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Agent Skill · 研究检索

build-skill(培养技能)

build-skill 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。

build-skill 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,权限为只读;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 23,589。

总安装 4,234周安装 180GitHub Stars 公开资料未说明下载 1,483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:build-skill(培养技能)
来源仓库:https://github.com/camacho/ai-skills
仓库路径:skills/build-skill
安装命令:
npx skills add https://github.com/camacho/ai-skills --skill build-skill
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/camacho/ai-skills --skill build-skill

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。

build-skill 通过并行运行三种方法在十步流程中构建或修订 AI 技能。适用于新技能开发或现有技能重构,强调基于证据而非直觉的设计原则。自动解析技能名称与作用描述,调用完整 pipeline 进行验证与输出。需明确传入参数格式,建议在模糊时发起一次澄清提问后再执行构建。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/build-skill

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/camacho/ai-skills --skill build-skill;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

build-skill 怎么安装?

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

build-skill 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。

build-skill 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

build-skill 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。

build-skill 用的人多吗?

公开数据是累计安装 4,234 次,近一周安装 180 次,下载 1,483 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。

build-skill 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。
  • 标注为只读权限,试用阶段不容易误改本地文件。

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。
  • 只想先试试效果、不希望它改动本地文件的人。

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
build-skill

培养技能 · 本页

4,234—只读未明确有提醒

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

Build Skill

Build skills by running 3 competing approaches in parallel inside the full 10-step workflow pipeline.

Args

The args string is the skill specification. It should contain:

  • The skill name (required)
  • What the skill does, its modes/subcommands, and any context needed

Example: /build-skill audit-permissions — wraps a TypeScript analyzer, default mode runs report, reset mode archives logs

If args are vague, ask one clarifying question before proceeding. Don't over-interview.

Steps

Step 1 — Orient

Extract from args:

  • Skill name (first word or hyphenated phrase before any separator)
  • Skill purpose (everything else)
  • Scope: user-level (~/.claude/skills/) or project-level (.claude/skills/) — default user-level unless the spec mentions a specific project
  • Rules-encoding signal: does the spec describe governance rules, policies, or decision algebra? If YES, set RULES_ENCODING=true — this triggers policy algebra integration in Step 5.

Present a one-line brief: Building skill [name]: [purpose]. Scope: [user|project]-level.

Step 2 — Isolate

Check if already in a worktree (git rev-parse --git-dir shows .git inside .worktrees/).

  • Already in a worktree: proceed.
  • Not in a worktree: invoke /isolate to create one before writing any files.

Step 3 — Design

Invoke /brainstorming with the skill spec. One round only — skill specs are usually clear enough for a single pass. Do not recurse into multi-round brainstorming.

Output: confirmed spec + any refinements. Proceed to Step 4 immediately.

Step 4 — Review

Write a lightweight plan to ai-workspace/plans/build-skill-<name>.md:

  • Spec (from Step 1)
  • Approach: 3 parallel builders (native, superpowers, manual)
  • Expected output: final SKILL.md at target path

Invoke /plan-review with the technical-editor only, 1 round max. This is a skill document, not architecture — skip architect-reviewer and security-auditor.

Proceed to Step 5 on APPROVE. Revise plan and re-submit once on REVISE. Escalate to human if still blocked.

Step 5 — Build

This is where the parallel-build pattern runs.

Pre-build: Policy Algebra (if RULES_ENCODING=true)

If the skill encodes governance rules (detected in Step 1), invoke /policy-algebra in SHALLOW mode against the skill spec to generate a frozen Starlark block. This block becomes a contract that all 3 builders must honor:

  1. Run /policy-algebra <spec-file-or-inline> to produce the frozen block
  2. Append the frozen block to each builder agent's prompt: "The skill MUST include this exact frozen governance block in a ## Governance section. Do not modify the block."
  3. Record the block for post-build verification

If /policy-algebra fails or produces <2 invariants, skip algebra integration and proceed with normal build. Log a warning.

Create a unique working directory: SKILL_TMP=$(mktemp -d -t skill-compare-XXXXXX) then create $SKILL_TMP/{native,superpowers,manual}/ subdirectories. Then launch 3 background agents simultaneously via the Agent tool. Each gets the SAME spec but a DIFFERENT approach:

Agent 1 — "native-builder": Invoke skill-creator:skill-creator via the Skill tool, then follow its process. Write to $SKILL_TMP/native/SKILL.md.

Agent 2 — "superpowers-builder": Invoke superpowers:writing-skills via the Skill tool, then follow its structural guidance (skip live subagent pressure testing but follow CSO, token efficiency, frontmatter, and checklist). Write to $SKILL_TMP/superpowers/SKILL.md.

Agent 3 — "manual-builder": No skill-building guide. Write the SKILL.md using general best practices and intuition only. Write to $SKILL_TMP/manual/SKILL.md.

All agents must be told:

  • Write ONLY to their $SKILL_TMP/<approach>/SKILL.md path
  • Do NOT write to ~/.claude/skills/ or .claude/skills/
  • The skill spec (passed through verbatim from args)
  • Brief context on what a Claude Code skill is (YAML frontmatter with name + description, markdown body with instructions)

Compare results. Once all 3 complete, read all 3 files and score on these dimensions:

DimensionWhat to evaluate
DiscoverabilityDoes the description help Claude find it? Trigger-only (good) vs workflow summary (bad per CSO)?
ClarityCan Claude follow instructions unambiguously? Are steps numbered?
CompletenessAll modes covered? Edge cases? Troubleshooting?
Token efficiencyWord count vs information density. Target: <500 words for non-startup skills
ActionabilityConcrete actions vs vague guidance? Explicit guardrails for failure modes?

Present a comparison table with word counts, token costs, and per-dimension winners.

Synthesize. Cherry-pick the best elements from each approach into a final skill. For each element kept, note which approach it came from and why. Write the final skill to the target location:

  • User-level: ~/.claude/skills/<name>/SKILL.md
  • Project-level: .claude/skills/<name>/SKILL.md

Post-build: Drift Check (if RULES_ENCODING=true)

After writing the final synthesized SKILL.md, verify the frozen block survived synthesis:

  1. Run /policy-algebra --verify <final-skill-path> against the frozen block from pre-build
  2. Exit code 0 (MATCH): proceed to Step 6
  3. Exit code 1 (DRIFT): the synthesis mutated the governance block — re-inject the original frozen block and re-verify
  4. Exit code 2/3: log error, proceed without algebra (degraded mode)

Step 6 — Verify

Run pnpm validate to confirm no breakage. If the skill is project-level, also verify it appears in the skills list in the system reminder after writing.

Report final word count and what was taken from each approach.

Step 7 — Archive

Fill Outcomes & Learnings in ai-workspace/plans/build-skill-<name>.md. Invoke /archive to rename the plan to .done.md.

Step 8 — Ship

Invoke /ship — PR or local merge depending on session type.

Step 9 — Reflect

Invoke /reflect to consolidate learnings to MEMORY.md.

Policy Algebra Integration

When a skill encodes governance rules (detected by the RULES_ENCODING signal in Step 1), the build process integrates /policy-algebra to ensure rule fidelity:

Orient ──→ rules detected? ──YES──→ /policy-algebra SHALLOW
                │                         │
                NO                    frozen block
                │                         │
                ▼                         ▼
           normal build          inject into 3 builders
                                         │
                                         ▼
                                    synthesize
                                         │
                                         ▼
                                  /policy-algebra --verify
                                         │
                                    MATCH? → Step 6
                                    DRIFT? → re-inject + retry

Skills that do NOT encode rules skip this entirely — no overhead.

Known Patterns from Prior Runs

These patterns consistently emerge — use them to inform the merge:

  • Superpowers excels at: merge guardrails, CSO-compliant descriptions, cross-surface compatibility, explicit failure-mode prevention
  • Manual excels at: unique safety guardrails humans think of, natural "done" summary steps, concise structure
  • Native (skill-creator) excels at: comprehensive coverage, but tends to over-explain internals Claude doesn't need — trim aggressively
  • Your own judgment matters most for: token efficiency and cutting bloat

平台分布

Codex

34.03%
按下载量换算505

Claude

30.17%
按下载量换算447

Cursor

18.91%
按下载量换算280

Gemini CLI

8.32%
按下载量换算123

安全审计

Gen Agent Trust Hub

通过

Socket

可疑

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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