菜单
Produces a complete SKILL.md packaged as a .skill file ready for CAS distribution. Use when: 'create a new skill', 'write a skill', 'build a skill for this workflow', 'turn this into a skill', 'upgrade this to a skill'.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/DojoGenesis/gateway --skill skill-creation命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
基于 SOC 职业分类
| name | skill-creation |
| model | sonnet |
| description | Produces a complete SKILL.md packaged as a .skill file ready for CAS distribution. Use when: 'create a new skill', 'write a skill', 'build a skill for this workflow', 'turn this into a skill', 'upgrade this to a skill'. |
| license | Complete terms in LICENSE.txt |
| category | skill-forge |
| triggers | ["create a new skill","write a skill","build a skill for this workflow","turn this into a skill","upgrade this to a skill"] |
| tier | 1 |
| agents | ["primary"] |
| tool_dependencies | ["file_system"] |
| inputs | [{"name":"skill_description","type":"string","description":"Description of the workflow or domain to formalize into a skill","required":true},{"name":"usage_examples","type":"string[]","description":"Concrete usage scenarios for the skill (1-3 examples)","required":false}] |
| outputs | [{"name":"skill_package","type":"ref","format":"cas-ref","description":"Complete SKILL.md packaged as a .skill file ready for CAS distribution"}] |
Use this skill to formalize a workflow or domain into a reusable, installable skill package. The output is a .skill file ready to be added to any agent that needs the capability.
Invoke when:
Before starting, gather:
bigquery-queries, pdf-rotation)Ask one targeted question at a time. Do not front-load a list of questions.
Every skill consists of a required SKILL.md file and optional bundled resources:
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── templates/ - Files used in output (templates, icons, fonts, etc.)
Every SKILL.md consists of:
name and description fields. These are the only fields that the agent reads to determine when the skill gets used; make the description clear and specific about what the skill produces and when to use it.scripts/ - Executable code for repetitive or deterministic tasks (e.g., rotate_pdf.py). Token efficient, can run without loading into context.references/ - Documentation loaded as needed (schemas, API docs, policies). Keeps SKILL.md lean. For large files (>10k words), include grep patterns in SKILL.md.templates/ - Output assets not loaded into context (logos, fonts, boilerplate code).Avoid duplication: Information lives in SKILL.md OR references, not both.
Do NOT include: README.md, CHANGELOG.md, or other auxiliary documentation. Skills are for AI agents, not users.
Three-level loading system:
Keep SKILL.md under 500 lines. When splitting content to references, clearly describe when to read them.
Key principle: Keep core workflow in SKILL.md; move variant-specific details to reference files.
Example structure for multi-domain skills:
bigquery-skill/
├── SKILL.md (overview + navigation)
└── references/
├── finance.md
├── sales.md
└── product.md
Match the level of specificity to the task's fragility and variability:
High freedom (text-based instructions): Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.
Medium freedom (pseudocode or scripts with parameters): Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.
Low freedom (specific scripts, few parameters): Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.
Skill creation involves these steps:
Follow these steps in order, skipping only if there is a clear reason why they are not applicable.
Skip this step only when the skill's usage patterns are already clearly understood.
Gather concrete examples of how the skill will be used. Ask questions like:
Avoid asking too many questions at once. Conclude when you have a clear sense of the functionality.
For each example, identify reusable resources:
| Resource Type | When to Use | Example |
|---|---|---|
scripts/ | Code rewritten repeatedly | rotate_pdf.py for PDF rotation |
templates/ | Same boilerplate each time | HTML/React starter for webapp builder |
references/ | Documentation needed repeatedly | Database schemas for BigQuery skill |
At this point, it is time to actually create the skill.
Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
When creating a new skill from scratch, always run the init_skill.py script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
Usage:
python /home/ubuntu/skills/skill-creation/scripts/init_skill.py <skill-name>
The script:
/home/ubuntu/skills/<skill-name>/scripts/, references/, and templates/After initialization, customize or remove the generated SKILL.md and example files as needed.
When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Manus to use. Include information that would be beneficial and non-obvious to Manus. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Manus instance execute these tasks more effectively.
Consult these helpful guides based on your skill's needs:
/home/ubuntu/skills/skill-creation/references/workflows.md for sequential workflows and conditional logic/home/ubuntu/skills/skill-creation/references/output-patterns.md for template and example patterns/home/ubuntu/skills/skill-creation/references/progressive-disclosure-patterns.md for splitting content across files.These files contain established best practices for effective skill design.
Begin with the scripts/, references/, and templates/ files identified in Step 2. This may require user input (e.g., brand assets for templates/, documentation for references/).
Test added scripts by running them to ensure they work correctly. For many similar scripts, test a representative sample.
Delete any unused example files from initialization.
Writing Guidelines: Always use imperative/infinitive form.
Write the YAML frontmatter with name and description:
name: The skill namedescription: Primary trigger mechanism. Must include what the skill does AND when to use it (body only loads after triggering).
Write instructions for using the skill and its bundled resources.
Once development of the skill is complete, validate and deliver it to the user.
Run the validation script to ensure the skill meets all requirements:
python /home/ubuntu/skills/skill-creation/scripts/quick_validate.py <skill-name>
If validation fails, fix the errors and run validation again.
Use message tool to send the SKILL.md file as attachment:
/home/ubuntu/skills/{skill-name}/SKILL.md
The system will automatically:
/home/ubuntu/skills/*/SKILL.md.skill fileAfter testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
Iteration workflow:
A completed skill package containing:
SKILL.md — valid YAML frontmatter + body with at least 8 ## sections (A+ standard)scripts/ — tested, executable code (if applicable)references/ — domain documentation (if applicable)templates/ — output assets (if applicable)The delivered .skill file is ready to install and triggers automatically when the agent encounters matching context.
An A+ skill meets all of these:
name is short and hyphenated; description covers both what and when## headers covering purpose, trigger/inputs, process, output, quality, examples, and anti-patternsreferences/Run quick_validate.py before delivery to confirm requirements are met.
Example 1: Formalizing a recurring export workflow
A user runs the same BigQuery export every week and asks: "Can you turn this into a skill?"
scripts/run_export.py as a reusable asset; schema docs as references/schema.mdinit_skill.py bigquery-weekly-exportExample 2: Upgrading a 3-section skill to A+
An existing skill has only ## Overview, ## Steps, ## Notes — below A+ standard.
## Steps to references/detailed-steps.md if over 150 lines## Notes as ## Anti-patterns with concrete failure modesmeta_skill to tool_dependencies, and link the referenced skill in the body's Related Skills sectionreferences/ before continuingOver-explaining what the agent already knows. Don't include basic API documentation the agent has in training. Only add domain-specific, proprietary, or procedural knowledge the agent cannot infer.
Vague description field. A skill with a vague description will never fire. The description is the only field read before loading the skill body — be explicit about when to use it.
Monolithic SKILL.md. A 600-line body defeats progressive disclosure. When the body exceeds ~400 lines, split variant details into references/.
README.md in the skill directory. Skills are for agents, not humans. Do not include README.md, CHANGELOG.md, or any auxiliary documentation.
Skipping validation. quick_validate.py catches structural errors before delivery. Running it only when something feels wrong means shipping broken skills.
Designing for one example. A skill built for exactly one known scenario fails on edge cases. Gather multiple examples in Step 1 to expose variation and plan for it.
High freedom where low freedom is needed. If a script must run in a specific sequence (e.g., database migration), do not leave it as a narrative description — use a script with enforced order.