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Skill Creator

  • 7.6k installs
  • 18 repo stars
  • Updated July 27, 2026
  • starchild-ai-agent/official-skills

skill-creator is an agent skill that scaffolds new skills with valid frontmatter, directory layout, and progressive-disclosure SKILL.md structure.

About

The skill-creator skill scaffolds new reusable agent skills with valid frontmatter, directory layout, and starter SKILL.md files. Core principles emphasize concise SKILL.md bodies because context window space is shared with system prompts and conversation history. Progressive disclosure uses three levels: name and description always in available_skills, full SKILL.md loaded on activation, and scripts references assets loaded on demand. Degrees of freedom guide instruction specificity from high freedom text guidance through medium pseudocode to low freedom executable scripts for fragile operations. Anatomy covers SKILL.md plus optional scripts for executable code, references for on-demand docs, and assets for templates not loaded into context. Creation steps include understanding the capability, choosing directories, writing frontmatter with strong trigger descriptions, drafting lean body content, and adding references or scripts as needed. Use when building new skills, wrapping APIs, or scaffolding reusable agent workflows.

  • Progressive disclosure: metadata, body on activation, references on demand.
  • Concise SKILL.md under 500 lines with heavy content in references/.
  • scripts/ for fragile executable operations never loaded into context.
  • Degrees of freedom from text guidance to executable script patterns.
  • Frontmatter description must be a strong activation trigger.

Skill Creator by the numbers

  • 7,592 all-time installs (skills.sh)
  • +67 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #16 of 826 Skill Development skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

skill-creator capabilities & compatibility

Capabilities
skill directory scaffolding · frontmatter and trigger description authoring · progressive disclosure structure design · scripts references assets layout guidance · degrees of freedom instruction calibration
Use cases
orchestration · documentation
From the docs

What skill-creator says it does

Scaffold new skills with valid frontmatter, directory layout, and a starter SKILL.md.
SKILL.md
npx skills add https://github.com/starchild-ai-agent/official-skills --skill skill-creator

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Listed on Skillselion
Installs7.6k
repo stars18
Security audit3 / 3 scanners passed
Last updatedJuly 27, 2026
Repositorystarchild-ai-agent/official-skills

How do I create a new agent skill with correct frontmatter, folder layout, and lean progressive-disclosure documentation?

Scaffold new agent skills with valid frontmatter, directory layout, progressive disclosure, and starter SKILL.md content.

Who is it for?

Developers authoring new agent skills or wrapping APIs into reusable workflow packages.

Skip if: Skip when you only need to invoke existing skills without creating new skill packages.

When should I use this skill?

User asks to create a new skill, scaffold an API helper, or start a reusable agent workflow package.

What you get

A scaffolded skill directory with SKILL.md, optional scripts, references, and assets following best practices.

  • SKILL.md template
  • skill directory structure

By the numbers

  • Enforces a 64-character maximum skill name length
  • Supports 3 allowed resource directory types: scripts, references, assets

Files

SKILL.mdMarkdownGitHub ↗

Core Principles

Concise is key. The context window is a shared resource between the system prompt, skills, conversation history, and your reasoning. Every line in a SKILL.md competes with everything else. Only add what you don't already know — don't document tool parameters visible in the system prompt, don't prescribe step-by-step workflows for things you can figure out. Focus on domain knowledge, interpretation guides, decision frameworks, and gotchas.

Progressive disclosure. Skills load in three levels: 1. Always in context — name, emoji, and description appear in <available_skills> in every conversation. This is how you decide which skill to activate. The description must be a strong trigger. 2. On activation — the full SKILL.md body is loaded via read_file when you decide the skill is relevant. This is where workflow, guidelines, and decision trees live. 3. On demand — scripts/, references/, and assets/ are only loaded when explicitly needed. Heavy content goes here, not in the body.

This means: keep the SKILL.md body lean (< 500 lines). Put detailed API docs in references/. Put automation in scripts/. The body should be what you need to start working, not an encyclopedia.

Degrees of freedom. Match instruction specificity to task fragility:

  • High freedom (text guidance) — When multiple approaches are valid. Write natural language explaining WHAT and WHY, not step-by-step HOW. Example: "Check funding rates and social sentiment to gauge market mood."
  • Medium freedom (pseudocode + params) — When a preferred pattern exists but details can vary. Describe the approach with key parameters. Example: "Use RSI with period 14, buy below 30, sell above 70."
  • Low freedom (scripts in scripts/) — When operations are fragile, require exact syntax, or are repetitive boilerplate. Put the code in standalone scripts that get executed, not loaded into context. Example: Chart rendering with exact color codes and API calls.

Default assumption: you are already smart. Only add context you don't already have.

Anatomy of a Skill

my-skill/
├── SKILL.md          # Required: Frontmatter + instructions
├── scripts/          # Optional: Executable code (low freedom)
│   └── render.py     #   Run via bash, not loaded into context
├── references/       # Optional: Docs loaded on demand (medium freedom)
│   └── api-guide.md  #   Loaded via read_file when needed
└── assets/           # Optional: Templates, images, data files
    └── template.json #   NOT loaded into context, used in output

When to use each:

DirectoryLoaded into context?Use for
SKILL.md bodyOn activationCore workflow, decision trees, gotchas
scripts/Never (executed)Fragile operations, exact syntax, boilerplate
references/On demandDetailed API docs, long guides, lookup tables
assets/NeverTemplates, images, data files used in output

Creating a Skill

Step 1: Understand the Request

Before scaffolding, understand what you're building:

  • What capability? API integration, workflow automation, knowledge domain?
  • What triggers it? When should the agent activate this skill? (This becomes the description.)
  • What freedom level? Can the agent improvise, or does it need exact scripts?
  • What dependencies? API keys, binaries, Python packages?

Examples:

  • "I want to generate charts" → charting skill with scripts (low freedom rendering)
  • "Help me think about trading strategies" → knowledge skill (high freedom, conversational)
  • "Integrate with Binance API" → API skill with env requirements and reference docs

Step 2: Scaffold

Use the init script:

python skills/skill-creator/scripts/init_skill.py my-new-skill --path ./workspace/skills

With resource directories:

python skills/skill-creator/scripts/init_skill.py api-helper --path ./workspace/skills --resources scripts,references

With example files:

python skills/skill-creator/scripts/init_skill.py my-skill --path ./workspace/skills --resources scripts --examples

Step 3: Plan Reusable Contents

Before writing, decide what goes where:

  • SKILL.md body: Core instructions the agent needs every time this skill activates. Decision trees, interpretation guides, "when to do X vs Y" logic.
  • scripts/: Any code that must run exactly as written — API calls with specific auth, rendering with exact formats, data processing pipelines.
  • references/: Detailed docs the agent might need occasionally — full API endpoint lists, schema definitions, troubleshooting guides.
  • assets/: Output templates, images, config files that the agent copies/modifies for output.

Step 4: Write the SKILL.md

Plan the content first — frontmatter trigger, body structure, freedom level. Then:

1. Frontmatter — Update description (CRITICAL trigger), add requirements, set emoji 2. Body — Write for the agent, not the user. Short paragraphs over bullet walls. Opinions over hedging.

Design patterns for the body:

  • Workflow-based — Step-by-step process (charting: fetch data → configure chart → render → serve)
  • Task-based — Organized by what the user might ask (trading: "analyze a coin" / "compare strategies" / "check sentiment")
  • Reference/guidelines — Rules and frameworks (strategy: core truths, conversation style, when to pull data)
  • Capabilities-based — Organized by what the skill can do (market-data: price tools / derivatives tools / social tools)

Step 5: Create / Update via skill_manage

`skill_manage` is the primary workflow — it validates frontmatter, runs a security scan, and auto-reloads the cache. Do NOT use write_file as the main path.

Creating a new skill:

skill_manage(action="create", name="my-skill", content="---\nname: my-skill\n...")

Patching an existing skill (preferred for targeted changes):

# Always read_file first to get exact whitespace/content
skill_manage(action="patch", name="my-skill", old_string="exact old text", new_string="new text")

Full rewrite of existing skill:

skill_manage(action="edit", name="my-skill", content="---\nname: my-skill\n...")

⚠️ Known gotchas:

  • create errors if skill already exists → use edit or patch instead.
  • edit/patch errors if skill does NOT exist → use create first.
  • patch requires exact old_string match (whitespace included) → always read_file before patching.
  • execute() must accept **kwargs — if you see unexpected keyword argument 'action', it's a bug in the tool implementation (fix: def execute(self, **kwargs)).

Fallback only — if skill_manage is unavailable, use write_file + skill_refresh() manually.

Step 6: Validate

python skills/skill-creator/scripts/validate_skill.py ./workspace/skills/my-new-skill

After skill_manage, validate is optional (auto-reloaded), but run it to catch schema issues early.

Frontmatter Format

The frontmatter uses metadata.starchild for Star Child-specific fields:

---
name: skill-name
version: 1.0.0
description: "What this skill does. Use when [specific trigger scenarios]."

metadata:
  starchild:
    emoji: "🔧"
    skillKey: skill-name
    requires:
      env: [API_KEY_NAME]
      bins: [python]
      anyBins: [curl, wget]
    install:
      - kind: pip
        package: pandas
      - kind: apt
        package: curl
        bins: [curl]

user-invocable: true
disable-model-invocation: false
---

Field reference:

FieldLocationRequiredPurpose
nametop-levelYesSkill identifier (lowercase hyphen-case)
versiontop-levelYesSemantic version (e.g. 1.0.0). Required for publishing. Always include.
descriptiontop-levelYesTrigger text — when should the agent use this?
emojimetadata.starchildNoDisplay emoji
skillKeymetadata.starchildNoDedup key
requires.envmetadata.starchildNoRequired env vars
requires.binsmetadata.starchildNoRequired binaries (ALL must exist)
requires.anyBinsmetadata.starchildNoRequired binaries (ANY one)
installmetadata.starchildNoHow to install deps (pip, apt, npm, etc.)
user-invocabletop-levelNoCan user trigger via /command (default: true)
disable-model-invocationtop-levelNoHide from <available_skills> (default: false)

On-Chain Skills — Wallet Policy Prerequisite

If the skill involves any on-chain operations (sending transactions, token approvals, swaps, bridging, signing, deposits, withdrawals, smart contract interactions), add a Prerequisites section near the top of the SKILL.md:


## Prerequisites — Wallet Policy
Before executing any [operation], the wallet policy must be active.
Load the **wallet-policy** skill and propose the standard wildcard
policy (deny key export + allow `*`). This covers all [skill-name]
operations across all chains.

This ensures the agent proposes a wallet policy before attempting any transaction. Without it, the first transaction will fail with a policy violation.

What NOT to Include

  • README.md — The SKILL.md IS the readme. Don't duplicate.
  • CHANGELOG.md — Skills aren't versioned packages.
  • Docs the agent already has — Don't repeat tool descriptions from the system prompt.
  • Step-by-step for simple tasks — The agent can figure out "read a file then process it."
  • Generic programming advice — "Use error handling" is noise. Specific gotchas are signal.

Best Practices

1. Description is the trigger. This is how the agent decides to activate your skill. Include "Use when..." with specific scenarios. Bad: "Trading utilities." Good: "Test trading strategies against real historical data. Use when a strategy needs validation or before committing to a trade approach."

2. Write for the agent, not the user. The skill is instructions for the AI. Use direct language: "You generate charts" not "This skill can be used to generate charts."

3. Scripts execute without loading. Good for large automation. The agent reads the script only when it needs to customize, keeping context clean.

4. Don't duplicate the system prompt. The agent already sees tool names and descriptions. Focus on knowledge it doesn't have: interpretation guides, decision trees, domain-specific gotchas.

5. Request credentials last. Design the skill first, then ask the user for API keys.

6. Always validate before refreshing — run validate_skill.py to catch issues early.

Related skills

FAQ

Where should detailed API docs go in a new skill?

In references/ for on-demand loading; keep SKILL.md body lean under 500 lines.

When should I use skill-creator?

When scaffolding a new skill with frontmatter, directory layout, and starter SKILL.md content.

Is Skill Creator safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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