xuzhougeng/wisp-science

skill-creator

Create, update, validate, and evaluate Wisp skills. Use when authoring a project-local or installable skill, refining its trigger description, adding deterministic scripts or Python sidecars, or testing whether another Agent can follow the workflow.

86Collecting
See how to use itView GitHub source
npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/skill-creator"

Quick start

Start using it in three steps

Install it or open the source, trigger it with a clear task, then follow the source workflow.

1

Install the Skill

npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/skill-creator"
2

Describe the task

Use skill-creator to help me with: [describe your task]. Before you begin, tell me what input you need, the steps you will follow, and the expected output.

3

Follow the workflow

5 key workflow steps, examples, and cautions are distilled below.

Continue to the workflow

Direct answers

Answers to review before you install

What is skill-creator?

Create, update, validate, and evaluate Wisp skills.

Who should use skill-creator?

It is relevant to workflows involving Testing, Operations, Python.

How do you install skill-creator?

SkillSignal detected this source-specific command: npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/skill-creator". Inspect the repository and command before running it.

Which Agent platforms does it support?

The upstream source does not declare a dedicated Agent platform.

What permissions or risks should you review?

No obvious permission action was detected by the static rules. This is not proof that the Skill is safe.

What are the current evidence limits?

This page combines upstream documentation with deterministic repository, quality, and static-risk signals. It is not described as a manual test or security review.

SkillSignal brief

Decide whether it fits your work first

Create, update, validate, and evaluate Wisp skills.

Useful in these contexts

Not yet included in a workflow collection

Core capabilities

TestingOperationsPython

Distilled from the source

Understand this Skill in one minute

About 3 min · 7 sections

When it is worth using

  1. Use when authoring a project-local or installable skill, refining its trigger description, adding deterministic scripts or Python sidecars, or testing whether another Agent can follow the workflow.

Core workflow

  1. 1

    Define concrete user requests that should trigger the skill and the expected

  2. 2

    Search existing skills before creating a duplicate.

  3. 3

    Choose a lowercase hyphenated name and create

  4. 4

    Add reusable scripts before writing long inline code examples. Execute every

  5. 5

    Add kernel.py only for small reusable Python helpers. Loading a skill does

Repository stars
560
Repository forks
68
Quality
86/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

86/100
Documentation26/30
Specificity25/25
Maintenance18/20
Trust signals17/25

Compare before choosing

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View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 3 min

Create Wisp skills

Author project-local skills under .wisp/skills/<name>/. Wisp also discovers bundled skills, user-installed skills, and paths configured by WISP_SKILLS_PATH, but only normal project paths are directly writable through Agent file tools.

Structure

<skill-name>/
├── SKILL.md
├── kernel.py              # optional pure helper definitions
├── scripts/               # optional standalone deterministic programs
├── references/            # optional detailed domain material
└── assets/                # optional output templates or static inputs

Keep SKILL.md concise. Put triggering information in frontmatter description; put essential procedure in the body; move detailed variants to one-level-deep references. Add only resources the workflow actually uses.

Workflow

  1. Define concrete user requests that should trigger the skill and the expected outputs.
  2. Search existing skills before creating a duplicate.
  3. Choose a lowercase hyphenated name and create .wisp/skills/<name>/SKILL.md with write.
  4. Add reusable scripts before writing long inline code examples. Execute every new script on representative local data.
  5. Add kernel.py only for small reusable Python helpers. Loading a skill does not inject Wisp tools into Python. The rendered skill supplies a one-time exec(compile(open(...))) instruction that defines the sidecar names in the persistent python kernel.
  6. Validate structure with this skill's scripts/quick_validate.py <skill-directory>.
  7. Refresh or reopen the project if the new skill does not yet appear, then find it with search_skills and load it with use_skill.
  8. Exercise the skill on realistic tasks. When explicit Wisp delegation is available, use a fresh bounded task with only the skill path and user-style request; do not leak the expected answer into the evaluation prompt.

For a user-wide installation, ask the user to install the validated folder via Settings → Skills. There is no Agent-side publish, overwrite, or delete API.

Frontmatter

At minimum include:

---
name: my-skill
description: Perform X. Use when the user asks for Y, Z, or related output.
---

The folder name and name should match. The description is the primary trigger; state both what the skill does and when it should be selected.

Python sidecar rules

Keep top-level code definition-only:

  • allow imports, function definitions, and literal constant assignments;
  • defer optional third-party imports into function bodies;
  • do not run work, access the network, or modify files at load time;
  • do not depend on injected Agent, Run, credential, artifact, or model objects;
  • pass paths and configuration explicitly;
  • use python to call helpers after the one-time loader instruction.

Use scripts/ instead when a helper is a standalone CLI, exceeds roughly one hundred lines, needs argument parsing, or should run through run_in_context.

Evaluation resources

Use the bundled scripts only when their extra rigor is useful:

  • scripts/run_eval.py for test prompts;
  • scripts/aggregate_benchmark.py for repeated result aggregation;
  • scripts/generate_report.py and eval-viewer/ for human review;
  • scripts/improve_description.py for trigger-description experiments;
  • scripts/package_skill.py to package a validated skill folder.

Inspect each script's CLI help before running it. Keep evaluation artifacts out of the skill folder unless they are intentional reusable resources.

Wisp boundaries

  • search_skills and use_skill do not edit the catalog.
  • Project file tools cannot manage user-wide installed skills outside granted workspace paths.
  • run_in_context executes deterministic work; it does not publish skills or call models.
  • Specialist creation is separate. Load customize and use save_specialist only when that explicit tool is advertised.
Skill path
skills/skill-creator/SKILL.md
Commit SHA
95d2c13d1665
Repository license
AGPL-3.0
Data collected