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Skf Create Stack Skill

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armelhbobdad
skf-create-stack-skill

Consolidated project stack skill with integration patterns — code-mode (analyzes manifests) or compose-mode (synthesizes from existing skills + architecture doc). Use when the user requests to "create a stack skill", "forge a stack", or "stack this project".

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-create-stack-skill
Stars
100
Forks
11
Bundled files
18
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.

  • 18 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 Stack 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-stack-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-stack-skill .claude/skills/skf-create-stack-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skf Create Stack 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 Stack 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 Stack 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 Stack Skill

Overview

Produces a consolidated stack skill documenting how libraries connect. Code-mode analyzes dependency manifests and co-import patterns from actual source code. Compose-mode synthesizes from pre-generated individual skills and architecture documents when no codebase exists yet. Every finding must trace to actual code with file:line citations; in compose-mode, inferred integrations are permitted but must be labeled [inferred from shared domain].

Conventions

  • Bare paths (e.g. references/<name>.md) resolve from the skill root, inside a references/ file too: only a stage's frontmatter nextStepFile names a file beside it.
  • 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. knowledge/tool-resolution.md, knowledge/version-paths.md and shared/references/feasibility-report-schema.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 prompt content carved out of SKILL.md (workflow stages chained via frontmatter nextStepFile, plus static reference docs); assets/ holds the stack skill template, the metadata contract and the provenance map schema.
  • {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 a dependency analyst and integration architect. You bring expertise in dependency analysis, cross-library integration patterns, and compositional architecture, while the user brings their project knowledge and scope preferences.

Workflow Rules

These rules apply to every step in this workflow:

  • Zero hallucination — all extracted content must trace to actual source code (compose-mode inferences must be labeled)
  • Only load one step file at a time — never preload future steps
  • If any instruction references a subprocess or tool you lack, achieve the outcome in your main context thread, with two exceptions: never decide by hand whether SKF generated a folder (the ownership check), and never work out by hand what a shared helper computes where its step names a helper-missing HALT: that helper resolving to no path stops the run with exit 3 helper-missing (references/invocation-contract.md lists every such halt). Where a step says what to do without its helper (an advisory check, a default value), do as it says
  • Never write into a skill folder SKF did not generate — generate-output §1 runs the inventory's write check before any prior metadata is read and again before staging
  • Always communicate in {communication_language}
  • If {headless_mode} is true, auto-proceed through confirmation gates with their default action, and record each auto-decision in the run sink the moment the gate takes it, with the record --decision command the gate shows
  • Every HARD HALT names its exit code, halt_reason and phase, and prints its envelope through the shared emitter with the command its step file shows (the init:emitter halt, with no emitter, only displays its message); never type an SKF_STACK_RESULT_JSON line
  • Warnings use a single accumulator: see ## Workflow state contract below for shape and surfacing.

Workflow state contract

Every step that emits a warning appends a structured entry to a single list named workflow_warnings[] (the one accumulator for the whole workflow). Each entry has the shape {step: "step-NN", severity: "info|warn|error", code: "<short-slug>", message: "<human text>", context: {<optional fields>}}. Appending one means recording it in the run sink at once, the context fields folded into the message. Write its [{step}/{severity}] {code}: {message} line to {run_dir}/warning.txt with a file write, never an echo: a message can quote backticks or $( ), which a shell runs. Then run:

bash
uv run {emitEnvelopeHelper} record --run-dir "{run_dir}" --warning "$(cat "{run_dir}/warning.txt")"

The shell does not expand what the substitution prints, so the message reaches the sink as text.

The sink, {run_dir}/warnings.jsonl, is the list: step 7 §8b lists it in evidence-report.md, step 9 §1 renders it, and the result envelope carries it.

Single-pass, no resume: the run's state lives in {run_dir}, the run folder step 1 creates under {project-root}/_bmad-output/.skf-run/ (the warnings and auto-decisions, step 3's import counts, step 4's extraction bundle and step 6's draft), and an interrupted run starts again from step 1.

Stages

#StepFileAuto-proceed
1Initialize & Mode Detectionreferences/init.mdConditional
2Detect Manifestsreferences/detect-manifests.mdConditional (no manifest found)
3Rank & Confirm Librariesreferences/rank-and-confirm.mdNo (confirm)
4Parallel Extractreferences/parallel-extract.mdYes
5Detect Integrationsreferences/detect-integrations.mdYes
6Compile Stackreferences/compile-stack.mdNo (review)
7Generate Outputreferences/generate-output.mdYes
8Validatereferences/validate.mdYes
9Reportreferences/report.mdYes
10Workflow Health Checkreferences/health-check.mdYes

Invocation Contract

references/invocation-contract.md holds the inputs, gates and outputs, the exit code and halt_reason of every HARD HALT, and the SKF_STACK_RESULT_JSON result envelope.

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
  2. Resolve {headless_mode} with explicit precedence (B2):

    1. Explicit disable wins. If --headless=false or --no-headless was passed, {headless_mode} is false regardless of any preference.
    2. Explicit enable next. If --headless or -H was passed (without =false), {headless_mode} is true.
    3. Preferences fallback. Otherwise, read headless_mode from {sidecar_path}/preferences.yaml (true or false).
    4. 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-stack-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): references/init.md records it as the run's first warning once its pre-flight has created {run_dir}.

    Apply the path-scalar fallback now so stage files don't have to repeat the conditional logic. For each of the two scalars, if the merged value is empty or absent, use the bundled default:

    • {stackSkillTemplatePath} ← workflow.stack_skill_template_path if non-empty, else assets/stack-skill-template.md (the SKILL.md, snippet and reference-file structures; assets/metadata-contract.md holds the metadata.json contract, which no override replaces)
    • {integrationPatternsPath} ← workflow.integration_patterns_path if non-empty, else references/integration-patterns.md

    Also resolve {onCompleteCommand} ← workflow.on_complete if non-empty, else empty string (no-op: references/report.md §2c then skips the hook).

    Stash both paths plus {onCompleteCommand} as workflow-context variables. Stage files reference {stackSkillTemplatePath} and {integrationPatternsPath} directly; empty-string overrides fall through to the bundled default.

    Also apply the array surfaces: run workflow.activation_steps_prepend now, keep workflow.persistent_facts as standing context (file: entries load their contents: 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 run workflow.activation_steps_append after.

  4. Load, read the full file, and then execute references/init.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 Stack Skill AI skill do?

Consolidated project stack skill with integration patterns — code-mode (analyzes manifests) or compose-mode (synthesizes from existing skills + architecture doc). Use when the user requests to "create a stack skill", "forge a stack", or "stack this project".

Why use Skf Create Stack Skill on TypingMind?

Because you install it once and use it with any model. Skf Create Stack 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 Stack 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-stack-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 Stack 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 Stack Skill?

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

Is the Skf Create Stack 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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