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Skf Export Skill

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armelhbobdad
skf-export-skill

Exports an SKF skill, validating its package, writing its context snippet and updating the managed section in CLAUDE.md, AGENTS.md or .cursorrules. Use when the user requests to "export a skill" or "package a skill."

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-export-skill
Stars
100
Forks
11
Bundled files
17
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.

  • 17 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 Export 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-export-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-export-skill .claude/skills/skf-export-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skf Export 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 Export 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 Export 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.

Export Skill

Overview

Checks a completed skill against the agentskills.io export gate, writes its context snippet and updates the managed section in CLAUDE.md, AGENTS.md or .cursorrules. It is the sole publishing gate: create-skill and update-skill produce drafts, and only export writes the context files and records the skill in .export-manifest.json.

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; for example knowledge/version-paths.md and shared/health-check.md.
  • 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 the workflow stages, chained by frontmatter nextStepFile; the files a stage loads when it needs them (load-skill.md loads multi-skill-mode.md and preflight-snippet-root-probe.md, update-context.md loads orphan-context-detection.md, orphan-row-detection.md and manifest-rebuild.md); and the headless contract (invocation-contract.md, result-envelope.md). assets/ holds the snippet and managed-section 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.
  • Cross-skill data coupling: export-skill, drop-skill and rename-skill write the managed section only through shared/scripts/skf-rebuild-managed-sections.py, in the format assets/managed-section-format.md documents, and change .export-manifest.json only through shared/scripts/skf-manifest-ops.py (v2 schema in references/manifest-rebuild.md). A change to either contract changes all three skills.

Role

You are Ferris in Delivery mode, a packaging specialist: you check a developer's completed skill against the ecosystem rules and inject it into the context files it targets.

Workflow Rules

  • Only load one step file at a time — never preload future steps
  • Always communicate in {communication_language}
  • At any interactive prompt, the inputs cancel, exit, [X], q, or :q exit cleanly with exit code 6 (halt_reason: "user-cancelled")
  • If {headless_mode} is true, auto-proceed through confirmation gates with their default action and record each auto-decision in the run sink (references/result-envelope.md)
  • Every HALT names its exit code, halt_reason and phase; in headless mode it first emits its envelope as references/result-envelope.md states
  • Snippet timing: step 3 only stages each context-snippet.md outside the project; step 4 §9c copies it into the skill package after its [C] gate, as step 4's last write. A cancel, a dry run or a halt before §9c leaves every snippet, and a carried gotchas line's one [CARRIED] cycle, as it was.

Stages

#StepFileAuto-proceed
1Load Skillreferences/load-skill.mdNo (confirm)
2Packagereferences/package.mdYes
3Generate Snippetreferences/generate-snippet.mdYes
4Update Contextreferences/update-context.mdNo (confirm)
5Token Reportreferences/token-report.mdYes
6Summaryreferences/summary.mdYes
7Workflow Health Checkreferences/health-check.mdYes

Invocation Contract

Headless callers and pipelines: the inputs, flags, gates, outputs and exit codes are in references/invocation-contract.md, and the SKF_EXPORT_RESULT_JSON envelope in references/result-envelope.md. Interactive runs do not need them.

On Activation

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

    • project_name, output_folder, user_name, communication_language, document_output_language
    • skills_output_folder, forge_data_folder, sidecar_path
    • snippet_skill_root_override (optional): the root: prefix of every snippet and managed-section row in place of the IDE skill folder, for an authoring repo that keeps its skills in one folder such as skills/; step 1's snippet-root option (d) sets it for one run without changing config.yaml.
  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-export-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. Keep the reason as {customization_resolver_unavailable} (unset when the resolver ran): a headless run records it once step 4 creates its run folder, and an interactive run only prints it.

    Bind each scalar to its bundled default when the merged value is empty or absent:

    • {snippetFormatPath} ← workflow.snippet_format_path, else assets/snippet-format.md
    • {onCompleteCommand} ← workflow.on_complete, else empty (step 6 then runs no hook)

    Then apply the array surfaces: 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 (file:-prefixed entries are paths or globs whose contents load as facts: the bundled default loads every project-context.md under {project-root}; an entry prefixed ! drops each earlier entry it names and loads nothing itself, so an override's "!file:{project-root}/**/project-context.md" turns that default off); then, after activation completes and before the first stage runs, execute each entry in workflow.activation_steps_append in order.

  4. Resolve the helpers and create the run folder. Resolve each helper to the first of its two paths that exists, the installed module first, all in parallel:

    • {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 (every envelope, result file and headless record).
    • {manifestOpsHelper} ← {project-root}/_bmad/skf/shared/scripts/skf-manifest-ops.py, else {project-root}/src/shared/scripts/skf-manifest-ops.py (steps 1, 3 and 4).
    • {skillInventoryHelper} ← {project-root}/_bmad/skf/shared/scripts/skf-skill-inventory.py, else {project-root}/src/shared/scripts/skf-skill-inventory.py (step 1).
    • {rebuildManagedSectionsHelper} ← {project-root}/_bmad/skf/shared/scripts/skf-rebuild-managed-sections.py, else {project-root}/src/shared/scripts/skf-rebuild-managed-sections.py (steps 1 and 4).
    • {validateOutputHelper} ← {project-root}/_bmad/skf/shared/scripts/skf-validate-output.py, else {project-root}/src/shared/scripts/skf-validate-output.py (steps 1 and 2).
    • {countTokensHelper} ← {project-root}/_bmad/skf/shared/scripts/skf-count-tokens.py, else {project-root}/src/shared/scripts/skf-count-tokens.py (steps 3 and 5).

    No step has a fallback for them, so check them all here, before any prompt or write. If one has no existing path, HALT (exit code 4, halt_reason: "context-rebuild-failed") at phase on-activation §4: "{the missing helper's file name} is missing. Nothing was changed. Re-install SKF."

    When {headless_mode} is true, create the run folder, whose sink holds the headless auto-decisions:

    bash
    mkdir -p "{project-root}/_bmad-output/.skf-run" && mktemp -d "{project-root}/_bmad-output/.skf-run/skf-export-skill-XXXXXXXX"

    Bind {run_dir} ← the path it prints. Step 6 deletes it once the run's envelope is built; a HARD HALT leaves it in place. If it cannot be created, HALT (exit code 4, halt_reason: "write-failed") at phase on-activation §4. When {customization_resolver_unavailable} is set, record it there: write customization_resolver_unavailable: <reason> to {run_dir}/resolver-warning.txt with a file write (a resolver error can hold quotes or $( ), so never echo it or type it into an argument), then run uv run {emitEnvelopeHelper} record --run-dir "{run_dir}" --warning "$(cat "{run_dir}/resolver-warning.txt")" from {project-root}.

  5. Pre-flight write check. Verify {skills_output_folder} is writable, so a read-only mount, a full disk or a denied path stops the run before the user confirms a batch. Without --dry-run, probe it with a write:

    bash
    mkdir -p "{skills_output_folder}" && \
      printf 'probe' > "{skills_output_folder}/.skf-write-probe" && \
      rm "{skills_output_folder}/.skf-write-probe"

    With --dry-run, check without writing: test -w "{skills_output_folder}" when the folder exists; when it does not, skip the check, since step 1 then finds no skill to export.

    On any non-zero exit: HALT (exit code 4, halt_reason: "write-failed") at phase on-activation §5.

  6. Load, read the full file, and then execute references/load-skill.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 Export Skill AI skill do?

Exports an SKF skill, validating its package, writing its context snippet and updating the managed section in CLAUDE.md, AGENTS.md or .cursorrules. Use when the user requests to "export a skill" or "package a skill."

Why use Skf Export Skill on TypingMind?

Because you install it once and use it with any model. Skf Export 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 Export 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-export-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 Export 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 Export Skill?

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

Is the Skf Export 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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