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- 需简单配置
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- 不需要
- 兼容的系统
- 未声明(默认跨平台)
- 底层运行要求
- 无特殊要求
- 文件与系统权限
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- 只读
- 允许写入 / 修改
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- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: skillify
description: | You are capturing this session's repeatable process as a reusable skill. Before asking any que…
category: 通用
runtime: 无特殊运行时
---
# skillify 输出预览
## PART A: 任务判断
- 适用问题:通用任务拆解、检查和交付。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Your Task / Step 1: Analyze the Session / Step 2: Interview the User”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于通用任务拆解、检查和交付,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Your Task / Step 1: Analyze the Session / Step 2: Interview the User”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文出现了 `/skillify` 这类斜杠命令;如果你的 Agent 支持命令触发,优先用命令开场,再补充目标和边界。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“Your Task / Step 1: Analyze the Session / Step 2: Interview the User”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: skillify
description: | You are capturing this session's repeatable process as a reusable skill. Before asking any que…
category: 通用
source: ZhangHanDong/harness-engineering-from-cc-to-ai-coding
---
# skillify
## 什么时候使用
- 用于把稳定流程沉淀成可复用 Skill 适合处理通用任务拆解、检查、交付和复盘,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查、可继续迭代的步骤;通常不需要额外 API Ke…
- 面向通用任务拆解、检查和交付,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Your Task / Step 1: Analyze the Session / Step 2: Interview the User」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "skillify" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Your Task / Step 1: Analyze the Session / Step 2: Interview the User
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} Skillify
Extracted from Claude Code v2.1.88 internal
/skillifyskill (originallyant-only).
You are capturing this session's repeatable process as a reusable skill.
Your Task
Step 1: Analyze the Session
Before asking any questions, analyze the full conversation to identify:
- What repeatable process was performed
- What the inputs/parameters were
- The distinct steps (in order)
- The success artifacts/criteria (e.g. not just "writing code," but "an open PR with CI fully passing") for each step
- Where the user corrected or steered you
- What tools and permissions were needed
- What agents were used
- What the goals and success artifacts were
Step 2: Interview the User
You will use the AskUserQuestion tool to understand what the user wants to automate. Important notes:
- Use AskUserQuestion for ALL questions! Never ask questions via plain text.
- For each round, iterate as much as needed until the user is happy.
- The user always has a freeform "Other" option to type edits or feedback -- do NOT add your own "Needs tweaking" or "I'll provide edits" option. Just offer the substantive choices.
Round 1: High level confirmation
- Suggest a name and description for the skill based on your analysis. Ask the user to confirm or rename.
- Suggest high-level goal(s) and specific success criteria for the skill.
Round 2: More details
- Present the high-level steps you identified as a numbered list. Tell the user you will dig into the detail in the next round.
- If you think the skill will require arguments, suggest arguments based on what you observed. Make sure you understand what someone would need to provide.
- If it's not clear, ask if this skill should run inline (in the current conversation) or forked (as a sub-agent with its own context). Forked is better for self-contained tasks that don't need mid-process user input; inline is better when the user wants to steer mid-process.
- Ask where the skill should be saved. Suggest a default based on context (repo-specific workflows -> repo, cross-repo personal workflows -> user). Options:
- This repo (
.claude/skills/<name>/SKILL.md) -- for workflows specific to this project - Personal (
~/.claude/skills/<name>/SKILL.md) -- follows you across all repos
- This repo (
Round 3: Breaking down each step For each major step, if it's not glaringly obvious, ask:
- What does this step produce that later steps need? (data, artifacts, IDs)
- What proves that this step succeeded, and that we can move on?
- Should the user be asked to confirm before proceeding? (especially for irreversible actions like merging, sending messages, or destructive operations)
- Are any steps independent and could run in parallel? (e.g., posting to Slack and monitoring CI at the same time)
- How should the skill be executed? (e.g. always use a Task agent to conduct code review, or invoke an agent team for a set of concurrent steps)
- What are the hard constraints or hard preferences? Things that must or must not happen?
You may do multiple rounds of AskUserQuestion here, one round per step, especially if there are more than 3 steps or many clarification questions. Iterate as much as needed.
IMPORTANT: Pay special attention to places where the user corrected you during the session, to help inform your design.
Round 4: Final questions
- Confirm when this skill should be invoked, and suggest/confirm trigger phrases too. (e.g. For a cherrypick workflow you could say: Use when the user wants to cherry-pick a PR to a release branch. Examples: 'cherry-pick to release', 'CP this PR', 'hotfix.')
- You can also ask for any other gotchas or things to watch out for, if it's still unclear.
Stop interviewing once you have enough information. IMPORTANT: Don't over-ask for simple processes!
Step 3: Write the SKILL.md
Create the skill directory and file at the location the user chose in Round 2.
Use this format:
---
name: {{skill-name}}
description: {{one-line description}}
allowed-tools:
{{list of tool permission patterns observed during session}}
when_to_use: {{detailed description of when Claude should automatically invoke this skill, including trigger phrases and example user messages}}
argument-hint: "{{hint showing argument placeholders}}"
arguments:
{{list of argument names}}
context: {{inline or fork -- omit for inline}}
---
# {{Skill Title}}
Description of skill
## Inputs
- `$arg_name`: Description of this input
## Goal
Clearly stated goal for this workflow. Best if you have clearly defined artifacts or criteria for completion.
## Steps
### 1. Step Name
What to do in this step. Be specific and actionable. Include commands when appropriate.
**Success criteria**: ALWAYS include this! This shows that the step is done and we can move on. Can be a list.
IMPORTANT: see the next section below for the per-step annotations you can optionally include for each step.
...
Per-step annotations:
- Success criteria is REQUIRED on every step. This helps the model understand what the user expects from their workflow, and when it should have the confidence to move on.
- Execution:
Direct(default),Task agent(straightforward subagents),Teammate(agent with true parallelism and inter-agent communication), or[human](user does it). Only needs specifying if not Direct. - Artifacts: Data this step produces that later steps need (e.g., PR number, commit SHA). Only include if later steps depend on it.
- Human checkpoint: When to pause and ask the user before proceeding. Include for irreversible actions (merging, sending messages), error judgment (merge conflicts), or output review.
- Rules: Hard rules for the workflow. User corrections during the reference session can be especially useful here.
Step structure tips:
- Steps that can run concurrently use sub-numbers: 3a, 3b
- Steps requiring the user to act get
[human]in the title - Keep simple skills simple -- a 2-step skill doesn't need annotations on every step
Frontmatter rules:
allowed-tools: Minimum permissions needed (use patterns likeBash(gh:*)notBash)context: Only setcontext: forkfor self-contained skills that don't need mid-process user input.when_to_useis CRITICAL -- tells the model when to auto-invoke. Start with "Use when..." and include trigger phrases. Example: "Use when the user wants to cherry-pick a PR to a release branch. Examples: 'cherry-pick to release', 'CP this PR', 'hotfix'."argumentsandargument-hint: Only include if the skill takes parameters. Use$namein the body for substitution.
Step 4: Confirm and Save
Before writing the file, output the complete SKILL.md content as a yaml code block in your response so the user can review it with proper syntax highlighting. Then ask for confirmation using AskUserQuestion with a simple question like "Does this SKILL.md look good to save?" -- do NOT use the body field, keep the question concise.
After writing, tell the user:
- Where the skill was saved
- How to invoke it:
/{{skill-name}} [arguments] - That they can edit the SKILL.md directly to refine it
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核