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Skf Create Skill

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armelhbobdad
skf-create-skill

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

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-create-skill
Stars
100
Forks
11
Bundled files
29
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 29 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by armelhbobdad on GitHub. Read the source before you install it.

Installation

Install the Skf Create Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/armelhbobdad/bmad-module-skill-forge/tree/main/src/skf-create-skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/armelhbobdad/bmad-module-skill-forge.git /tmp/bmad-module-skill-forge
mkdir -p .claude/skills
cp -r /tmp/bmad-module-skill-forge/src/skf-create-skill .claude/skills/skf-create-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skf Create Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skf Create Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skf Create Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Create Skill

Overview

Compiles a verified agent skill from a skill-brief.yaml and the source code or documentation it names, producing an agentskills.io-compliant SKILL.md with provenance map, evidence report, and progressive disclosure references. The workflow is mostly autonomous: it stops for the user in step 1 when no brief is named and several exist, in step 3 when several tags match the brief's version, for each authoritative AI documentation file the brief's scope leaves out (step 3 §2a), after source extraction (to confirm findings), at step 3c when a source brief extracted no export and none of its doc_urls could be fetched, and, for a component library, at step 3d's demo-exclusion and registry prompts. When the user has opted in to Tessl Review, step 6 also records Tessl's review of the skill as advice; it never stops the run. Steps adapt behavior based on forge tier (Quick/Forge/Forge+/Deep). Zero hallucination tolerance: every statement in the output carries a provenance citation, and content that cannot be cited is left out. 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.
  • Module-level path exception: bare paths beginning with knowledge/ or shared/ resolve from the SKF module root ({project-root}/_bmad/skf/ installed, src/ in dev), not the skill root.
  • Sibling skills: a path that names another SKF skill's folder (skf-<name>/...) resolves from the SKF module root, and that skill must be installed with this one.
  • references/ holds prompt content carved out of SKILL.md (workflow stages chained via frontmatter nextStepFile, plus static reference docs) and references/sub/ the conditional sub-steps; a nextStepFile is relative to its own stage file (so a sub-step that returns to a main stage names it in the parent folder). 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.

Workflow Rules

These rules apply to every step in this workflow:

  • Never include content in SKILL.md without a provenance citation: [AST:] or [SRC:] for code (T1 and T1-low), [QMD:] for T2 context or [EXT:] for T3 documentation, in the forms assets/skill-sections.md gives
  • Never write into a skill folder SKF did not generate — generate-artifacts §1 runs the inventory's write check before creating any directory; without the helper it writes only into a skill folder that does not exist yet
  • Only load one step file at a time — never preload future steps
  • Once step 3 §2b has bound {source_tree} (the private tree a remote source is read from), every HALT after it first runs uv run {sourceTreeHelper} close --tree "{source_tree}" from {project-root} and goes on whatever it prints. Step 7 removes the tree on a run that finishes, and a later run removes one a stopped run left behind, seven days on
  • Every HARD HALT in steps 1 to 7 emits the result envelope before it stops, in every mode. Each HALT names its exit code, halt_reason and phase where it fires, and its emit command. Stage {"phase": "<phase>", "halt_reason": "<halt_reason>", "reason": "<the halt message, one line>", "summary": {"halt_reason": "<halt_reason>", "evidence_report": null}} as {run_dir}/halt.json (add "path" when the halt names a file or folder; once step 7 has promoted the package, add "skill_package" and "outputs" and set summary.evidence_report to {forge_version}/evidence-report.md), then run uv run {emitEnvelopeHelper} emit-halt --workflow skf-create-skill --run-dir "{run_dir}" --target stderr < "{run_dir}/halt.json", adding --result-dir "{forge_version}" once step 7 has created {forge_version}, so the emitter also writes the halt's create-skill-result-*.json and its -latest.json copy there. Display the line it prints, then stop with the exit code; under --batch the halt ends only its brief: go to references/batch-mode.md §3, which records it and takes the next brief. If {emitEnvelopeHelper} resolved no path, display the halt message alone. The schema, shared/scripts/schemas/skf-create-skill-result-envelope.v1.json, lists every halt_reason with its exit code
  • Always communicate in {communication_language}
  • If {headless_mode} is true, auto-proceed through confirmation gates with their default action
  • Every decision the run takes without asking (each gate a headless run resolves by its default, step 1's tier override and its pick of the only brief) is recorded the moment it is taken: stage {"step": "<step>", "gate": "<gate>", "decision": "<action>", "rationale": "<why>", "timestamp": "<ISO-8601>"}, plus "value" or "fallback" where the gate names one, as {run_dir}/decision.json and run uv run {emitEnvelopeHelper} record --workflow skf-create-skill --run-dir "{run_dir}" --decision < "{run_dir}/decision.json". The run sink it appends to, {run_dir}/headless-decisions.jsonl, is the brief's one decision trail: it survives context compaction, step 5 §7 renders the evidence report's ## Auto-Decisions table from it, and the result envelope and contract carry it

Stages

#StepFileAuto-proceed
1Load Briefreferences/load-brief.mdConditional
2bCCC Discoverreferences/sub/ccc-discover.mdYes
3Extractreferences/extract.mdNo (confirm)
3bFetch Temporalreferences/sub/fetch-temporal.mdYes
3cFetch Docsreferences/sub/fetch-docs.mdConditional
3dComponent Extractionreferences/component-extraction.mdConditional
4Enrichreferences/enrich.mdYes
5Compilereferences/compile.mdYes
5aDoc Sourcesreferences/step-doc-sources.mdYes
5bAuto-Shardreferences/step-auto-shard.mdYes
5cDoc-Rotreferences/step-doc-rot.mdYes
6Validatereferences/validate.mdYes
7Generate Artifactsreferences/generate-artifacts.mdYes
8Reportreferences/report.mdYes
9Workflow Health Checkreferences/health-check.mdYes

Sub-steps under references/sub/ are conditional branches (CCC discovery, temporal and doc enrichment), lettered by the place they take in the run and kept out of the main-line step count. Step 3d (Component Extraction) is a branch inside step 3 for a component-library brief: extract §2c loads it in place of §4 to §4c and resumes at its §5, before Gate 2.

Invocation Contract

AspectDetail
Inputsbrief_path (a skill-brief.yaml path or a skill name) [optional: without it, step 1 offers the briefs in {forge_data_folder}, and a headless run halts unless there is one], --batch [optional: the briefs or folders of briefs after it, {forge_data_folder} when none is given, each validated before the first compiles]
Gatesstep 1: Brief Selection (no brief named and several in {forge_data_folder}; headless: HARD HALT brief-missing, exit 2; one brief: picked and recorded as gate brief-selection)
OutputsSKILL.md, context-snippet.md, metadata.json, provenance-map.json, evidence-report.md, extraction-rules.yaml, references/; per brief, the result contract (create-skill-result-{YYYYMMDD-HHmmss}.json and its -latest.json copy in {forge_version}) and one SKF_CREATE_SKILL_RESULT_JSON line on stdout, on stderr at a HARD HALT
HeadlessAll gates auto-resolve with default action when {headless_mode} is true; each auto-decision reaches the envelope's and the result contract's headless_decisions
Exit codes0 for a finished brief; at a HARD HALT 2 (the brief is missing, malformed or invalid), 3 (no forge config, source, helper or documentation), 4 (a write failed), 5 (a refused or broken package), 6 (the user chose to refine the scope). A --batch run exits with the highest code among its briefs, 0 when every brief finished

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 {sidecar_path}/preferences.yaml. Default: false.

  3. Resolve workflow customization. Run:

    bash
    uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --project-root {project-root} --key workflow

    It merges the bundled {skill-root}/customize.toml with {project-root}/_bmad/custom/skf-create-skill.toml (team overrides, committed) and .user.toml (personal overrides, gitignored). When it exits non-zero, prints no JSON or is missing, print one line, [activation/warn] customization_resolver_unavailable: <reason> (<reason>: its first stderr line, not found when the script is missing, no JSON when it printed none). If the resolver cannot run, read {skill-root}/customize.toml alone and use its bundled defaults: the {project-root}/_bmad/custom/ overrides do not apply to this run. In that case keep the reason as {customization_resolver_unavailable} (unset when the resolver ran) for step 1 §0 to record in each brief's run folder.

    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), except a ! entry and the entry it names (!file:{project-root}/**/project-context.md drops the bundled project-context default); and stash {onCompleteCommand} ← workflow.on_complete (empty string = no-op) for step 8 to invoke once each brief's result contract is written. After activation completes, execute each entry in workflow.activation_steps_append in order.

  4. Resolve {emitEnvelopeHelper} ← the first existing path of {project-root}/_bmad/skf/shared/scripts/skf-emit-result-envelope.py and {project-root}/src/shared/scripts/skf-emit-result-envelope.py, the shared emitter every result envelope, result contract and recorded decision goes through.

  5. If --batch was given, load, read the full file, and then execute references/batch-mode.md: it validates every brief first, runs steps 1 to 8 for each one and the health check once. Otherwise load, read the full file, and then execute references/load-brief.md to begin the workflow.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skf Create Skill AI skill do?

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

Why use Skf Create Skill on TypingMind?

Because you install it once and use it with any model. Skf Create Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skf Create Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/armelhbobdad/bmad-module-skill-forge/tree/main/src/skf-create-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skf Create Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skf Create Skill?

As many as you like. As long as a model supports skills, you can use Skf Create Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skf Create Skill AI skill free?

It is published on GitHub by armelhbobdad. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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