| name | skill-packager |
| description | Package AI agent skills into deployment formats — .zip, .skill, Claude plugin, Claude marketplace, Cursor plugin, Cursor marketplace, native Codex/ChatGPT plugin, ChatGPT/Manus zip, Codex CLI, NanoClaw marketplace, OpenClaw/ClawHub, Agent Skills standard (.agents/), or a universal repo with all formats + CI/CD. Use when the user says "package this skill", "deploy my skill", "create a plugin from this skill", "make this work on Cursor/ChatGPT/Codex/NanoClaw/OpenClaw", "set up a repo for my skill", or wants to distribute a SKILL.md to any platform.
|
| metadata | {"author":"Yaniv Golan","version":"0.3.2"} |
Skill Packager
You package one or more skills (directories containing SKILL.md) into deployment-ready formats for distribution across AI platforms.
Step 1: Discover the input
Ask the user for the skill directory path(s).
If the user provides a name instead of a path (e.g., fiction-studio rather than /some/path/fiction-studio), search these locations in order before asking the user:
~/.claude/plugins/ — installed Claude Code plugins~/.claude/skills/ — personal skills directory.agents/skills/ relative to CWD — cross-platform skills- CWD itself
If not found in the above, do a broader search under ~/Library/Application Support/Claude/ (macOS) or ~/.config/claude/ (Linux).
Then read each skill's SKILL.md to extract:
name — the skill identifierdescription — what it doesmetadata.author, metadata.version — if presentlicense — if present- Whether it has
scripts/, references/, assets/, agents/, evals/
If packaging multiple skills, confirm the list before proceeding.
Step 2: Gather metadata
You need these values to generate manifests. Extract what you can from the SKILL.md frontmatter and the git repo (if any). Ask the user for anything missing:
| Field | Source | Fallback |
|---|
| Skill name(s) | SKILL.md name | required |
| Description | SKILL.md description | required |
| Author name | SKILL.md metadata.author or git config | ask user |
| Author email | git config user.email | ask user |
| Version | SKILL.md metadata.version or VERSION file | default 0.1.0 |
| License | SKILL.md license | default MIT |
| GitHub owner/repo | git remote or ask | needed for marketplace/repo formats |
| Plugin name | derived from skill name | user can override |
| Keywords/tags | extracted from description | user can refine |
For multi-skill packages, also ask for an overall plugin/marketplace name and description.
When asking for fields that have a fixed set of choices (like license), use the AskUserQuestion tool to present structured options. For free-form fields (author name, email, description), ask in plain text — batch related free-form questions together to minimize back-and-forth.
Step 3: Choose destination(s)
Ask where the skill will run, not which file format to emit — users think in destinations (Claude Code, ChatGPT, Cursor…), and several destinations share one artifact. Use the AskUserQuestion tool. Because it allows at most 4 options per question, use two questions.
Question 1 (single-select, header: "Scope"):
"How do you want to distribute this skill?"
| Option | Description |
|---|
| All destinations / public distribution (Recommended) | Universal repo — every format below + CI/CD, version management, README, GitHub Pages install page |
| Pick specific destinations | Choose exactly where it should run |
| Just a ZIP (manual upload) | Clean zip of the skill folder(s), for one-off upload anywhere |
If the user picks All destinations, use formats: ["universal"] and skip Question 2.
If the user picks Just a ZIP, do NOT scaffold — run python -m skill_packager build-zip --skill-dir <skill-dir> --output <zip> (see Step 4). Do not put a format key in skill-packager.json for this.
Question 2 (multiSelect, header: "Destinations", only if "Pick specific" was chosen):
"Which destinations should this skill install into?"
| Option | Covers | Format key |
|---|
| Anthropic (Claude Code, Cowork, NanoClaw) | All install from one .claude-plugin/ marketplace repo | claude-marketplace |
| Codex / ChatGPT | Native .codex-plugin/ + .agents/plugins/marketplace.json (+ legacy .claude-plugin/) | codex-plugin |
| Cursor | .cursor-plugin/plugin.json | cursor-plugin |
| OpenClaw / cross-client | .agents/skills/ (Agent Skills standard) | agent-skills |
Destination → format-key mapping (resolve the answer, then write formats in skill-packager.json)
| Destination | Format key |
|---|
| Claude Code · Claude Cowork · NanoClaw | claude-marketplace |
| Codex · ChatGPT | codex-plugin |
| Cursor | cursor-plugin |
| OpenClaw / ClawHub · any Agent-Skills client | agent-skills |
| Generic ZIP upload | (no key — use the build-zip subcommand) |
| All / public distribution | universal |
Collect the chosen destinations, map each to its key, de-duplicate, and set formats to that list. Notes:
- Collapse: Claude Code, Cowork, and NanoClaw all resolve to the single
claude-marketplace repo — if the user picked several of those, tell them "these all install from one repo."
- Codex/ChatGPT additionally keeps the legacy
.claude-plugin/marketplace.json, so a codex-plugin package is installable in both ecosystems.
- If the user picked 3+ ecosystems or wants public distribution, recommend
universal instead.
If the user asks for a .skill file specifically, use the package_skill.py script from the skill-creator-plus plugin if available, or tell them to run it separately.
Advanced: full internal format-key table
| Format key | What it produces |
|---|
universal | Full repo with ALL formats + CI/CD + version management |
claude-plugin | Inner .claude-plugin/plugin.json + plugin tree |
claude-marketplace | Above + root .claude-plugin/marketplace.json |
cursor-plugin | .cursor-plugin/plugin.json (also serves Cursor marketplace) |
codex-plugin | <plugin>/.codex-plugin/plugin.json + .agents/plugins/marketplace.json + legacy .claude-plugin/marketplace.json |
agent-skills | .agents/skills/ stripped copy (Agent Skills standard) |
Generic ZIP is not a format key — build it with python -m skill_packager build-zip.
Step 4: Generate the output
Output directory
Use the AskUserQuestion tool to confirm the output directory (header: "Output"):
"Where should the packaged output go?"
| Option | Description |
|---|
../<skill-name>-skill/ (Recommended) | New directory relative to CWD (not relative to the skill source — use this unless the skill source is already in a convenient location) |
./dist/ | Subdirectory inside the current project |
The user can also pick "Other" to specify a custom path.
Generation process
Use the bundled scripts to handle all deterministic generation:
1. Extract metadata from source skill(s)
The scripts are a Python package and must be run with -m from the scripts/ directory:
cd ${CLAUDE_SKILL_DIR}/scripts && python3 -m skill_packager metadata \
--skill-path /path/to/skill > /tmp/partial-skill-packager.json
cd ${CLAUDE_SKILL_DIR}/scripts && python3 -m skill_packager metadata \
--skill-path /path/to/skill-a --skill-path /path/to/skill-b > /tmp/partial-skill-packager.json
Read the output JSON. Fill in any empty fields — paying special attention to these:
marketplace_name — the script sets this to the same value as plugin_name. You must append -marketplace manually: "marketplace_name": "<plugin-name>-marketplace".description — the script copies the full SKILL.md description, which may be hundreds of words. Truncate it to the first sentence only (the one-liner that will appear in plugin.json, marketplace.json, and the README).github_repo, formats, keywords, category, targets — fill as needed.- For multi-skill: also fill
plugin_name, display_name, version.
Write the complete metadata to skill-packager.json.
2. Scaffold the repo
cd ${CLAUDE_SKILL_DIR}/scripts && python3 -m skill_packager scaffold \
--metadata /path/to/skill-packager.json --output ./my-skill-repo/
This creates the full directory structure, copies skill files, renders all manifests, and creates the .agents/skills/ stripped copy. The canonical copy (under <plugin-name>/skills/) keeps ${CLAUDE_SKILL_DIR}/ paths; the .agents/skills/ copy has them stripped for cross-platform portability.
3. Write README.md and CHANGELOG.md
These are the creative parts that require judgment. Read ${CLAUDE_SKILL_DIR}/references/platforms.md for the per-platform installation instructions template.
The scaffolded README.md and CHANGELOG.md contain <!-- SKILL_PACKAGER: REPLACE THIS --> markers. Replace the entire file content with real content. The README should include:
- Title and badges (Agent Skills compatible, Claude Code plugin, Cursor plugin, license)
- Brief description of what the skill(s) do
- Installation sections for every supported platform
- Usage examples (pulled from the skill's description or examples)
- License
4. Build zip (if needed)
For formats that need a zip (ZIP, ChatGPT/Manus, Codex CLI):
cd ${CLAUDE_SKILL_DIR}/scripts && python3 -m skill_packager build-zip \
--skill-dir ./my-skill-repo/<plugin-name>/skills/<skill-name> \
--output dist/<skill-name>.zip
Step 5: Verify the output
Run the validation script:
cd ${CLAUDE_SKILL_DIR}/scripts && python3 -m skill_packager validate ./my-skill-repo/
This checks JSON validity, version consistency across all locations, skill path resolution, .agents/skills/ entries, and stub detection (README/CHANGELOG must not still contain the placeholder marker). Fix any failures and rerun until all checks pass.
For machine-readable output, add --json.
Step 6: Present results
Tell the user what was generated with a file tree. Highlight:
- How to do the first release (bump version, tag, push)
- Which platforms are supported and how to install on each
- Any manual steps needed (e.g., marketplace registration)
- GitHub Pages setup: Remind the user to enable GitHub Pages in repo Settings → Pages → Source: GitHub Actions. This powers the "Install in Claude Desktop" button.
Edge cases and gotchas
Skills with dependencies. If a skill's compatibility field mentions required tools (Python, ffmpeg, etc.), include a note in the README about prerequisites. For ChatGPT specifically, warn if the skill requires tools that won't work in ChatGPT's sandbox.
Skills with scripts. Scripts are included in the zip. Make sure relative paths in SKILL.md still resolve after the zip is extracted.
The .agents/skills/ symlink. On Windows, symlinks may not work. The README should mention the alternative of copying the skill directory.
ChatGPT limitations. ChatGPT Skills are in beta (Business/Enterprise/Edu/Teachers/Healthcare plans only). Always include this caveat. If the skill requires network access or specific CLI tools, warn that it may not work in ChatGPT.
Codex CLI. Supports $skill-installer <github-url> for automated install, or manual extraction to ~/.codex/skills/. Both should be documented.
npx skills add. The vercel-labs/skills package provides cross-platform install via npx skills add <owner>/<repo>. Include this in the README when generating a universal repo.
Cursor plugin path. The .cursor-plugin/plugin.json at repo root needs "skills" to point to the inner plugin's skills directory (e.g., "./pretext/skills"), not the repo root.
Marketplace naming. The marketplace name is <plugin-name>-marketplace. The inner plugin name is just <plugin-name>.
NanoClaw. NanoClaw is a containerized AI assistant built on Anthropic's Agent SDK. It uses standard Claude Code skills distributed via marketplace repos — the Claude marketplace format works directly. NanoClaw has four skill types; this packager covers operational and utility skills (standard Claude Code skills). Feature skills (skill/* branches) and container skills (container/skills/) are NanoClaw-internal patterns outside packaging scope. NanoClaw enforces a 500-line SKILL.md limit.
OpenClaw / ClawHub. OpenClaw uses standard Claude Code skills with an optional metadata.openclaw frontmatter block for runtime gating (required binaries, env vars, install specs). The Agent Skills standard format (#8), Claude marketplace, or ZIP all work for distribution. Skills can also be published to ClawHub via clawhub skill publish. ClawHub requires a version field in frontmatter and enforces MIT-0 licensing.
