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Agent Skill · 研究检索

lovstudio%3askill-optimizer(lovstudio%3akill 优化器)

lovstudio%3askill-optimizer 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。

lovstudio%3askill-optimizer 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,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 51,787。

总安装 574周安装 23GitHub Stars 45下载 186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:lovstudio%3askill-optimizer(lovstudio%3akill 优化器)
来源仓库:https://github.com/lovstudio/skills
仓库路径:skills/lovstudio%3Askill-optimizer
安装命令:
npx skills add https://github.com/lovstudio/skills --skill lovstudio:skill-optimizer
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/lovstudio/skills --skill lovstudio:skill-optimizer

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。

lovstudio%3askill-optimizer 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词或任务场景快速定位候选结果。通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。

安装前需确认权限范围和维护状态,注意可能触发联网、命令执行或文件读写操作。建议结合原始 README 和仓库内容进一步核验具体功能和用法边界。

来源仓库内路径:skills/lovstudio%3Askill-optimizer

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/lovstudio/skills --skill lovstudio:skill-optim…;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

lovstudio%3askill-optimizer 怎么安装?

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

lovstudio%3askill-optimizer 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。

lovstudio%3askill-optimizer 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。

lovstudio%3askill-optimizer 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。

lovstudio%3askill-optimizer 用的人多吗?

公开数据是累计安装 574 次,近一周安装 23 次,下载 186 次,GitHub 45 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。

lovstudio%3askill-optimizer 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

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。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 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。
  • 要求有安全扫描结论才肯安装的团队。
  • 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
lovstudio%3askill-optimizer

lovstudio%3akill 优化器 · 本页

57445只读未明确未收录

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-optimizer — 自动优化 lovstudio skill 并维护版本与 changelog

Runs a lint → fix → bump → changelog pipeline on an existing skill in this repo. Fully automatic — no interactive prompts. Every run produces a concrete version bump and a new CHANGELOG.md entry so optimization history is traceable.

When to Use

  • User mentioned a concrete problem with an existing skill in this conversation (wrong trigger phrases, stale CLI flags, missing README badge, etc.) and wants it fixed.
  • User asks to "audit", "polish", "refine", "刷一遍" an existing skill.
  • User explicitly asks to bump a skill's version or update its changelog.
  • Proactively after another skill has been modified meaningfully — run this to record the change with a version bump.

Workflow (MANDATORY, fully automatic)

Do not ask the user for options. Infer everything from (a) the target skill name and (b) any optimization notes mentioned in the current conversation.

Step 1: Identify target & context

From the user's message, extract:

  1. Target skill name — e.g. any2pdf, lovstudio-any2pdf, or lovstudio:any2pdf. Normalize to the bare name (strip prefix). If the user did not name a skill explicitly, infer it from recent conversation context (the skill they were just working on). If still ambiguous, ask one targeted question.
  2. Context-driven fix list — scan the current conversation for issues the user raised about this skill: wrong flags, trigger misfires, broken CJK rendering, missing options, confusing README, etc. This list is the primary driver. The generic lint only supplements it.

Step 2: Lint

python3 skills/lovstudio-skill-optimizer/scripts/lint_skill.py <name> --json

Parse the JSON findings. Combine with the context-driven fix list from Step 1. Prioritize in this order:

  1. Fixes the user explicitly mentioned in conversation (highest priority)
  2. Lint error findings
  3. Lint warn findings
  4. Lint info findings — apply only if cheap and safe

Step 3: Apply fixes directly

Edit SKILL.md, README.md, scripts/*.py with the Edit tool based on the prioritized fix list. Guidelines:

  • SKILL.md frontmatter description: make sure it covers what + when + concrete trigger phrases (中文 + English). Don't bloat it; keep under ~800 chars.
  • CLI args in scripts: if the user hit a bug with a specific flag, fix the root cause — don't paper over it.
  • Progressive disclosure: if SKILL.md body > 500 lines, split the largest section to references/<topic>.md.
  • Don't write tests or docs that weren't asked for. The CHANGELOG entry IS the documentation of the change.
  • Don't add emojis unless the original file already uses them consistently.

Step 4: Decide bump type

Choose semver bump based on the fixes applied:

BumpUse when
patchbug fix, wording fix, frontmatter tweak, CJK rendering fix
minornew CLI flag, new option, new reference doc, expanded scope
majorbreaking CLI change, removed option, renamed skill

Stay in 0.x unless explicitly told otherwise — per repo release conventions.

Step 5: Bump version + write changelog

python3 skills/lovstudio-skill-optimizer/scripts/bump_version.py <name> \
  --type <patch|minor|major> \
  --message "<one-line summary of the biggest change>" \
  --change "<additional bullet>" \
  --change "<additional bullet>"

This updates:

  • README.md version badge (source of truth)
  • SKILL.md frontmatter metadata.version (kept in sync if present)
  • CHANGELOG.md — prepends a new entry with today's date (creates the file if missing)

Step 6: Re-lint & report

python3 skills/lovstudio-skill-optimizer/scripts/lint_skill.py <name>

Report to the user, in this exact shape and nothing more:

optimized: lovstudio-<name>
version:   <old> → <new>
fixes:
  - <bullet 1>
  - <bullet 2>
remaining lint warnings: <count>  (or "none")

Do not print a trailing summary, self-congratulation, or next-step suggestions. The diff speaks for itself.

Step 7: Commit, push & sync all locations

Skills live in three locations that must stay in sync:

source (edit here):  ~/projects/lovstudio-skills/        → lovstudio/skills
claude reads from:   ~/.claude/skills/*                   → symlinks to source
distribution repo:   ~/projects/lovstudio-pro-skills/     → lovstudio/pro-skills

7a. Commit & push to source repo:

cd ~/projects/lovstudio-skills
git add skills/lovstudio-<name>/
git commit -m "fix(<name>): <one-line summary>"
git push
  • Commit message follows repo convention: fix|feat|docs(<skill-name>): <summary>
  • Use fix for patch, feat for minor, feat! for major

7b. Sync to pro-skills distribution repo:

The two repos have independent git histories (not forkable), so sync via file copy:

SRC=~/projects/lovstudio-skills/skills/lovstudio-<name>
DST=~/projects/lovstudio-pro-skills/skills/lovstudio-<name>
rsync -av --delete "$SRC/" "$DST/"
cd ~/projects/lovstudio-pro-skills
git add skills/lovstudio-<name>/
git commit -m "sync(<name>): <version> from lovstudio/skills"
git push origin main

~/.claude/skills/ is already up-to-date via symlinks — no action needed.

If any step fails, report the sync state to the user rather than silently skipping. A partial sync (source updated but pro-skills stale) is the exact problem this step exists to prevent.

CLI Reference

lint_skill.py

ArgumentDefaultDescription
<name>—Skill name (with or without lovstudio- prefix)
--path—Absolute path to skill dir (overrides name)
--jsonoffEmit findings as JSON

Exit code: 2 if any error-severity finding, 0 otherwise.

bump_version.py

ArgumentDefaultDescription
<name>—Skill name
--path—Absolute path to skill dir (overrides name)
--type—patch \minor \major (mutually exclusive with --set)
--set—Explicit version e.g. 0.2.0
--message, -mrequiredPrimary changelog bullet
--change, -c—Additional bullet (repeatable)
--dry-runoffShow what would change without writing

Dependencies

Python 3.8+ (stdlib only, no pip install needed).

平台分布

Codex

36.84%
按下载量换算69

Claude

27.41%
按下载量换算51

Cursor

19.39%
按下载量换算36

Gemini CLI

8.99%
按下载量换算17

安全审计

暂无安全审计结果可展示。

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。目前只有一个公开来源,建议同时核对源仓库。

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