference library. Topics: descriptions, workflows, testing, evaluation, troubleshooting, distribution, quality.

平台分布

Codex

33.96%
按下载量换算22

Claude

27.01%
按下载量换算18

Cursor

20.67%
按下载量换算13

Gemini CLI

9.73%
按下载量换算6

安全审计

Gen Agent Trust Hub

可疑

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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