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Skf Verify Stack

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armelhbobdad
skf-verify-stack

Pre-code stack feasibility verification against architecture and PRD documents. Use when the user requests to "verify a tech stack" or "verify stack."

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-verify-stack
Stars
100
Forks
11
Bundled files
16
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.

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

Use it in TypingMind

Enable Skf Verify Stack 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 Verify Stack 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 Verify Stack 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.

Verify Stack

Overview

Cross-references generated skills against architecture and PRD documents to produce a feasibility report with evidence-backed integration verdicts, coverage analysis, and requirements mapping. Read-only: it reads skills and input documents and writes only the feasibility report, its result files and a scratch run folder (see Workflow Rules).

Schema contract: This skill is the producer of the SKF shared feasibility report schema — every report conforms to it.

Conventions

  • Bare paths (e.g. references/<name>.md) resolve from the skill root.
  • references/ holds prompt content carved out of SKILL.md (workflow stages chained via frontmatter nextStepFile, plus static reference docs); 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.
  • 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/references/output-contract-schema.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.

Role

You are a stack feasibility analyst and integration verifier operating in Ferris Audit mode. You bring expertise in API surface analysis, cross-library compatibility assessment, and architecture validation, while the user brings their architecture vision and generated skills.

Workflow Rules

These rules apply to every step in this workflow:

  • Read-only — never modify skills, architecture docs, or PRD files
  • Every verdict must cite evidence from the generated skills
  • 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
  • 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 log each auto-decision; a gate that picks its default for the user (step 1's offer of the newest earlier report) also records that decision in the run sink the moment it decides
  • Every helper a stage runs (a scripts/ file, or a shared helper from the stage's probe order) is required: if the probe order resolves to no path, or the helper cannot start, HALT (exit code 3, halt_reason: "resolution-failure") at the stage's phase, through the stage's halt envelope. Never recompute a helper's result by hand: the main-thread rule above covers subagent work, not helpers.
  • Every HARD HALT names its exit code, halt_reason and phase, and in headless mode prints its envelope through the shared emitter, with the command each stage file shows (references/exit-codes.md describes the envelope).
  • Stages 1 to 5 write only the timestamped report {outputFile}, and each write that fails halts (exit code 4, halt_reason: "write-failed"). Step 6 copies the report to {outputFileLatest} only after it passes the feasibility-report check, so a run that halts before then leaves the previous finished copy, the one consumers read, in place.
  • Run state lives in {run_dir}, not in context; step 6 deletes it, a HALT keeps it.

Stages

#StepFileAuto-proceed
1Initialize & Load Inputsreferences/init.mdNo (confirm)
2Coverage Analysisreferences/coverage.mdYes
3Integration Verificationreferences/integrations.mdYes
4Requirements Mappingreferences/requirements.mdYes
5Synthesize Verdictreferences/synthesize.mdYes
6Reportreferences/report.mdYes
7Workflow Health Checkreferences/health-check.mdYes

Invocation Contract

AspectDetail
Inputsarchitecture_doc_path [required], prd_path [optional], previous_report_path [optional]
Flags--headless / -H (auto-resolve all gates); --architecture-doc <path> (skip step 1 prompt for the required input); --prd <path> (skip step 1 prompt for the optional PRD); --previous-report <path> (skip step 1 prompt for delta comparison)
Gatesstep 1 Input Gate (use args), including the offer to compare against the newest earlier report (headless default: use it). No later stage waits for input: a run with 0% coverage or only Blocked pairs prints one warning and continues, so its report still carries the recommendations.
Outputsfeasibility-report-{project_slug}-{timestamp}.md and its feasibility-report-{project_slug}-latest.md copy (not a symlink), written by step 6 once the finished report passes the feasibility-report check, both per the SKF shared feasibility report schema ({project-root}/_bmad/skf/shared/references/feasibility-report-schema.md; {project-root}/src/shared/references/feasibility-report-schema.md in a dev checkout), with integration verdicts, coverage analysis, recommendations, and evidence sources; plus verify-stack-result-{YYYYMMDD-HHmmss}.json and verify-stack-result-latest.json in {forge_data_folder}
HeadlessAll gates auto-resolve with default action when {headless_mode} is true. Per-flag args (--architecture-doc, --prd, --previous-report) consumed at the gates that would otherwise prompt.
Exit codesSee references/exit-codes.md

Result Contract (Headless)

When {headless_mode} is true, step 6 prints one SKF_VERIFY_STACK_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, sidecar_path
  2. Fix the run-scoped variables so no stage derives them again:

    • timestamp ← UTC YYYYMMDD-HHmmss captured at activation time
    • project_slug is bound by init.md before its §1, from the shared feasibility-report helper, which holds the one slug rule the consumers of this report also apply; never slugify project_name by hand
    • The two combine in init.md §4 into {outputFile} and {outputFileLatest} per the stage frontmatter template, and stay fixed for the whole run, so every later reference to either file resolves to the same path.
    • run_dir ← {project-root}/_bmad-output/.skf-run/skf-verify-stack-{timestamp}, the run folder init.md creates first
  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.

    Bind the inputs in every mode: {architecture_doc_path} ← the --architecture-doc value, {prd_path} ← --prd and {previous_report_path} ← --previous-report, each null when its flag is absent. init.md §1 asks only for those still null.

  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-verify-stack.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 init.md to record once it has created {run_dir}.

    Apply the path-scalar fallback now so stage files don't repeat it: {reportTemplatePath} ← workflow.report_template_path if non-empty, else assets/feasibility-report-template.md. An empty or absent value falls through to that default, and init.md §4 loads the variable as it is. Bind {outputFolderPath} ← {forge_data_folder}, always (no setting moves it): the report and its -latest copy go where create-stack-skill and refine-architecture look for them, and step 1 finds the earlier reports there.

    The same merge resolves workflow.on_complete (default empty = no-op); report.md §5 executes it, if non-empty, at the terminal stage.

    Also apply the array surfaces so they are not silent no-ops: 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 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 execute each entry in workflow.activation_steps_append after activation completes.

  5. Pre-flight: the emitter. Before the first prompt, 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: "Verify Stack cannot run without skf-emit-result-envelope.py, which is not installed. Re-install SKF."

  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 Verify Stack AI skill do?

Pre-code stack feasibility verification against architecture and PRD documents. Use when the user requests to "verify a tech stack" or "verify stack."

Why use Skf Verify Stack on TypingMind?

Because you install it once and use it with any model. Skf Verify Stack 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 Verify Stack in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/armelhbobdad/bmad-module-skill-forge/tree/main/src/skf-verify-stack. 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 Verify Stack?

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 Verify Stack?

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

Is the Skf Verify Stack 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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