Agent Skills: [H1][SKILL-BUILDER]

Creates and edits Claude Code skills with YAML frontmatter, folder structure, and depth-scaled content. Use when building new skills, updating existing skills, designing SKILL.md metadata, organizing skill folders, validating skill structure, or adding Python and TypeScript scripts for deterministic operations.

UncategorizedID: bsamiee/parametric_forge/skill-builder

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pnpm dlx add-skill https://github.com/bsamiee/Parametric_Forge/tree/HEAD/.claude/skills/skill-builder

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.claude/skills/skill-builder/SKILL.md

Skill Metadata

Name
skill-builder
Description
Creates and edits Claude Code skills with YAML frontmatter, folder structure, and depth-scaled content. Use when building new skills, updating existing skills, designing SKILL.md metadata, organizing skill folders, validating skill structure, or adding Python and TypeScript scripts for deterministic operations.

[H1][SKILL-BUILDER]

Dictum: Structured authoring produces discoverable, maintainable skills.

<br>

Create and refine Claude Code skills via structured workflows.

Tasks:

  1. Collect parameters — Scope: create | refine, Type: simple | standard | complex, Depth: base | extended | full
  2. Read frontmatter.md — Discovery metadata, trigger patterns
  3. Read structure.md — Folder layout gated by Type
  4. Read depth.md — LOC limits, nesting gated by Depth
  5. (complex) Read scripting.md — Automation standards
  6. Capture requirements — purpose, triggers, outputs
  7. Invoke skill-summarizer with skill style-standards — Extract voice, formatting, taxonomy
  8. Invoke deep-research — Domain research for skill topic
  9. Plan with 3 agents — file inventory, section structure, content framework
  10. Execute per Scope:
    • (create) Author new artifacts; select template:
    • (refine) Compare input to existing frontmatter; see refine.md:
      • Input = existing → optimize (density, fixes, quality)
      • Input > existing → upgrade (expand structure or depth)
      • Input < existing → downsize (combine, refactor, remove low-relevance)
  11. Validate — Quality gate, LOC compliance, structure match

Dependencies:

  • deep-research — Domain research via parallel agents
  • skill-summarizer — Voice and formatting extraction (with skill style-standards)
  • report.md — Sub-agent output format

[REFERENCE]: index.md — Complete file listing


[1][FRONTMATTER]

Dictum: Metadata enables discovery before loading.

<br>

Frontmatter indexed at session start (~100 tokens). Description is ONLY field parsed for relevance—quality determines invocation accuracy.

Guidance:

  • Discovery — LLM reasoning matches description to user intent. No embeddings, no keyword matching.
  • Trigger Density — Include file types, operations, "Use when" clauses. Every word aids matching.
  • Voice — Third person, active, present tense. Prohibit: 'could', 'might', 'probably', 'should'.

Best-Practices:

  • Length — 1-2 sentences. Concise triggers outperform verbose explanations.
  • Classification — Include type and depth fields for refine workflow detection.

[2][STRUCTURE]

Dictum: Type determines breadth—folder existence defines capability scope.

<br>

Type gates folder creation. Structure defines WHAT exists; Depth constrains HOW MUCH content.

| [INDEX] | [TYPE] | [FOLDERS] | | :-----: | -------- | ---------------------------------- | | [1] | Simple | SKILL.md only | | [2] | Standard | +index.md, references/, templates/ | | [3] | Complex | +scripts/ |

Guidance:

  • Naming — Skill folder matches frontmatter name exactly. Kebab-case throughout.
  • Index — Standard/Complex require index.md at root listing all reference files.
  • Upgrade Path — Start with simplest type satisfying requirements.

Best-Practices:

  • Directory Purpose — references/ for domain knowledge, templates/ for output scaffolds, scripts/ for automation.
  • File Limit — Max 7 files in references/ (including nested).

[3][DEPTH]

Dictum: Depth determines comprehensiveness—hard caps prevent bloat.

<br>

Depth enforces LOC limits and nesting rights. Each level adds +50 SKILL.md, +25 reference files (cumulative).

| [INDEX] | [DEPTH] | [SKILL.MD] | [REF_FILE] | [NESTING] | | :-----: | -------- | :--------: | :--------: | -------------- | | [1] | Base | <300 | <150 | Flat only | | [2] | Extended | <350 | <175 | 1 subfolder | | [3] | Full | <400 | <200 | 1-3 subfolders |

Guidance:

  • Nesting Gate — Subfolder requires 3+ related files OR distinct domain concern.
  • Content Scaling — Base: 1-2 items per Guidance/Best-Practices. Extended: 2-4. Full: comprehensive.
  • LOC Optimization — Density over deletion; see depth.md§LOC_OPTIMIZATION.
  • Content Separation — SKILL.md = WHY, references = HOW; see depth.md§CONTENT_SEPARATION.

Best-Practices:

  • Hard Caps — Exceeding limits requires refactoring, not justification.
  • No Brute-Force — Consolidate → restructure → densify → prune (in order).

[4][SCRIPTING]

Dictum: Deterministic automation extends LLM capabilities.

<br>

Complex type enables scripts/ folder for external tool orchestration, artifact generation, validation.

Guidance:

  • Justification — Script overhead demands explicit need: tool wrapping, exact reproducibility, schema enforcement.
  • Depth Scaling — Base/Extended: single script. Full: multiple when distinct concerns justify.

Best-Practices:

  • Type Selection — Standard suffices for most skills. Complex only when automation is core purpose.
  • Augmentation — Scripts support workflows; core logic remains in SKILL.md and references.

[5][TEMPLATES]

Dictum: Templates enforce canonical structure.

<br>

Templates define output scaffolds. Agent combines user input with template skeleton for consistent artifacts.

Guidance:

  • Purpose — Follow template exactly. No improvisation.
  • Composition — Input data + template skeleton = generated artifact.

Best-Practices:

  • Placeholder Syntax — Use ${variable-name} for insertion points.
  • Structure Match — Template complexity matches depth selection.

[6][VALIDATION]

Dictum: Gates prevent incomplete artifacts.

<br>

[VERIFY] Completion:

  • [ ] Parameters: Scope, Type, Depth collected and applied.
  • [ ] Research: deep-research completed fully before authoring.
  • [ ] Style: skill-summarizer constraints applied to output.
  • [ ] Workflow: Executed per Scope (create | refine).
  • [ ] Quality: LOC within limits, content separation enforced.

[REFERENCE] Operational checklist: →validation.md