skf-create-skill

name	skf-create-skill
description	Compile a skill from a brief. Supports --batch for multiple briefs. Use when the user requests to "create a skill" or "compile a skill."

Create Skill

Overview

Compiles a verified agent skill from a skill-brief.yaml and source code, producing an agentskills.io-compliant SKILL.md with provenance map, evidence report, and progressive disclosure references. The workflow is mostly autonomous with three interaction points — after ecosystem check (if match found), after source extraction (to confirm findings), and after content quality review (when tessl produces suggestions). Steps adapt behavior based on forge tier (Quick/Forge/Forge+/Deep). Zero hallucination tolerance: every instruction in the output must trace to source code with a confidence tier citation. A single run is not resumable — if it is interrupted mid-compile, re-run from the brief (only --batch checkpoints progress across briefs).

Conventions

• Bare paths (e.g. references/<name>.md) resolve from the skill root.
• references/ holds prompt content carved out of SKILL.md (workflow stages chained via frontmatter nextStepFile, plus static reference docs); scripts/ and assets/ hold deterministic helpers and templates.
• {skill-root} resolves to this skill's installed directory (where customize.toml lives, if present).
• {project-root}-prefixed paths resolve from the project working directory.
• {skill-name} resolves to the skill directory's basename.

Role

You are operating in Ferris Architect mode — a skill compilation engine performing structural extraction and assembly. Apply zero hallucination tolerance: uncitable content is excluded, not guessed.

Workflow Rules

These rules apply to every step in this workflow:

• Never include content in SKILL.md that cannot be cited to source code
• Only load one step file at a time — never preload future steps
• Always communicate in {communication_language}
• If {headless_mode} is true, auto-proceed through confirmation gates with their default action, logging each auto-decision to the in-context headless_decisions[] buffer AND appending it as a JSON line to the on-disk auto-decision sink (established at step 1 §3) the moment it lands, so the audit trail survives context compaction before step 5 first writes the evidence report

Stages

#	Step	File	Auto-proceed
1	Load Brief	references/load-brief.md	Yes
2	Ecosystem Check	references/ecosystem-check.md	Conditional
2b	CCC Discover	references/sub/ccc-discover.md	Yes
3	Extract	references/extract.md	No (confirm)
3b	Fetch Temporal	references/sub/fetch-temporal.md	Yes
3c	Fetch Docs	references/sub/fetch-docs.md	Yes
3d	Component Extraction	references/component-extraction.md	Conditional
4	Enrich	references/enrich.md	Yes
5	Compile	references/compile.md	Yes
5a	Doc Sources	references/step-doc-sources.md	Yes
5b	Auto-Shard	references/step-auto-shard.md	Yes
5c	Doc-Rot	references/step-doc-rot.md	Yes
6	Validate	references/validate.md	Conditional
7	Generate Artifacts	references/generate-artifacts.md	Yes
8	Report	references/report.md	Yes
9	Workflow Health Check	references/health-check.md	Yes

Sub-steps under references/sub/ are conditional branches (CCC discovery, temporal/doc enrichment) kept out of the top-level step count so main-line steps 1–9 drive the workflow. Step 3d (Component Extraction) stays top-level as an alternative main step that replaces the standard extraction path when scope.type: "component-library".

Invocation Contract

Aspect	Detail
Inputs	brief_path (path to skill-brief.yaml) [required], --batch [optional]
Gates	step 2: Choice Gate [P] (if match)
Outputs	SKILL.md, context-snippet.md, metadata.json, provenance-map.json, evidence-report.md, references/
Headless	All gates auto-resolve with default action when {headless_mode} is true

On Activation

1. Load config from {project-root}/_bmad/skf/config.yaml and resolve:

   • output_folder, user_name, communication_language, document_output_language, sidecar_path, skills_output_folder, forge_data_folder

2. Resolve {headless_mode}: true if --headless or -H was passed as an argument, or if headless_mode: true in preferences.yaml. Default: false.

3. Resolve workflow customization. Run:

   python3 {project-root}/_bmad/scripts/resolve_customization.py \
       --skill {skill-root} --key workflow
   

   The script merges the three customization layers per bmad-customize's structural merge rules (scalars override, arrays append): {skill-root}/customize.toml (bundled defaults), _bmad/custom/skf-create-skill.toml under {project-root} (team overrides, committed), and _bmad/custom/skf-create-skill.user.toml under {project-root} (personal overrides, gitignored). If the script fails or is missing, fall back to reading {skill-root}/customize.toml directly.

   Apply the resolved values so the surface is not a silent no-op: execute each entry in workflow.activation_steps_prepend in order now; treat every entry in workflow.persistent_facts as standing context for the whole run (entries prefixed file: are paths or globs whose contents load as facts); and stash {onCompleteCommand} ← workflow.on_complete (empty string = no-op) for the final stage to invoke after the result JSON and metadata.json are finalized. After activation completes, execute each entry in workflow.activation_steps_append in order.

4. Load, read the full file, and then execute references/load-brief.md to begin the workflow.