skill-master

skill-master is a skill for Claude Code, Codex from Jiang-Yude/skill-master. It costs 322 tokens per session (7,573 once invoked), scanned A, original, Apache-2.0.

A Chinese-language toolkit for managing coding-agent skills throughout their lifecycle, from creating and organizing them to testing and improving them. It supports several workflows for simple and complex skill projects.

In plain words
What is it for?
Use it to create skills, turn existing material into a skill, maintain complex skills, run evaluations, and improve when a skill is triggered.
Why use it?
It provides a defined way to build, maintain, evaluate, and adjust skills instead of handling those tasks ad hoc.

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/jiang-yude/skill-master/skill-master
Any agent
npx skills add Jiang-Yude/skill-master --skill skill-master
Clone the repo
git clone --depth 1 https://github.com/Jiang-Yude/skill-master

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for skill-master

README.md
[![agentmods](https://agentmods.dev/badge/skills/jiang-yude/skill-master/skill-master.svg)](https://agentmods.dev/skills/jiang-yude/skill-master/skill-master)
Your own site
<a href="https://agentmods.dev/skills/jiang-yude/skill-master/skill-master"><img src="https://agentmods.dev/badge/skills/jiang-yude/skill-master/skill-master.svg" alt="Measured on agentmods" height="20"></a>
Per session 322 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,573 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 5.1 $0.00322 $0.07573
Opus 5 $0.00161 $0.03786
Sonnet 5 $0.00064 $0.01515
Haiku 4.5 $0.00032 $0.00757

Measured 5d ago against content hash 44ad8988b41b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

skill-master 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 5d 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.

SKILL.md · 581 lines

How it starts

The opening of the file, as written. The whole thing — 581 lines — stays where its author put it; the contents beside it link to each section on GitHub.

技能包大師 公開版 v1.0

中文版技能包全生命週期管理系統。整合 Anthropic 官方 skill-creator + 中文化處理 + 規格驅動開發 + 評估與優化 + 觸發調校 + 技能包寫作指引。

目標:讓你做出第一個可用的技能包,並具備測試與調校的能力。


五種工作模式總覽

情境 推薦模式 階段
快速做一個簡單技能包 模式一:引導探索型
有完整內容要整理 模式二:專業整理型
複雜技能包或需長期維護 模式三:規格驅動型
技能包做好了,要驗證品質 模式四:評估與優化
技能包沒被正確觸發 模式五:觸發調校 調

完整生命週期:建 → 測 → 調 → 再測 → 再調 → ...


技能包標準檔案結構

所有技能包一律採用以下結構:

skill-name/
├── SPEC.md           ← 設計圖(規格驅動模式使用,選填)
├── SKILL.md          ← 主檔案(必要)
├── agents/openai.yaml ← OpenAI / Codex 類環境的 UI metadata(建議)
├── references/       ← 參考文件(按需載入)
├── scripts/          ← 可執行腳本(確定性/重複性任務)
└── assets/           ← 模板、圖示、字型等靜態資源

SPEC.md 不是每個技能包都需要。簡單技能包直接寫 SKILL.md 就好。複雜的、會長期迭代的才需要 SPEC.md。

如果技能包要拿去不同 Agent 環境獨立運作,建議補上 agents/openai.yaml。它不是技能本體的必要條件,但能讓 OpenAI / Codex 類介面正確顯示名稱、短描述與預設提示詞。


技能包寫作指引

這一節定義「怎麼寫出好的技能包」,適用於所有模式產出的技能包。

Progressive Disclosure:三層載入

技能包用三層式載入,控制 context 佔用:

層級 內容 何時載入 長度建議
第一層 name + description 永遠在 context 裡 約 100 字
第二層 SKILL.md 本體 技能包被觸發時 500 行以內(理想值,可超過但要有意識控制)
第三層 references/ 裡的文件 需要時才讀 不限,但單檔超過 300 行建議加目錄

設計原則:SKILL.md 放「什麼時候做什麼」的決策邏輯,references/ 放「怎麼做」的操作細節。使用者不需要每次都讀完所有 references/,SKILL.md 裡面要寫清楚「什麼時候去讀哪個檔案」。

按領域拆分 references/ 的範例:

cloud-deploy/
├── SKILL.md(流程 + 選擇邏輯)
└── references/
    ├── aws.md
    ├── gcp.md
    └── azure.md

Claude 只讀跟當前任務相關的那一個。

自由度設計:不要每次都寫到一樣細

技能包不是越細越好,而是要根據任務的脆弱程度,決定要給模型多大自由度。

三種自由度:

  1. 高自由度:用文字原則引導。適合情境差異大、沒有唯一正解、需要現場判斷的任務。
  2. 中自由度:用偽代碼、模板、可調參數腳本。適合有偏好做法,但仍需依情境調整的任務。
  3. 低自由度:用固定腳本、固定步驟、少量參數。適合容易出錯、順序不能亂、穩定性優先的任務。

判斷原則:

  • 任務像走平地,路很多都能到 → 高自由度
  • 任務像有欄杆的山路,需要方向但不是唯一走法 → 中自由度
  • 任務像窄橋,走錯一步就掉下去 → 低自由度

寫技能包前先問自己:這件事最怕的是「做不出來」,還是「做錯一步整個壞掉」?越怕出錯,越要收斂自由度。

先規劃可重用資源,再寫 SKILL.md

不要一開始就衝去寫 SKILL.md。先看 2-3 個真實案例,判斷哪些東西值得抽成可重用資源。

最小資源規劃表:

資源類型 什麼時候該做 例子
scripts/ 同樣的程式碼會重寫,或任務需要高確定性 rotate_pdf.py、clean_csv.py
references/ 細節很多,但不是每次都要載入 schema.md、api-notes.md
assets/ 最終輸出會直接用到的靜態檔案 ppt 模板、logo、前端骨架

Read the full file on GitHub · 581 lines

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. 5d ago First seen · 581 lines · 322 tokens per session scan A 44ad8988b41b

Subscribe to this mod's changes

skill-master is a skill published in the GitHub repository Jiang-Yude/skill-master (23 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 322 tokens to every session and 7,573 once invoked, about $0.0016 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 2026-08-30.

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