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Skf Refine Architecture

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armelhbobdad
skf-refine-architecture

Refines an architecture document against generated skills and a Verify Stack report. Use when the user requests to "refine the architecture against generated skills" or "refine an architecture doc with a VS report."

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-refine-architecture
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 Refine Architecture 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-refine-architecture
  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-refine-architecture .claude/skills/skf-refine-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skf Refine Architecture 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 Refine Architecture 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 Refine Architecture 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.

Refine Architecture

Overview

Produces a refined architecture document: gaps filled, issues flagged and improvements suggested from the generated skills and an optional VS feasibility report. It never deletes original content, only adds annotations, subsections and suggestions between <!-- RA:BEGIN ... --> and <!-- RA:END --> marker lines; run again on a refined document, it sets its earlier annotations aside and writes one current set.

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.
  • {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 an architecture refinement analyst in Ferris Architect mode; the user brings the architecture vision and the generated skills.

Workflow Rules

These rules apply to every step in this workflow:

  • Never speculate — every gap, issue, or improvement must cite specific APIs, types, or function signatures from the generated skills
  • 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; a gate that picks its default records that decision in the run sink the moment it decides
  • Every HARD HALT names its exit code, halt_reason and phase, and in headless mode prints the envelope the Result Contract below describes
  • Run state lives in {run_dir}, not in context; step 6 deletes it when the run finishes, a HALT keeps it

Stages

#StepFileAuto-proceed
1Initialize & Load Inputsreferences/init.mdNo (input gate)
2Gap Analysisreferences/gap-analysis.mdConditional (confirm a derived scope)
3Issue Detectionreferences/issue-detection.mdYes
4Improvementsreferences/improvements.mdYes
5Compile Refined Architecturereferences/compile.mdNo (review)
6Reportreferences/report.mdYes
7Workflow Health Checkreferences/health-check.mdYes

Invocation Contract

AspectDetail
Inputsarchitecture_doc_path [required], vs_report_path [optional]
Flags--headless / -H; --architecture-doc <path> and --vs-report-path <path> answer the step 1 prompts (--vs-report-path none refines without a report and skips the search for one); --scope-skills <names> (comma-separated skill names that replace the derived scope, so step 2 asks no scope confirmation)
Gatesstep 1: Input Gate [use args]
Outputsrefined-architecture-{arch_project_name}.md at {outputFolderPath} (the document's frontmatter project_name, else config's), promoted from a draft once the step 5 review approves it; an earlier one is first renamed to refined-architecture-{arch_project_name}-{timestamp}.md. Also refine-architecture-result-{YYYYMMDD-HHmmss}.json, its -latest.json copy and, once an approved review drops a finding, .ra-dismissed-{arch_project_name}.json
HeadlessEvery gate takes its default when {headless_mode} is true: --scope-skills skips the step 2 scope confirmation, and without --vs-report-path step 1 uses the [VS] report it finds.

Result Contract (Headless)

When {headless_mode} is true, step 6 prints one SKF_REFINE_ARCHITECTURE_RESULT_JSON: {...} line on stdout, and every HARD HALT one on stderr, built by the shared emitter: references/exit-codes.md gives its fields, the halt_reason values and the halt command.

On Activation

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

    • project_name, user_name, communication_language, document_output_language
    • skills_output_folder, forge_data_folder, output_folder, sidecar_path
  2. Compute run-scoped variables:

    • timestamp ← the output of date -u +%Y%m%d-%H%M%S, run once now: it names the run folder and the copy an earlier refined document is renamed to.
    • run_dir ← {project-root}/_bmad-output/.skf-run/skf-refine-architecture-{timestamp}, the run folder §5 creates
  3. 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.

  4. 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-refine-architecture.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} for §5 to record.

    Bind each path scalar, taking the bundled default when the merged value is empty or absent:

    • {refinementRulesPath} ← workflow.refinement_rules_path if non-empty, else references/refinement-rules.md (house style only: the file's first section names what a copy can change)
    • {outputFolderPath} ← workflow.output_folder_path if non-empty, else {output_folder}
    • {onCompleteCommand} ← workflow.on_complete if non-empty, else empty

    Then run workflow.activation_steps_prepend now, treat workflow.persistent_facts as standing context for the run (file:-prefixed entries load their file/glob contents 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 run workflow.activation_steps_append once §5's pre-flight has passed, before §6 loads the first stage.

  5. Pre-flight: the emitter, config, write probe and run folder. In that order, so every halt can print its envelope and an empty path halts as missing config (exit 3), not as a write failure.

    The emitter. Resolve {emitEnvelopeHelper} ← {project-root}/_bmad/skf/shared/scripts/skf-emit-result-envelope.py, else {project-root}/src/shared/scripts/skf-emit-result-envelope.py (the first that exists). If neither exists, HALT (exit code 3, halt_reason: "resolution-failure") at phase on-activation:emitter and display only: "Refine Architecture cannot run without skf-emit-result-envelope.py, which is not installed. Re-install SKF."

    The halts below come before the run folder, so in headless mode each passes its payload on stdin, with "customization_resolver_unavailable": "<reason>" added when step 4 kept one:

    bash
    uv run {emitEnvelopeHelper} emit-halt --workflow skf-refine-architecture --target stderr <<'SKF_RA_HALT'
    {"phase": "<phase>", "reason": "<the halt message>", "halt_reason": "<halt_reason>"}
    SKF_RA_HALT

    Config-completeness (exit 3). If {outputFolderPath} is empty: HALT (exit code 3, halt_reason: "output-folder-unconfigured") at phase on-activation:config: "Set output_folder in config.yaml, then re-run [RA]." If {forge_data_folder} is empty: HALT (exit code 3, halt_reason: "forge-folder-unconfigured") at phase on-activation:config: "Set forge_data_folder in config.yaml, then re-run [RA]."

    Write probe (exit 4). Check that both paths are writable before any prompt:

    bash
    for dir in "{outputFolderPath}" "{forge_data_folder}"; do
      mkdir -p "$dir" && \
        printf 'probe' > "$dir/.skf-write-probe" && \
        rm "$dir/.skf-write-probe"
    done

    On any non-zero exit: HALT (exit code 4, halt_reason: "write-failed") at phase on-activation:write-probe, with "path" set to the folder that failed.

    Run folder (exit 4). Create the run folder:

    bash
    mkdir -p "{project-root}/_bmad-output/.skf-run" && mkdir "{run_dir}"

    If the command fails, HALT (exit code 4, halt_reason: "write-failed") at phase on-activation:run-folder: "Cannot create the run folder {run_dir}: {the first stderr line}." Once it exists and {customization_resolver_unavailable} is set, record the reason in the run sink: write customization_resolver_unavailable: {customization_resolver_unavailable} to {run_dir}/resolver-warning.txt with a file write, never echo, then run uv run {emitEnvelopeHelper} record --run-dir "{run_dir}" --warning "$(cat "{run_dir}/resolver-warning.txt")".

  6. 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 Refine Architecture AI skill do?

Refines an architecture document against generated skills and a Verify Stack report. Use when the user requests to "refine the architecture against generated skills" or "refine an architecture doc with a VS report."

Why use Skf Refine Architecture on TypingMind?

Because you install it once and use it with any model. Skf Refine Architecture 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 Refine Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/armelhbobdad/bmad-module-skill-forge/tree/main/src/skf-refine-architecture. 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 Refine Architecture?

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 Refine Architecture?

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

Is the Skf Refine Architecture 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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