SkillFather

A Python command-line tool that reviews whether an agent skill fits a user's needs, setup, workflow, and documentation.

In plain words
What is it for?
It is for assessing skills used with WorkBuddy, CodeBuddy, Codex, Claude Code, Coze, and Hermes Agent.
Why use it?
It helps decide whether a skill is suitable before relying on it, while leaving security checks out of the review.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/alpsmonkey/skillfather/data
Any agent
npx skills add alpsmonkey/SkillFather --skill data
Clone the repo
git clone --depth 1 https://github.com/alpsmonkey/SkillFather

Made for: Claude Code, Codex.

Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,516 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00097 $0.02516
Opus 5 $0.00048 $0.01258
Sonnet 5 $0.00019 $0.00503
Haiku 4.5 $0.00010 $0.00252

Measured yesterday against content hash 1184426e307b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

SkillFather 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 yesterday.

The scan reads SKILL.md. This mod also ships 1 executable file (__init__.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

100% identical to SkillFather — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

src/skillfather/data/SKILL.md · 246 lines

How it starts

The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SkillFather - Multi-Platform Agent Skill 适配度分析工具

定位

SkillFather 从使用角度分析一个 Agent Skill 是否适用于当前用户。 仅做适用性评审,不对安全性负责。

评分维度

维度 权重 说明
用例契合度 25% Skill 用途是否匹配用户工作场景
环境就绪度 20% 所需工具、API、平台是否就绪
前置条件 20% 依赖、配置、权限是否满足
工作流匹配 20% 是否融入现有工作流程
文档质量 15% README 完整度、可操作性

使用方式

SkillFather 是一个 Python CLI 工具,本技能指导 Agent 如何调用它。

前置条件

  • Python 3.10+ 已安装
  • 已执行 pip install git+https://github.com/alpsmonkey/SkillFather.git(或本地 pip install -e .
  • 安装后命令 python -m skillfather 可用

核心流程:选择分析模式

当用户要求分析某个 Skill 时,Agent 必须先让用户选择分析模式,再执行对应的流程。

Step 0:确定 Skill 文件路径

  • 如果用户提供了 Skill 路径(文件或目录),直接使用
  • 如果用户引用了 @skill:xxx,从 WorkBuddy skills 目录查找:~/.workbuddy/skills/xxx/SKILL.md
  • 如果用户提供 GitHub URL,提示用户先 clone 到本地

Step 1:让用户选择分析模式

使用 AskUserQuestion 工具向用户展示以下选项:

模式 说明 适用场景
基于记忆分析 Agent 结合自身记忆(已装技能、工具环境、用户画像)个性化解读评分 想要最贴合自己的分析结果
交互分析 Agent 逐题提问,用户根据实际情况回答,得到精确评分 首次分析、需要深度了解
自动分析 基于 Skill 内容特征自动估算评分,快速出结果 快速预览、不需要个性化

Step 2:根据用户选择执行对应流程


模式一:基于记忆分析

此模式利用 Agent 自身的记忆和上下文来个性化解读评分。

执行步骤:

  1. 收集上下文:Agent 读取以下信息构建用户画像:

    • 读取 workspace 记忆文件 .workbuddy/memory/MEMORY.md 和当日日志
    • 列出用户已安装的技能(~/.workbuddy/skills/ 目录)
    • 检查用户常用的工具和环境(通过记忆中的偏好信息)
    • 读取 ~/.workbuddy/MEMORY.md 获取跨项目偏好
  2. 运行 CLI 分析

    python -m skillfather analyze <skill_path> --format console
    
  3. 个性化解读:Agent 基于收集到的上下文,对 CLI 输出的每个维度评分进行个性化解读:

    • 用例契合度:结合用户日常工作内容(如 SAP 分析、前端开发等)判断该 Skill 是否匹配
    • 环境就绪度:结合用户已装技能和工具,判断所需依赖是否就绪
    • 前置条件:结合用户记忆中的配置信息(API Key、已连接的服务等)
    • 工作流匹配:结合用户的工作习惯和已用工具链,判断是否融入
    • 文档质量:Agent 自行评估文档的完整性和可操作性
  4. 输出格式:Agent 以 Markdown 表格 + 文字解读的方式呈现最终结论,每个维度给出:

    • 原始分数(CLI 输出)
    • 个性化调整理由(基于记忆)
    • 最终建议分数
  5. 总结:给出是否推荐安装的结论,以及需要特别注意的前置条件或适配事项


模式二:交互分析

此模式由 Agent 逐题提问,用户回答后计算精确评分。

执行步骤:

  1. 运行 CLI 分析获取题目
    python -m skillfather analyze <skill_path> --format console
    
    记录 CLI 输出的所有诊断问题。

Read the full file on GitHub · 246 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. yesterday First seen · 246 lines · 97 tokens per session scan A 1184426e307b

Subscribe to this mod's changes

SkillFather is a skill published in the GitHub repository alpsmonkey/SkillFather (2 stars, last pushed 21d ago), licensed MIT. It adds 97 tokens to every session and 2,516 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to SkillFather, differing in 0 lines, and is treated as a copy.

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