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Agent skill

Skillify

by ZhangHanDong in ZhangHanDong/harness-engineering-from-cc-to-ai-coding

Capture this session's repeatable process into a reusable skill.

MITAuto-check passed

Install Skillify

skills CLI
$ npx skills add ZhangHanDong/harness-engineering-from-cc-to-ai-coding --skill skillify -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ZhangHanDong/harness-engineering-from-cc-to-ai-coding skillify --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ZhangHanDong/harness-engineering-from-cc-to-ai-coding.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/skillify .claude/skills/skillify && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
skillify
GitHub stars
1.5k
Token cost
~1.9k tokens
SKILL.md length
934 words
Files
1
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Capture this session's repeatable process into a reusable skill.

  • Works in 4 steps: Analyze the Session → Interview the User → Write the SKILL.md → …
  • : user says make this a skill
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Capture this workflow

What it does

Skillify is an agent skill from ZhangHanDong/harness-engineering-from-cc-to-ai-coding. Capture this session's repeatable process into a reusable skill. Use when: user says "make this a skill", "capture this workflow", "skillify", "save this as a skill", "turn this into a skill", "create skill from session", "把这个流程变成技能", "提炼成skill", "保存为技能"

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Harness Engineering From Claude Code source code to AI Coding. The licence is MIT.

When your agent uses it

  • : user says make this a skill
  • Capture this workflow
  • Save this as a skill
  • Turn this into a skill

Example prompts

  • “s repeatable process into a reusable skill. Use when: user says”
  • “capture this workflow”
  • “skillify”
  • “/skillify”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Analyze the Session
  2. Interview the User
  3. Write the SKILL.md
  4. Confirm and Save

What it can do on your machine

Read from SKILL.md and the folder at commit e40e0fe. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Skillify loads about 1.9k tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 934 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ZhangHanDong/harness-engineering-from-cc-to-ai-coding at commit e40e0fe, republished under its MIT licence (© ZhangHanDong). 934 words, ~1,879 tokens.

Download SKILL.mdSave it as .claude/skills/skillify/SKILL.md (or your agent's skills folder).
name
skillify
description
Capture this session's repeatable process into a reusable skill. Use when: user says "make this a skill", "capture this workflow", "skillify", "save this as a skill", "turn this into a skill", "create skill from session", "把这个流程变成技能", "提炼成skill", "保存为技能"
argument-hint
[description of the process you want to capture]

Skillify

Extracted from Claude Code v2.1.88 internal /skillify skill (originally ant-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

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!

Show full SKILL.md (348 more words)Show less
Step 3: Write the SKILL.md

Create the skill directory and file at the location the user chose in Round 2.

Use this format:

markdown
---
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 like Bash(gh:*) not Bash)
  • context: Only set context: fork for self-contained skills that don't need mid-process user input.
  • when_to_use is 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'."
  • arguments and argument-hint: Only include if the skill takes parameters. Use $name in 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

© ZhangHanDong, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/skillify of ZhangHanDong/harness-engineering-from-cc-to-ai-coding.

Open the folder on GitHubat commit e40e0fe

Compare with similar skills

Skillify next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Skillify compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Skillify this skillZhangHanDong/harness-engineering-from-cc-to-ai-coding1.5k—~1.9kAutomated safety check: PassMIT
SkillifyYeachan-Heo/oh-my-claudecode40k—~641Automated safety check: PassMIT
Capturealirezarezvani/claude-skills28k1 repos~2.8kAutomated safety check: PassMIT
Skillify Scrape Flowsgarrytan/gstack136k—~11kAutomated safety check: NotesMIT
Skillifygarrytan/gbrain31k—~7.5kAutomated safety check: PassMIT
Monitoring Capture ServicePostHog/posthog40k—~5kAutomated safety check: PassCustom licence

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Questions about Skillify

What does Skillify do?

Capture this session's repeatable process into a reusable skill. Skillify is an agent skill from ZhangHanDong/harness-engineering-from-cc-to-ai-coding. Capture this session's repeatable process into a reusable skill.

When should I use Skillify?

Skillify fits situations like: : user says make this a skill; capture this workflow; save this as a skill; turn this into a skill.

How do I install Skillify in Claude Code?

Run `npx skills add ZhangHanDong/harness-engineering-from-cc-to-ai-coding --skill skillify -a claude-code`. Or copy the skill folder (.claude/skills/skillify in ZhangHanDong/harness-engineering-from-cc-to-ai-coding) into .claude/skills/skillify in your project. Claude Code loads it when a task matches its description.

How do I install Skillify in Codex?

Run `npx skills add ZhangHanDong/harness-engineering-from-cc-to-ai-coding --skill skillify -a codex`. Or copy the skill folder (.claude/skills/skillify in ZhangHanDong/harness-engineering-from-cc-to-ai-coding) into .agents/skills/skillify in your project. Codex loads it when a task matches its description.

Can I use Skillify in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ZhangHanDong/harness-engineering-from-cc-to-ai-coding --skill skillify -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/skillify, .gemini/skills/skillify, .github/skills/skillify and .opencode/skills/skillify in your project.

What does Skillify need to run?

SKILL.md names no scripts, command-line tools or credentials: Skillify is instructions for the agent only.

Does Skillify access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Skillify safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Skillify use?

Skillify is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Skillify use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Skillify?

Skills that share tags, products or a category with Skillify: Skillify (Yeachan-Heo/oh-my-claudecode, 40k stars), Capture (alirezarezvani/claude-skills, 28k stars), Skillify Scrape Flows (garrytan/gstack, 136k stars) and Skillify (garrytan/gbrain, 31k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Skillify?

ZhangHanDong (a GitHub user) maintains it in ZhangHanDong/harness-engineering-from-cc-to-ai-coding, which has 1,505 GitHub stars. The repository was last updated on April 10, 2026.

Source: ZhangHanDong/harness-engineering-from-cc-to-ai-coding on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.