Best for
- 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.
xuzhougeng/wisp-science/skills/skill-creator/SKILL.md
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.
Decision brief
Author project-local skills under .wisp/skills//. Wisp also discovers bundled skills, user-installed skills, and paths configured by WISPSKILLSPATH, but only normal project paths are directly writable through Agent file tools.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/xuzhougeng/wisp-science --skill "skills/skill-creator"Inspect the Agent Skill "skill-creator" from https://github.com/xuzhougeng/wisp-science/blob/95d2c13d1665d46a388b5bdc998dcce0d5ec2eee/skills/skill-creator/SKILL.md at commit 95d2c13d1665d46a388b5bdc998dcce0d5ec2eee. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
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//SKILL.md with write. 4. Add reusable scripts before writing long inline…
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.
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.
Keep top-level code definition-only:
Use the bundled scripts only when their extra rigor is useful:
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 86/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 560 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
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.
<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.
.wisp/skills/<name>/SKILL.md with write.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.scripts/quick_validate.py <skill-directory>.search_skills and load it with use_skill.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.
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.
Keep top-level code definition-only:
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.
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.
search_skills and use_skill do not edit the catalog.run_in_context executes deterministic work; it does not publish skills or
call models.customize and use
save_specialist only when that explicit tool is advertised.Alternatives
CherryHQ/cherry-studio
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
K-Dense-AI/scientific-agent-skills
Build, inspect, test, and analyze bounded process-based discrete-event simulations with SimPy, including events, resources, interrupts, monitoring, replications, warm-up, and reproducible output analysis.
github/awesome-copilot
Build, scaffold, and deploy Power Automate cloud flows using the FlowStudio MCP server. Your agent constructs flow definitions, wires connections, deploys, and tests — all via MCP without opening the portal. Load this skill when asked to: create a flow, build a new flow, deploy a flow definition, scaffold a Power Automate workflow, construct a flow JSON, update an existing flow's actions, patch a flow definition, add actions to a flow, wire up connections, or generate a workflow definition from