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Skf Analyze Source

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armelhbobdad
skf-analyze-source

Discover what to skill in a large repo and produce recommended skill briefs. Use when the user requests to "analyze source for skills" or "discover skill opportunities."

Overview

Publisherarmelhbobdad
Repositorybmad-module-skill-forge
Skill nameskf-analyze-source
Stars
100
Forks
11
Bundled files
22
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.

  • 22 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 Analyze Source 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-analyze-source
  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-analyze-source .claude/skills/skf-analyze-source
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skf Analyze Source 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 Analyze Source 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 Analyze Source 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.

Analyze Source

Overview

Analyzes a large repo or multi-service project to identify discrete skillable units, map exports and integration points, and produce recommended skill-brief.yaml files as the primary entry point for brownfield onboarding. The analysis must be thorough enough to produce actionable briefs, but scoped enough to avoid overwhelming the user with false positives. Exports come from the ast-grep recipe runner at every forge tier (read by eye where ast-grep is missing or the language has no recipe).

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/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.
  • references/ holds prompt content carved out of SKILL.md (workflow stages chained via frontmatter nextStepFile, plus static reference docs); assets/ holds the brief schema and templates/ the analysis report skeleton.
  • {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 source code analyst and decomposition architect collaborating with a developer onboarding an existing project, pairing your codebase-analysis and skill-scoping expertise with their domain knowledge.

Workflow Rules

These rules apply to every step in this workflow:

  • Only load one step file at a time — never preload future steps
  • Always communicate in {communication_language}; the text persisted into skill-brief.yaml (each recommendation's description and scope.notes) is written in {document_output_language}
  • If {headless_mode} is true, auto-proceed through confirmation gates with their default action, and record each one in the run sink as the gate says

Stages

#StepFileAuto-proceedCondition
1Initializereferences/init.mdYesAlways
1aAuto-Scopereferences/step-auto-scope.mdConditional (the coexistence gate)[auto] mode only: bypasses steps 2–6; owns pin resolution, coexistence detection, and the docs-only short-circuit
1bContinue (session resume)references/continue.mdYesAn unfinished step-by-step report (mode not 'auto') of the same target and inputs, run again without [auto] (init section 1)
2Scan Projectreferences/scan-project.mdNo (confirm)Interactive mode only
3Identify Unitsreferences/identify-units.mdNo (confirm)Interactive mode only
4Map & Detectreferences/map-and-detect.mdNo (confirm)Interactive mode only
5Recommendreferences/recommend.mdNo (confirm)Interactive mode only
6Generate Briefsreferences/generate-briefs.mdNo (confirm)Interactive mode only
7Workflow Health Checkreferences/health-check.mdYesAlways

Auto mode path: With [auto] present, init routes directly to step 1a, which loads a branch file only when its branch fires: references/step-auto-scope-coexistence.md (a skill already matches), references/step-auto-scope-split.md (a monorepo splits into N briefs) and references/step-auto-scope-corpora.md (a whole-language reference), and routes docs-only targets to references/auto-docs-only.md.

[D] Discover Additional Source (steps 4 and 5) loads references/discover-additional-source.md. Step 2 and [D] make each project path's scan root by references/scan-root.md (steps 3 and 4 make one again when a resumed session no longer finds it), and step 4 and [D] map each unit's exports by references/map-unit-exports.md.

Shape detection reference: references/step-shape-detect.md — loaded by step 1a as a reference doc (not a chained step).

Invocation Contract

AspectDetail
Inputsproject_path [required], intent_hint, scope_hint, target_ref or target_refs [optional]. project_path is a repository URL or a local path, or in [auto] mode a documentation URL (routed docs-only). An interactive run reads them from the invocation and asks at most one opening question.
Headless inputs--project-path <path> (comma-separated for several), --scope-hint <text> (folders or packages to focus on or skip, [auto] included), --intent-hint <text> (the goal: ranks the step 5 recommendations; an interactive run also defers the units outside it before step 4, restorable, a headless run never; [auto] keeps a merged repo's facets it names), --target-ref <ref> (a tag or branch for every project path, written into every brief; not [auto], which pins with --pin), --target-refs <path:ref,...> (one ref per path, written into each unit's brief; not with --target-ref, and not [auto]), --pin <version> ([auto] only: a tag or branch; absent, the latest release tag; a step-by-step analysis an [auto] run falls back to keeps its pin)
Headless flag--headless / -H flips every confirm gate to auto-proceed
Auto flag[auto] bracket modifier — activates auto-scope mode (step 1a; see Auto mode path above). Pipelines pass this as AN[auto]. Requires --project-path.
Gatesone per step: steps 2 to 4 [C] (step 4 also takes composite merges, default accept); step 5 [C] after the decisions (default: confirm every card, flag every stack skill candidate); step 6 [Y] (write briefs). Headless takes each default and records it in headless_decisions. Auto mode: only the step 1a coexistence gate (default [A]longside)
Outputsanalysis-report.md, skill-brief.yaml files (one per recommended unit) and the run's result files, written by every run that ends, zero confirmed units included; final SKF_ANALYZE_RESULT_JSON line on stdout when {headless_mode} is true. In auto mode, the envelope includes "mode":"auto".
Exit codesEnvelope, emitter, result files, halt_reason enum and exit codes: references/headless-contract.md

On Activation

  1. Resolve the envelope helper. {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. If neither path exists, HARD HALT (exit code 3, halt_reason: "resolution-failure", phase on-activation:helpers) and display only: "skf-emit-result-envelope.py is missing. Nothing was changed. Re-install SKF." In headless mode every other HARD HALT prints its envelope on stderr through the emitter: with the first command below once step 5 has created {run_dir}, before that with the second, the payload on one line. references/headless-contract.md (Halt Envelope) gives the payload rules, and each step file restates them in its opening.

    bash
    uv run {emitEnvelopeHelper} emit-halt --workflow skf-analyze-source --run-dir "{run_dir}" --target stderr < "{run_dir}/halt.json"
    uv run {emitEnvelopeHelper} emit-halt --workflow skf-analyze-source --target stderr <<'SKF_ANALYZE_HALT'
    {"phase": "on-activation:config", "reason": "<the halt message>", "halt_reason": "input-missing", "mode": "interactive"}
    SKF_ANALYZE_HALT
  2. Load config from {project-root}/_bmad/skf/config.yaml and resolve:

    • project_name, output_folder, user_name, communication_language, document_output_language, forge_data_folder, skills_output_folder, sidecar_path
    • If the config cannot be loaded, HARD HALT (exit code 2, halt_reason: "input-missing", phase on-activation:config): "SKF cannot load {project-root}/_bmad/skf/config.yaml: the workflow has no forge context to run against. Run setup first." When --headless or -H was passed, print its envelope with the second command above.
  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-analyze-source.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 step 5 to record in the run sink.

    Then bind, so stage files use the variable with no conditional at the usage site:

    • {analysisReportTemplatePath} ← workflow.analysis_report_template_path (the bundled templates/analysis-report-template.md when an override leaves it empty)
    • {onCompleteCommand} ← workflow.on_complete (empty: no hook)

    Run workflow.activation_steps_prepend now, and 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).

  5. Create the run folder. It holds what the run stages for its helpers (the tree listing, each brief's context, the subagents' unit records, the halt and result payloads) and the sink of its auto-decisions:

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

    Bind {run_dir} ← the path it prints. The step that ends the run deletes it once the envelope is out; a HARD HALT keeps it. If it cannot be created, HARD HALT (exit code 4, halt_reason: "write-failed", phase on-activation:run-folder, path {project-root}/_bmad-output/.skf-run): "SKF cannot create its run folder under {project-root}/_bmad-output/.skf-run/: {the first stderr line}. Nothing was changed." Its envelope goes through the second command of step 1, its payload with "customization_resolver_unavailable": "<reason>" added when step 4 kept one. Once the folder 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 (the reason can hold quotes or $( )), then run uv run {emitEnvelopeHelper} record --run-dir "{run_dir}" --warning "$(cat "{run_dir}/resolver-warning.txt")".

  6. Run workflow.activation_steps_append, then load, read the full file, and 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 Analyze Source AI skill do?

Discover what to skill in a large repo and produce recommended skill briefs. Use when the user requests to "analyze source for skills" or "discover skill opportunities."

Why use Skf Analyze Source on TypingMind?

Because you install it once and use it with any model. Skf Analyze Source 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 Analyze Source in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/armelhbobdad/bmad-module-skill-forge/tree/main/src/skf-analyze-source. 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 Analyze Source?

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 Analyze Source?

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

Is the Skf Analyze Source 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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