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Skf Test Skill

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armelhbobdad
skf-test-skill

Cognitive completeness verification — quality gate before export. Use when the user requests to "test a skill" or "verify skill completeness."

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-test-skill
Stars
100
Forks
11
Bundled files
31
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.

  • 31 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 Test 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-test-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-test-skill .claude/skills/skf-test-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skf Test 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 Test 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 Test 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.

Test Skill

Overview

Verifies that a skill is complete enough to be useful to an AI agent by checking coverage of the public API surface (naive mode) or validating SKILL.md + references coherence (contextual mode). Produces a completeness score and gap report as a quality gate before export.

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; 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. The coherence and migration rules cite skf-create-skill/assets/skill-sections.md and skf-create-stack-skill/references/compose-mode-rules.md.
  • references/ holds prompt content carved out of SKILL.md (workflow stages chained via frontmatter nextStepFile, plus static reference docs); scripts/ holds deterministic helpers, assets/ the output section formats and templates/ the test report template.
  • {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 skill auditor and completeness analyst operating in Ferris's Audit mode. This is a deterministic quality gate — you bring AST-backed analysis expertise and zero-hallucination verification, while the skill artifacts provide the evidence.

Workflow Rules

These rules apply to every step in this workflow:

  • Zero hallucination — every finding must trace to actual code with file:line citations
  • Only load one step file at a time — never preload future steps
  • Update stepsCompleted in output file frontmatter before loading next step
  • Always communicate in {communication_language}
  • No stage waits for an answer in headless mode: the one question, the skill name, halts input-missing there
  • Every HARD HALT names its halt_reason and phase and carries exit code 1 (exit_code in its envelope); in headless mode it prints its envelope through the shared emitter, as each stage's Halt envelope paragraph shows
  • Once references/init.md §6a has taken the run lock, every HALT after it first releases the lock, whatever the release prints (it never removes another run's lock): from {project-root}, run uv run {runLockHelper} release --lock "{forge_version}/.test-skill.lock" --owner "{run_owner}".
  • The report is {report_file}: init.md §6c creates it as .skf-test-report-{skill_name}-{run_id}.md, and it takes its public name test-report-{skill_name}-{run_id}.md only once report.md §4c's checks pass, a blocked run's included, so a halted run never leaves a partial report where export-skill and update-skill look. The steps read the verdict inputs (threshold, toolingStatus, workspaceDrift, testResult) from its frontmatter, never from memory
  • Run state (the scripts' files, the emitter's payloads) lives in {run_dir}: report.md §7 deletes it, a HALT keeps it

Stages

#StepFileAuto-proceed
1Initialize & Load Skillreferences/init.mdYes
2Detect Modereferences/detect-mode.mdYes
3Coverage Checkreferences/coverage-check.mdYes
4Coherence Checkreferences/coherence-check.mdYes
4bExternal Validatorsreferences/external-validators.mdYes
4cHard Gatereferences/step-hard-gate.mdYes
5Scorereferences/score.mdYes
6Reportreferences/report.mdYes
7Workflow Health Checkreferences/health-check.mdYes

Invocation Contract

Headless callers: the full argument set (Inputs), the outputs, the exit codes and the SKF_TEST_RESULT_JSON result envelope live in references/invocation-contract.md. Interactive runs do not need it.

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. 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-test-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}: report.md §4c hands it to the result as a warning.

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

    • {testReportTemplatePath} ← workflow.test_report_template_path if non-empty, else templates/test-report-template.md
    • {defaultThreshold} ← workflow.default_threshold if non-empty/non-null, else 80. init.md §1b resolves the precedence (CLI, then per-pipeline default, then this scalar) once and writes threshold and thresholdSource into the report frontmatter, which score.md §1 reads.
    • {onCompleteCommand} ← workflow.on_complete if non-empty, else empty string (the post-finalization hook in references/report.md is then a no-op).

    Stash all three as workflow-context variables that stage files reference directly, with no conditional at the usage site. The scoring rules, the Gap Severity table and the output formats are not overridable: scripts own the scoring and the hard gate reads the table, so a house-style copy could change what blocks export.

    Apply the array surfaces (not silent no-ops): run workflow.activation_steps_prepend in order now; treat each workflow.persistent_facts entry 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 after activation.

  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 Test Skill AI skill do?

Cognitive completeness verification — quality gate before export. Use when the user requests to "test a skill" or "verify skill completeness."

Why use Skf Test Skill on TypingMind?

Because you install it once and use it with any model. Skf Test 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 Test 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-test-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 Test 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 Test Skill?

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

Is the Skf Test 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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