Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add seaworld008/Commonly-used-high-value-skills --skill find-skillsgit clone --depth 1 https://github.com/seaworld008/Commonly-used-high-value-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/find-skills)<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/find-skills"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/find-skills/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/seaworld008/commonly-used-high-value-skills/find-skills"><img src="https://agentmods.dev/badge/skills/seaworld008/commonly-used-high-value-skills/find-skills.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 23 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 32 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 60 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00040 | $0.01724 |
| Opus 5 | $0.00020 | $0.00862 |
| Sonnet 5 | $0.00008 | $0.00345 |
| Haiku 4.5 | $0.00004 | $0.00172 |
Grade A, and why
find-skills scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Find Skills
当用户只描述目标、但未指定具体工具或技能时，优先使用本技能做自动匹配。Find Skills 是 Agent 的“大脑插件中心”，它能理解用户意图，并从浩如烟海的 ClawHub 仓库中精准定位、安装并配置合适的技能。
安装
npx clawhub@latest install find-skills
工作方式
- 语义理解 (Semantic Extraction)：根据用户复杂、模糊的目标，生成 3-5 个核心技能检索词（如 “SQL optimization”, “React chart libraries”）。
- 多源检索 (Multi-Source Search)：在 ClawHub 官方仓库、本地已安装列表及相关开源索引中搜索候选技能。
- 优先级排序 (Prioritization)：根据技能的评分、下载量、更新频率以及与当前任务的匹配度进行综合排序。
- 决策与理由 (Reasoning)：为前 3 名技能给出详细的推荐安装理由。
- 按需执行 (On-Demand Execution)：按用户确认结果或自主决策执行
npx clawhub@latest install <skill-name>。
触发条件 / When to Use
- 冷启动场景：新项目开始，用户提出了一个 Agent 之前没处理过的任务类型（如“帮我写一个 Chrome 插件”）。
- 工具链补强：Agent 发现现有技能集无法完成任务，提示缺失必要的工具（如缺失
image-edit时）。 - 优化替代方案：现有技能效率低下或报错，需要寻找功能类似的“增强版”技能。
- 批量环境部署：需要为一套复杂的工程方案（如云原生架构）一键安装全套开发、运维技能。
- 自动升级检测：当用户询问最新功能时，Agent 自动搜索是否有对应的 Beta 版技能可用。
核心能力 / Core Capabilities
1. 精准意图映射 (Intent Mapping)
- 操作步骤：
- 接收到原始 Prompt。
- 调用内部 LLM 对 Prompt 进行“原子任务”拆解。
- 将拆解后的原子任务（如“PDF parsing”, “OCR”, “Summarization”）映射为标准技能标签。
- 最佳实践：生成检索词时，应包含“动词 + 名词”结构，如
parse-xlsx而非仅仅是xlsx。
2. 候选技能对比评估 (Skill Benchmarking)
- 操作步骤：
- 调用
list_agent或搜索接口。 - 提取每个候选技能的
description和metadata。 - 建立评分矩阵（功能覆盖度、稳定性、易用性）。
- 调用
- 最佳实践：优先推荐那些有
verified标签或在大厂生产环境验证过的技能。
3. 一键环境就绪 (Zero-Config Readiness)
- 操作步骤：
- 确认目标技能后，自动执行
npx clawhub@latest install <skill-name>。 - 检查安装后的
README.md，识别是否需要环境变量（ENV）或 OAuth 授权。 - 如果需要，主动提示用户配置，并使用当前客户端已连接的原生授权流程；不要假设存在特定第三方连接命令。
- 确认目标技能后，自动执行
- 最佳实践：安装完成后，自动运行一个
hello world级别的测试指令，确保技能真实可用。
4. 依赖项解析与递归安装
- 操作步骤：
- 分析目标技能的依赖链。
- 自动补充安装底层依赖（如
puppeteer,ffmpeg等底层二进制工具）。
常用命令/模板 / Common Patterns
智能技能匹配模板 (Matching Template)
### 用户目标
[描述：实现一个能够自动分析 XHS 数据并生成周报的机器人]
### 识别出的关键词 (Generated Keywords)
- `xiaohongshu-crawler`
- `data-analysis-pandas`
- `markdown-report-generator`
- `chart-visualizer`
### 推荐技能组合 (Recommended Stack)
1. **xhs-analyzer** (评分 4.8): 包含成熟的爬虫逻辑和反爬策略。
2. **super-report** (评分 4.5): 支持将 JSON 直接转化为美观的 Markdown。
3. **quick-chart** (评分 4.9): 能够生成静态图片图表并插入报告。
### 是否立即安装前两项？ (Confirm Installation?)
> [Yes] / [No] / [Tell me more]
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 128 lines · 40 tokens per session scan A 2b14eec09d81
find-skills is a skill published in the GitHub repository seaworld008/Commonly-used-high-value-skills (70 stars, last pushed 5d ago), licensed MIT. It adds 40 tokens to every session and 1,724 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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