/create-skill — Scaffold a New Skill or Command

VerifiedSafe

Meta-tool that scaffolds new AI-DLC skills and commands with correct structure, frontmatter, gates, and integration points.

Sby Skills Guide Bot
DevelopmentIntermediate
7/23/2026
Claude Code
#scaffolding#meta-skill#skill-creation#cli-tool

Recommended for

Automated summary

Scaffolds a new AI-DLC skill or command with correct structure, frontmatter, gates, and integration points.

Strengths

  • Automates file structure and frontmatter creation.
  • Ensures consistency with existing patterns and the pipeline.
  • Includes name conflict checks and references to similar skills.

Limitations

  • Requires prior understanding of the skill ecosystem.
  • May not cover all edge cases for highly custom skills.
  • Interactive phase can slow down if user wants a quick scaffold.
When to use it

When you need to add a new reusable skill or command to the AI-DLC system.

When not to use it

For one-off tasks that don't follow the standard skill structure or don't need pipeline integration.

Security analysis

Safe
Quality score92/100

The skill is a meta-tool that scaffolds new skill files. It uses only safe filesystem operations (ls, grep, mkdir, writing markdown) and runs a pre-existing install script. No destructive or exfiltrating actions are instructed.

No concerns found

How this analysis is made

Examples

Create a skill for API scanning
/create-skill api-scanner skill A tool that scans codebases for API endpoints and generates OpenAPI specs
Create a command to fix lint errors
/create-skill fix-lint command Automatically fix common linting errors in the current project

/create-skill — Scaffold a New Skill or Command

Usage: /create-skill [skill-name] [--type command|skill] [description]

Arguments: $ARGUMENTS


Purpose

Meta-tool that scaffolds new AI-DLC skills and commands with correct structure, frontmatter, gates, and integration points. The self-improvement flywheel — our skills can create new skills.

Every great system can extend itself. This command ensures new skills follow our patterns, integrate with the pipeline, and work on day one.


Instructions for Claude

Step 0: Parse Arguments

Extract from $ARGUMENTS:

  • name: The skill name (kebab-case, e.g., api-scanner)
  • type: command (lightweight, single file) or skill (multi-phase, directory-based). Default: command
  • description: What the skill does (remaining text after name and type)

If name is missing: Ask "What should this skill be called? (kebab-case, e.g., api-scanner)" If description is missing: Ask "What should this skill do? (one sentence)"


Step 1: Research Existing Patterns

Before scaffolding, understand the ecosystem:

  1. Check for conflicts: Search skills/commands/ and skills/skills/ for existing skills with the same or similar name
  2. Find similar skills: Identify 1-2 existing skills closest in purpose to the new one — these become pattern references
  3. Check integration points: Which existing skills should this one chain with?
# Check for name conflicts
ls skills/commands/${name}.md 2>/dev/null
ls skills/skills/${name}/ 2>/dev/null

# Find similar skills by searching descriptions
grep -r "Purpose" skills/commands/*.md skills/skills/*/SKILL.md | head -20

GATE: No name conflict. If conflict found, suggest an alternative name.


Step 2: Gather Requirements (Interactive)

Ask these questions one at a time (skip any the user already answered):

2a. What triggers this skill?

  • User invokes it directly (slash command)
  • Called by another skill in a pipeline
  • Both

2b. What tools does it need?

  • Read-only (Read, Glob, Grep) — for analysis skills
  • Read-write (+ Edit, Write, Bash) — for generation skills
  • Full (+ Agent, Task) — for orchestration skills

2c. Where does it fit in the pipeline?

  • Before planning (exploration, research)
  • During planning (enrichment, validation)
  • During work (implementation, generation)
  • After work (review, audit, documentation)
  • Standalone (independent of bolt pipeline)

2d. What artifacts does it produce?

  • Documents (where? what format?)
  • Code changes
  • Reports / dashboards
  • Configuration files

2e. Does it need personas or panels?

  • No personas (straightforward execution)
  • Single persona (focused expert)
  • Multi-persona panel (adversarial analysis)

Step 3: Scaffold the Skill

If type = command:

Create skills/commands/${name}.md:

# /${name} — ${Title}

Usage: \`/${name} [arguments]\`

**Arguments:** $ARGUMENTS

---

## Purpose

${description}

---

## Instructions for Claude

### Step 0: Setup

[Parse arguments and check preconditions]

${If pipeline integration:}
Check for per-project config:
\`\`\`bash
ls .ai-dlc.local.yaml 2>/dev/null
\`\`\`

### Step 1: ${First Phase Name}

[Phase instructions]

GATE: STOP. [Verification condition]. If not met, [recovery instruction]. Do NOT proceed to Step 2 until [condition].

### Step 2: ${Second Phase Name}

[Phase instructions]

${If produces artifacts:}
### Step N: Output

Write to \`${artifact_path}\`:

\`\`\`markdown
---
date: YYYY-MM-DD
topic: [kebab-case]
---
# [Title]
\`\`\`

---

## Integration

${Integration points with other skills}

If type = skill:

Create directory skills/skills/${name}/ with SKILL.md:

---
name: ${name}
description: ${description}
user-invocable: true
allowed-tools: ${tools}
argument-hint: ${hint}
---

# /${name} — ${Title}

${Same structure as command but with:}
- Full YAML frontmatter
- Richer phase structure
- Persona definitions (if panel-based)
- Quality standards section
- Adaptation notes

Step 4: Wire Integration

4a. Update install-skills.sh

Verify the new skill will be picked up by scripts/install-skills.sh (it should — the script uses glob patterns).

4b. Update README

Add the new skill to the appropriate table in skills/README.md:

For commands: Add row to Commands Reference table For skills: Add row to Skills Reference table

4c. Update Architecture Diagram

If the architecture diagram in skills/README.md needs updating (new directory), update it.

4d. Suggest Pipeline Integration

If the skill fits into the bolt pipeline, suggest where to add a call:

  • In /bolt-lfg (which step?)
  • In /slfg (which step?)
  • As a standalone recommendation

Step 5: Install and Verify

Install the new skill:

bash scripts/install-skills.sh

Verify it works:

# For commands: check it exists in ~/.claude/commands/
ls ~/.claude/commands/${name}.md

# For skills: check it exists in ~/.claude/skills/
ls ~/.claude/skills/${name}/SKILL.md

Step 6: Report

## Skill Created ✓

**Name:** /${name}
**Type:** ${type}
**Location:** skills/${type}s/${name}${type == 'skill' ? '/SKILL.md' : '.md'}
**Tools:** ${tools}
**Pipeline position:** ${position}
**Artifacts:** ${artifact_path}

### Integration Points
- ${upstream skills that feed into this}
- ${downstream skills this feeds into}

### Next Steps
- Test the skill: \`/${name} [test arguments]\`
- Refine based on usage
- Run \`/heal-skill ${name}\` after testing to fix any issues

Meta-Skill Patterns

This command follows AI-DLC skill conventions:

  • Frontmatter for skills, no frontmatter for commands
  • Gates between phases with STOP + verification
  • Per-project config check (.ai-dlc.local.yaml)
  • Artifact paths use docs/[type]/YYYY-MM-DD-[topic].* pattern
  • Integration section documenting upstream/downstream chains
  • Install via scripts/install-skills.sh glob patterns

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/create-skill-scaffold-a-new-skill-or-command-mrwakcin/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/create-skill-scaffold-a-new-skill-or-command-mrwakcin' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/create-skill-scaffold-a-new-skill-or-command-mrwakcin/download' --output '.skill-downloads/create-skill-scaffold-a-new-skill-or-command-mrwakcin/SKILL.md.part' && mv '.skill-downloads/create-skill-scaffold-a-new-skill-or-command-mrwakcin/SKILL.md.part' '.skill-downloads/create-skill-scaffold-a-new-skill-or-command-mrwakcin/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

This file does not provide a usable name/description header for this native procedure. It remains available to download and inspect; use the manual method below.

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/create-skill-scaffold-a-new-skill-or-command-mrwakcin/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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