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Skf Quick Skill

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armelhbobdad
skf-quick-skill

Fast skill from a package name or GitHub URL — no brief needed. Use when the user requests a "quick skill" or "skill from URL" or "skill from package."

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-quick-skill
Stars
100
Forks
11
Bundled files
12
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.

  • 12 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 Quick 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-quick-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-quick-skill .claude/skills/skf-quick-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skf Quick 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 Quick 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 Quick 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.

Quick Skill

Overview

The fastest path to a skill — accept a GitHub URL or package name, resolve to source, extract the public API surface, and produce a best-effort SKILL.md with context snippet and metadata. No brief needed. Output is always community-tier quality, regardless of which tools are available.

Conventions

  • Bare paths (e.g. references/<name>.md) resolve from the skill root, {skill-root}: this skill's installed directory, where customize.toml lives.
  • {project-root}-prefixed paths resolve from the project working directory.
  • 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; e.g. shared/health-check.md, which the terminal step chains to.
  • 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.

Role

You are a rapid skill compiler collaborating with a developer. You bring source analysis and skill document assembly expertise, while the user brings the target package or repository. Work together efficiently — speed is the priority.

Workflow Rules

These rules apply to every step in this workflow:

  • Never fabricate content — all data must come from source extraction or user input
  • Never write into a skill folder SKF did not generate
  • Only load one step file at a time — never preload future steps
  • Always communicate in {communication_language}
  • Universal cancel-line affordance: at any interactive prompt the user may type cancel, exit, :q, or select the [X] Cancel and exit menu option (where surfaced) to leave cleanly. HARD HALT with exit code 6 (user-cancelled): stage {"phase": "<the step's slug>", "halt_reason": "user-cancelled", "reason": "Cancelled. No files were written.", "skill_package": null} as {run_dir}/halt.json and run uv run {emitEnvelopeHelper} emit-halt --workflow skf-quick-skill --run-dir "{run_dir}" --target stderr < "{run_dir}/halt.json" (references/halt-contract.md). In step 4 §6 the equivalent affordance is [Q] Quit without writing, with the same exit code and envelope.
  • If {headless_mode} is true, auto-proceed through confirmation gates with their default action, and record each auto-decision in the run folder the moment its gate decides, so the envelope's headless_decisions carries it: stage {"gate": "<the gate's name>", "default_action": "<X>", "taken_action": "<X>", "reason": "headless: <why>"} as {run_dir}/decision.json and run uv run {emitEnvelopeHelper} record --workflow skf-quick-skill --run-dir "{run_dir}" --decision < "{run_dir}/decision.json". Each gate names its gate and default where it decides.
  • If {headless_mode} is true, print the one-line JSON progress events on stderr that references/halt-contract.md (Headless Events) defines: when each step starts, just before it chains on, and at a HARD HALT.

Stages

#StepFileAuto-proceed
1Resolve Targetreferences/resolve-target.mdConditional (no target, or an ambiguous one)
2Ecosystem Checkreferences/ecosystem-check.mdConditional (an official skill matches)
3Quick Extractreferences/quick-extract.mdConditional (an unusual repo shape, zero exports)
4Compilereferences/compile.mdNo (review)
5Write & Validatereferences/write-and-validate.mdConditional (the skill folder exists)
6Finalizereferences/finalize.mdYes
7Workflow Health Checkreferences/health-check.mdYes

Invocation Contract

AspectDetail
Inputstarget (GitHub URL, package name or npm, PyPI or crates.io page URL) [required for single-target mode], language_hint [optional, --language-hint], scope_hint [optional, --scope-hint]
Overrides--language-hint, --scope-hint, --description, --exports, --skip-snippet, --no-active-pointer, --batch <file>, --fail-fast: see On Activation step 4
Gatesstep 1: target input, ambiguous package name [C/n/U/X] (if another registry also holds the name; headless keeps the first registry's pick), multi-language disambiguation [C/n/A] (pick any detected language); step 2: ecosystem match [P/I/A] (none until agentskills.io has a registry API); step 3: repo-shape [C/A] + zero-exports rescue [R/P/A]; step 4: review [C/E/S/Q]; step 5: overwrite [Y/N]
OutputsSKILL.md, context-snippet.md, metadata.json, active pointer, result contract (timestamped + -latest copy), one SKF_QUICK_SKILL_RESULT_JSON line (stdout when the run finishes, stderr at a HARD HALT), and under --batch the batch summary. Snippet and active pointer can be skipped per overrides.
HeadlessAll gates auto-resolve with default action when {headless_mode} is true; each auto-decision is recorded in the envelope's headless_decisions
Exit codesSee references/halt-contract.md: the exit-code map and the envelope every HARD HALT emits, except On Activation step 1's check for python3 and uv, which prints none

On Activation

  1. Read {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 and sidecar_path.

    Then check the runtime: every helper this workflow runs, the envelope emitter included, runs through uv run, so confirm that python3 and uv are both on $PATH (command -v python3 and command -v uv). If either is missing, HARD HALT with exit code 3 (resolution-failure): display "Quick Skill needs {the missing tool}, which is not on your PATH. Install it (uv from https://docs.astral.sh/uv/getting-started/installation/, python3 from https://www.python.org/downloads/), then re-run." No envelope is printed: the emitter needs uv too.

    Then 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 envelope and auto-decision goes through, and create the run folder:

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

    Bind {run_dir} ← the path it prints (a finished run deletes it; a halted one keeps it). If it cannot be created, HARD HALT with exit code 4 (write-failure): display "Quick Skill cannot start: the run folder could not be created. {the first stderr line}" and emit with nothing to stage in:

    bash
    uv run {emitEnvelopeHelper} emit-halt --workflow skf-quick-skill --target stderr <<'SKF_JSON'
    {"phase": "on-activation", "halt_reason": "write-failure", "reason": "<that message, one line>", "skill_package": null}
    SKF_JSON

    With no emitter path (an incomplete install), each halt displays its message alone and step 6 §3 writes no result contract.

  2. Resolve {headless_mode}: true if --headless or -H was passed as an argument, or if {sidecar_path}/preferences.yaml sets headless_mode: true. 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-quick-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) and, when {emitEnvelopeHelper} has a path, record it in the run sink: write customization_resolver_unavailable: {customization_resolver_unavailable} to {run_dir}/resolver-warning.txt with a file write, never echo (the reason can hold quotes or $( )), then run uv run {emitEnvelopeHelper} record --run-dir "{run_dir}" --warning "$(cat "{run_dir}/resolver-warning.txt")". Under --batch, references/batch-mode.md §2 records that file in each target's run folder.

    Bind these three as workflow-context variables, taking the default when the merged value is empty or absent:

    • {skillTemplatePath} ← workflow.skill_template_path, else assets/skill-template.md
    • {batchOutputPath} ← workflow.batch_output_path, else {skills_output_folder}/_batch/
    • {onCompleteCommand} ← workflow.on_complete, else empty (step 6 §3 then runs no hook)

    Run each workflow.activation_steps_prepend entry now, in order. Hold every workflow.persistent_facts entry as standing context for the whole run; a file: entry loads the contents of the paths or globs it names: 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. Once step 4 has parsed the flags, before step 5 or 6 starts the run, run each workflow.activation_steps_append entry in order.

  4. Parse CLI overrides into the workflow context as {overrides}:

    FlagEffect
    --language-hint <lang>Sets language_hint: step 1 §4 takes it as the language, with no detection and no multi-language gate, and step 1 §3 asks only that language's registry.
    --scope-hint <path>Sets scope_hint: the folder step 3 reads the entry points and skill folders from.
    --description "<string>"Replaces the extracted description in step 4 §2, which checks its length and voice as it checks an extracted one.
    --exports "<name1,name2,...>"Replaces the extracted export list in step 4 §2: comma-separated, each name trimmed, empty items skipped.
    --skip-snippetNo context-snippet.md: step 4 §3 and step 5 §2 skip it.
    --no-active-pointerStep 6 §1 leaves {skill_group}/active as it is.
    --batch <file>Compiles every target the file lists, headless (On Activation step 5); --skip-snippet and --no-active-pointer apply to every target in the batch.
    --fail-fastWith --batch: the batch stops at the first failed target.

    --language-hint, --scope-hint, --description and --exports are single-target flags: batch mode refuses them.

  5. If --batch is set, load and read references/batch-mode.md in full before anything else and follow it.

  6. Load, read the full file, and then execute references/resolve-target.md to begin the workflow; when {headless_mode} is true, print step 1's start event first (references/halt-contract.md). In batch mode, references/batch-mode.md loads it for each target instead.

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 Quick Skill AI skill do?

Fast skill from a package name or GitHub URL — no brief needed. Use when the user requests a "quick skill" or "skill from URL" or "skill from package."

Why use Skf Quick Skill on TypingMind?

Because you install it once and use it with any model. Skf Quick 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 Quick 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-quick-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 Quick 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 Quick Skill?

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

Is the Skf Quick 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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