What does this skill do, and when should you use it?
skill-creator is a skill that teaches how to make skills. It systematically explains the skill structure standard (a SKILL.md plus optional scripts/references/assets directories), the progressive-disclosure principle for context management, and a six-step creation process. The process covers understanding requirements through concrete examples, planning reusable resources, initializing a template via script, writing and editing content, packaging with validation, and iterating after real-world use. It suits developers and power users who want to turn their domain knowledge into reusable skill packages.
Reads the user's description of target skill use cases; asks clarifying questions about scope and trigger conditions; analyzes each example to decide which scripts, references, and assets are needed; runs scripts/init_skill.py to generate a template directory with YAML frontmatter; guides writing SKILL.md in imperative form; runs scripts/package_skill.py to validate (frontmatter, naming conventions, description quality, file organization) and produce a distributable zip; revises content via an iteration workflow after real usage.
- A team engineer wants to codify internal API specs or database schemas into a shared team skill
- A content operator wants to bundle document templates and brand assets into reusable skill assets
- A power user keeps rewriting the same code for a task type (e.g., PDF handling) and wants to wrap it as a script-based skill
- A skill maintainer needs a structured process to iterate on an existing skill after feedback and repackage it
- A newcomer to the Agent Skills standard wants a structural reference and best-practice guide
How do you install this skill?
- Static review only; no scripts were executed — script behavior and validation outcomes are source-code inferences.
- The LICENSE.txt referenced in frontmatter is absent from the provided files; licensing rests solely on the repository-level MIT notice.
- Skill content is substantially derived from Anthropic's official skills (implied by README References) but attribution is missing inside the skill.
- package_skill.py imports quick_validate from the same directory, breaking if files are moved; init_skill.py writes into the target path with error-exit but no rollback.
- Repository tests require a live MiniMax API key; test_agent.py is a networked manual check and provides no automated verification of this skill.
- Shell / CLI
- Local filesystem
Python (for init_skill.py and package_skill.py scripts)
The skill ships with the Mini-Agent repository. Quick mode: uv tool install git+https://github.com/MiniMax-AI/Mini-Agent.git, then run the official setup script and fill api_key and api_base into ~/.mini-agent/config/config.yaml. Development mode: git clone the repository, run uv sync, and fetch skills via git submodule update --init --recursive. The skill lives at mini_agent/skills/skill-creator/. How to install just this folder into other Agent Skills clients is not documented in the source.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/MiniMax-AI/Mini-Agent.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/mini_agent/skills/skill-creator" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Help me create a skill for editing PDFs.
Trigger it with natural language in a skill-capable agent, e.g., 'Help me create a skill for editing PDFs.' The skill will first ask for concrete usage examples, then plan scripts/references/assets, run scripts/init_skill.py <skill-name> --path <output-directory> to scaffold the template; after editing, run scripts/package_skill.py <skill-folder> to validate and create the zip; optionally pass an output directory like ./dist. Validation failures report errors and exit without packaging; fix and rerun.
What are this skill's strengths and limitations?
- Provides a complete, ordered six-step process that reduces ad-hoc work
- Bundled template-generation and packaging/validation scripts produce standards-compliant skill packages
- Systematically explains the progressive-disclosure principle for controlling context usage
- Rich concrete examples (PDF editor, frontend template, BigQuery) make concepts easy to grasp
- The scripts' full dependencies and runtime requirements are not detailed in the skill file
- Output quality depends on the user's ability to describe the domain clearly; the example-gathering stage is interaction-heavy
- The README describes the whole Mini-Agent collection (15 skills); this skill itself has no standalone test documentation
- Written for the Claude ecosystem; other agent platforms may require rewording and tool-reference adjustments
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Skill Creator this page | 48 · Use with care | ★ 3.1k | 8mo ago | MIT |
| Skill Creator | 61 · Recommended | ★ 20k | 15d ago | MIT |
| Skill Creator | 70 · Strongly recommended | ★ 2.2k | 12d ago | MIT |
| Create New Skills (Meta-Skill) | 62 · Recommended | ★ 1.5k | 8mo ago | — |
| Skill Builder Guide ✓ Anthropic · Official | 52 · Use with care | ★ 40k | 1mo ago | Apache-2.0 |
The Mini-Agent repo bundles 15 Claude Skills (drawn from Anthropic's skills ecosystem); this one is the only meta-skill that 'manufactures' the others. The direct alternative is reading Anthropic's official skills repository documentation directly, but this skill formalizes the process and ships init/packaging scripts.
How did FollowSkills review this skill?
A pure guidance skill plus three local Python scripts: init_skill.py only creates directories/templates, package_skill.py only zips, quick_validate.py only regex-validates. No network calls, no credential access, no deletion; small permission surface and visible risks. Deducted for: no confirmation/rollback for file writes, SKILL.md does not disclose script side-effects or data flow, and provenance (derived from Anthropic skills) is not attributed inside the skill.
Scripts are internally consistent: init_skill.py validates args with clear errors, package_skill.py validates before packaging and aborts on failure, quick_validate.py gives readable failure messages. Deducted for: no tests for any of the three scripts, crude regex-based frontmatter validation (multiline descriptions slip through), unexecuted static review, and behavior on abnormal inputs only inferable.
Description states clear trigger conditions (create/update a skill), six-step process and examples are clear, target audience and scenarios defined; scripts do not depend on overseas services and run in Chinese environments (English content is not blocking). Deducted for: no declared non-fit boundaries, no Chinese-language support notes, and trigger precision supported only by prose with no counter-examples.
SKILL.md is well layered (metadata → body → progressive disclosure), has name/description/license metadata, repository is MIT with comprehensive README/docs, loaded via submodule. Deducted for: the referenced LICENSE.txt is not present, no per-skill version or changelog, and maintenance/update responsibility is only implicit at the repository level.
Guidance is structured and actionable (with init/validate/package scripts), offering clear marginal value for the 'create a Claude-style skill' task versus manual trial-and-error. Deducted for: static review without execution, unverified quality of the generated skill templates, limited incremental value over upstream Anthropic skills, and thin cost/benefit evidence.
Complete auditable primary material (SKILL.md and all three scripts are independently reviewable), facts separable from inference. Deducted for: no tests or CI coverage of this skill, no third-party execution corroboration, key claims like 'scripts run and validation works' remain static inferences with thin coverage.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →