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Skf Campaign

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armelhbobdad
skf-campaign

Campaign orchestration — multi-library skill production with dependency tracking, file-based state, and resume. Use when the user asks to "run a campaign" or "orchestrate skills."

Overview

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

Use it in TypingMind

Enable Skf Campaign 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 Campaign 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 Campaign 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.

Campaign

Overview

Orchestrates the production of 15+ skills across multiple sessions by driving them through the full SKF pipeline (brief, generate, compile, test, export) in dependency order. File-based state (_campaign-state.yaml) survives context death, enabling resume from any point.

Conventions

  • Bare paths (e.g. references/step-01-setup.md) resolve from the skill root.
  • references/ holds the stage-chained step files plus reference specs (e.g. the _campaign-directive.md contract at references/campaign-directive-spec.md); templates/, scripts/, and assets/ hold templates, deterministic helpers, and the state schema.
  • {skill-root} resolves to this skill's installed directory (where customize.toml lives).
  • {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/health-check.md and the envelope schemas under shared/scripts/schemas/.
  • 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 campaign orchestrator operating in Ferris's Management mode. You sequence workflows, track per-skill state, enforce quality gates, and ensure every skill reaches its target tier, while the individual pipeline workflows handle the actual artifact production.

On Activation

Run these steps once, in order, before dispatching to Mode Routing.

  1. Load config. Read {project-root}/_bmad/skf/config.yaml and {sidecar_path}/preferences.yaml in one batched message (independent files). From config resolve project_name, user_name, communication_language, document_output_language, skills_output_folder, forge_data_folder, sidecar_path. If the config file is missing, fall back to forge_data_folder = forge-data.

  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-campaign.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, unless the invocation is campaign status (which writes nothing), log customization_resolver_unavailable: <reason> as an event in the decision log once {campaignWorkspacePath} is bound below (references/campaign-contracts.md, Decision Log).

    Resolve each scalar now (so step files never repeat the conditional) and stash as workflow-context variables:

    • {campaignWorkspacePath} ← workflow.campaign_workspace_path if non-empty, else {forge_data_folder}/_campaign
    • {qualityGateHard} / {qualityGateSoftTarget} / {qualityGateSoftFallback} ← the quality_gate_* scalars (defaults zero-critical-high / 90 / 80): the base gate, which step-01 §2 and scripts/campaign-quality-gate.py adjust with the brief and the directive
    • {reportTemplatePath} ← workflow.report_template_path if non-empty, else templates/campaign-report-template.md
    • {kickoffTemplatePath} ← workflow.kickoff_template_path if non-empty, else templates/kickoff-template.md
    • {briefTemplatePath} ← workflow.brief_template_path if non-empty, else templates/campaign-brief-template.yaml
    • {onComplete} ← workflow.on_complete (empty = no-op)
    • {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)

    Load workflow.persistent_facts (literal sentences and file: references, globs expanded: 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) and keep them in mind for the whole campaign: they are injected into every per-skill kickoff, whose loader drops a ! entry the same way. Run any activation_steps_prepend now, right after the resolve: the config and preferences of steps 1 and 2 are already loaded, so a prepend step can read them but cannot run before them. activation_steps_append runs at step 5, once the CLI overrides are parsed.

  4. Parse CLI overrides into the workflow context:

    FlagEffect
    --headless / -HForce {headless_mode} = true (see step 2).
    --brief <file>Seed step-01 targets from a campaign-brief.yaml instead of interactive prompts. Implies --headless.
    --manifest <file>Seed step-01 targets from a plain-text name,repo_url,tier,pin manifest. Implies --headless.
    --from <skill>Resume override — see Mode Routing.

    If --brief or --manifest is set, force {headless_mode} = true (log "headless: coerced by --brief/--manifest" if it was false). This is deliberate: a seeded run is an unattended run (CI), so it takes the default at every gate. To review the plan and the export, give the same file at the Setup question instead, which stays interactive. step-01 §1 documents the manifest format and validates every target.

  5. Run any activation_steps_append, then dispatch per Mode Routing below.

Workflow Rules

These rules apply to every step in this workflow:

  • State-first: write state to disk before chaining to the next step or workflow
  • Every state write goes through scripts/campaign-state.py (State Contract in references/campaign-contracts.md); never edit the state or its .bak by hand
  • Validate _campaign-state.yaml on every load by running uv run scripts/campaign-validate-state.py --state-file {stateFile} and HALT (exit code 3, invalid-state) on non-zero; never hand-validate the schema
  • Zero memory dependency: campaign state is 100% recoverable from disk; never rely on conversation context for progress tracking
  • A sub-skill's result is what the step that runs it reads (an SKF_*_RESULT_JSON envelope or a verdict): a missing, unparseable or error result is a sub-skill failure, and never write partial state from an unparsed envelope
  • Log every operator decision, headless default and event (skip/force, overwrite, export cancel/proceed, .bak recovery, user-cancel, a failed skill) as one typed entry in the append-only {campaignWorkspacePath}/_campaign-decision-log.md through campaign-state.py log (references/campaign-contracts.md), so rationale survives compaction and resume
  • Universal cancel affordance: at any interactive gate between Setup and the Export gate, cancel/exit/:q triggers a HARD HALT with exit code 12 (user-cancelled): log it and leave state intact and resumable. Exception: the Export gate's own [C]ancel stays exit code 11 (export-cancelled); never also emit 12 there, so an automator's exit-code branch stays deterministic.
  • Always communicate in {communication_language}
  • If {headless_mode} is true, auto-proceed through confirmation gates with their default action and log each auto-decision
  • If {headless_mode} is true, emit a single-line JSON progress event to stderr at each step's entry, exit, and HARD HALT, and at every HARD HALT the error envelope (references/campaign-contracts.md: Headless Progress Events, Result Contract on HARD HALT)

Stages

#StepFileAuto-proceed
0Setupreferences/step-01-setup.mdYes
1Strategyreferences/step-02-strategy.mdNo (plan confirmation; headless takes [P])
2Pin Validationreferences/step-03-pins.mdYes
3Provenancereferences/step-04-provenance.mdYes
4Skill Loopreferences/step-05-skill-loop.mdConditional (a blocked dependency)
5Tier B Batchreferences/step-06-batch.mdYes
6Capstonereferences/step-07-capstone.mdYes
7Verificationreferences/step-08-verify.mdYes
8Refinementreferences/step-09-refine.mdYes
9Exportreferences/step-10-export.mdNo (write-gate HALT)
10Maintenancereferences/step-11-maintenance.mdYes

Stage numbering: campaign.current_stage is 0-indexed, so step-NN runs stage NN - 1; campaign-state.py resume maps a resumed campaign to its step file.

Invocation Contract

AspectDetail
Inputscampaign to start a new campaign; campaign resume [--from=<skill>] to resume from last active or specified skill; campaign status for a read-only progress summary
Outputs_campaign-state.yaml (state), campaign-brief.yaml (machine-generated brief), campaign-report.md (post-campaign summary), _campaign-decision-log.md (append-only rationale), all under {campaignWorkspacePath}; SKF_CAMPAIGN_RESULT_JSON (headless envelope)

Contracts

Exit codes, the HARD-HALT error envelope, the State Contract (scripts/campaign-state.py), the decision log and per-step progress events live in references/campaign-contracts.md: consult it when you HALT, write state, or log. The envelope's one definition is shared/scripts/schemas/skf-campaign-result-envelope.v1.json; step-11 emits its success line.

Mode Routing

On invocation:

  1. campaign resume [--from=<skill>]: load references/step-resume.md (validates state, recovers from backup, chains to the stage campaign-state.py resume computes). --from=<skill> overrides the resume point to the named skill.
  2. campaign (new, no existing state) to run from stage 0 (Setup).
  3. campaign (state exists): when {campaignWorkspacePath}/_campaign-state.yaml or its .bak exists, prompt resume (via references/step-resume.md) or overwrite. On overwrite, first run uv run scripts/campaign-state.py archive --state-file {campaignWorkspacePath}/_campaign-state.yaml --brief-file {campaignWorkspacePath}/campaign-brief.yaml, which moves the state, its .bak and the brief into a new folder under {campaignWorkspacePath}/archive/, and log the archive_dir it returns (type decision) before chaining to step-01 (its init refuses to overwrite a state or backup). A --brief that named the workspace's campaign-brief.yaml is then read from archive_dir. GATE [default: resume]: in headless mode, default to resume (never silently clobber); archive-and-overwrite only when --brief/--manifest explicitly seeds a new campaign.
  4. campaign status (read-only): load {campaignWorkspacePath}/_campaign-state.yaml, validate it via campaign-validate-state.py, then run uv run scripts/campaign-status.py --state-file {campaignWorkspacePath}/_campaign-state.yaml and display its summary (campaign name, current stage, completed-vs-total, per-status counts) followed by the last ~15 lines of {campaignWorkspacePath}/_campaign-decision-log.md for the recent decision trail, then stop. No backup, no mutation, no chaining. Exit 0 (or 9 if the state is unrecoverable).

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 Campaign AI skill do?

Campaign orchestration — multi-library skill production with dependency tracking, file-based state, and resume. Use when the user asks to "run a campaign" or "orchestrate skills."

Why use Skf Campaign on TypingMind?

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

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

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 Campaign?

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

Is the Skf Campaign 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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