Nima Skill Creator

Create, refactor, and improve Codex-compatible skills with gated requirement discovery, reusable resource planning, executable scaffolding scripts, and valid...

Treat skill creation as workflow design, not just file formatting.

Start Here

1. Ground the skill in 2-4 concrete user requests before writing structure.
2. Choose the simplest fitting pattern from design-patterns.md.
3. Create only the resources that remove repeated work: scripts/, references/, assets/, and optionally agents/openai.yaml.
4. Keep SKILL.md procedural and concise. Move deep detail into references/.
5. Validate before packaging.

Do not create the skill body until the trigger examples, outputs, and reusable resources are clear.

Phase 1: Discovery Gate

Run this phase first. Do not jump into implementation until the gaps below are resolved.

Capture:

• What inputs the future skill must handle.
• What outputs it must reliably produce.
• What a user would actually say to trigger it.
• Whether the skill is new or an update to an existing folder.

Ask in Chinese when the user is exploring requirements. Keep it short and concrete. Use the prompts in interaction-guide.md if the request is underspecified.

Before moving on, summarize:

• Primary job of the skill.
• Trigger phrases or task shapes.
• Constraints or quality bar.
• Target directory.

Phase 2: Pattern Selection

Choose one primary pattern, then add a secondary pattern only if it removes ambiguity.

• Use design-patterns.md to map the request to tool-wrapper, generator, reviewer, inversion, or pipeline.
• Use inversion when the agent must collect structured context before acting.
• Use generator when output shape must stay consistent.
• Use reviewer when evaluation criteria should live in a checklist.
• Use pipeline when steps must happen in order with explicit checkpoints.
• Use tool-wrapper when the main value is on-demand domain guidance.

For most skill-creation requests, combine:

• inversion for discovery
• generator for scaffolding
• reviewer for validation
• pipeline for the overall sequence

Phase 3: Resource Planning

Translate the examples into reusable artifacts.

• Put deterministic automation in scripts/.
• Put long-lived, load-on-demand guidance in references/.
• Put templates or starter files in assets/.

Use best-practices.md to tighten naming, frontmatter, and progressive disclosure. Use workflows.md to shape staged skills with gates.

Avoid:

• Auxiliary docs like README.md, PROJECT.md, or status reports inside the skill folder.
• Repeating the same guidance in both SKILL.md and references/.
• Deep reference chains.

Phase 4: Implementation

When creating a new skill, initialize it with the provided scripts instead of hand-building the folder.

Create a new skill

bash

python3 scripts/init_skill.py my-skill --path "${CODEX_HOME:-$HOME/.codex}/skills" --resources scripts,references

Optional:

bash

python3 scripts/init_skill.py my-skill --path /path/to/skills --resources scripts,references,assets --examples --interface display_name="My Skill" --interface short_description="Create or update My Skill tasks"

Validate a skill

bash

python3 scripts/validate_skill.py /path/to/skill

Package a skill

bash

python3 scripts/package_skill.py /path/to/skill

Phase 5: Review Gate

Before calling the skill done, verify:

• Frontmatter has only name and description.
• description explains both function and trigger scenarios.
• SKILL.md tells the agent what to do, not what the project is.
• Every optional directory exists for a reason.
• Scripts are real, runnable programs.
• References are one hop away from SKILL.md.

If the skill still feels vague, run another discovery pass instead of adding filler.

Output Shape

When responding to a user about a skill you are creating or improving, prefer this order:

1. Discovery summary
2. Chosen pattern and why
3. Planned resources
4. Files created or changed
5. Validation result

Use output-patterns.md when you need a compact deliverable format.