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Workflow Blueprint

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devton
workflow-blueprint

Blueprint workflow to scaffold agent-oriented documentation with progressive disclosure and executable contracts.

Overview

Publisherdevton
Repositoryagentic-workflow-blueprint
Skill nameworkflow-blueprint
Stars
73
Forks
4
Bundled files
8
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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by devton on GitHub. Read the source before you install it.

Installation

Install the Workflow Blueprint 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/devton/agentic-workflow-blueprint/tree/main
  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/devton/agentic-workflow-blueprint.git \
  .claude/skills/workflow-blueprint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workflow Blueprint 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 Workflow Blueprint 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 Workflow Blueprint 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.

agentic-workflows.blueprint

Goal

Provide a reusable blueprint workflow that scaffolds an "agentic workflows" documentation structure for any codebase, with progressive disclosure and executable contracts (workflows).

Scope

  • Applies to: any repository that wants an agent-oriented documentation system (orchestrator + workflow skills + references + runbooks).
  • Does not cover: implementing product features; this is scaffolding/documentation only.

Triggers

  • "Create agentic workflow structure"
  • "Set up skills folder + workflows"
  • "Make docs agent-friendly"
  • "Refactor AGENTS.md into linked skills"

Inputs

  • projectSlug: short identifier for the repo (e.g. my-backend)
  • baseBranch: default integration branch (e.g. develop, main)
  • techStack: short list (e.g. NestJS + MikroORM + Graphile Worker)
  • existingRootDoc: root instruction file path (AGENTS.md, CLAUDE.md, etc.)
  • workflowsWanted: list of workflow ids to scaffold (e.g. modules, specs, document, network-engineering, infra-operations, iac, os-platform, plan-to-blueprint)
  • constraints: project hard rules (e.g. "no emojis", "mock external boundaries only")
  • skillName (optional): slash-invocation name for the project entry skill; defaults to projectSlug (lowercase, hyphens only)

Outputs

Creates a minimal, navigable structure:

skills/<projectSlug>/
  SKILL.md
  template.json                     ← Declarative manifest (commands, entry)
  reference/
    routing-matrix.md
    role-contracts.md
    hook-blueprint.md (optional)
  workflows/
    <workflowName>/
      SKILL.md
      template.json (optional)      ← Per-workflow manifest when useful
docs/runbooks/
  agent-role-system.md
  agent-role-hooks.md (optional)
  plan-to-blueprint.md (when workflow included)

And updates the root doc (AGENTS.md or equivalent) to link to the new entrypoints.

This blueprint folder carries bundled workflows (embed-aihero-radioactive, mattpocock/wayfinder, mattpocock/grilling, mattpocock/domain-modeling, mattpocock/research, mattpocock/prototype, mattpocock/to-spec, radioactive, brainstorming, plan-writing, ui-ux-pro-max, remotion-video-motion, thermo-nuclear-code-quality-review, thermo-fix, changelog-generator, document, review, changelog, linear, mcp-linear-planner, mcp-linear-sync, network-engineering, infra-operations, iac, os-platform, implementing-devsecops-security-scanning, scanning-containers-with-trivy-in-cicd, scanning-docker-images-with-trivy, scanning-kubernetes-manifests-with-kubesec, implementing-network-policies-for-kubernetes, implementing-rbac-hardening-for-kubernetes, implementing-pod-security-admission-controller, securing-aws-iam-permissions, securing-container-registry-images, securing-kubernetes-on-cloud, triaging-vulnerabilities-with-ssvc-framework, performing-kubernetes-cis-benchmark-with-kube-bench, analyzing-kubernetes-audit-logs, securing-github-actions-workflows, performing-container-image-hardening, remediating-s3-bucket-misconfiguration, performing-container-security-scanning-with-trivy, performing-vulnerability-scanning-with-nessus, implementing-syslog-centralization-with-rsyslog, c4-architecture, plan-to-blueprint) to demonstrate full-lifecycle chained execution, Matt Pocock / AI Hero methodology embedding, plan-to-skill transformation, visual documentation generation, MCP integration patterns, and infrastructure operations coverage.

It can also carry runbook examples under runbooks/ to show operator-facing execution playbooks for those workflows.

Invariants (guardrails)

  • Progressive disclosure: root doc stays short; details live behind links.
  • Executable contracts: every workflow is written as a contract the agent can follow:
    • Goal, Scope, Triggers, Inputs, Invariants, Procedure, Outputs, Review gate, References.
  • No duplication: do not copy/paste long rules across files; link to the source of truth.
  • Consistency: workflow ids and file paths must match exactly across all references.
  • Minimal surface: only add the workflows actually requested.
  • Source of truth: SKILL.md is authoritative; template.json is a complementary declarative layer for agents/tools that expose command interfaces.
  • Command routing: router-only — users invoke /<skillName> <cmd>; the entry skill resolves cmd to workflows/<cmd>/SKILL.md and loads only that contract.
  • Manifest sync: every command in template.json must map to an existing workflow folder and match routing-matrix.md.

Procedure

1) Create the project entry skill

Create skills/<projectSlug>/SKILL.md as the global entrypoint:

  • A short description of what the skill is for.
  • An "orchestrator" section that explains:
    • how to classify a task
    • how to select one workflow
    • how to close (validation gate if applicable)
  • A Command routing section (router-only):
    • invocation pattern: /<skillName> <cmd> (e.g. /my-backend document)
    • map each cmd in workflowsWanted to workflows/<cmd>/SKILL.md
    • if the agent does not parse subcommands, read cmd from the user message and load the mapped workflow contract
    • do not create flat alias skills unless explicitly requested
  • A list of internal workflow helpers:
    • workflows/<name>/SKILL.md links
  • Project constraints and hard rules (short bullets).

Create skills/<projectSlug>/template.json as the declarative manifest:

json
{
  "name": "<skillName>",
  "version": "1.0.0",
  "entry": "SKILL.md",
  "routing": "router-only",
  "commands": [
    {
      "name": "<workflowName>",
      "description": "Short trigger description",
      "skill": "workflows/<workflowName>/SKILL.md"
    }
  ]
}
  • Include one commands[] entry per item in workflowsWanted.
  • Keep name aligned with skillName (folder may remain projectSlug).
2) Create references (progressive disclosure)

In skills/<projectSlug>/reference/, create:

  • routing-matrix.md: task category -> workflow mapping; include slash form /<skillName> <cmd> alongside intent triggers.
  • role-contracts.md: roles, boundaries, handoffs.
  • Optional hook-blueprint.md: opt-in automation checklist.

Keep each file self-contained and linkable.

3) Scaffold each workflow as an executable contract

For each workflow in workflowsWanted, create:

skills/<projectSlug>/workflows/<workflowName>/SKILL.md with:

  • id: "<projectSlug>.workflow.<workflowName>" (as the first header line)

  • Goal: 1 sentence

  • Scope: applies/does not cover

  • Triggers: file triggers + intent triggers

  • Inputs: baseBranch, diff scope, required config

  • Invariants: the project's hard rules + workflow-specific rules

  • Procedure: deterministic steps (evidence-driven; use git diff when documenting)

  • Outputs: what files/notes/checkpoints must be produced

  • Review gate: checklist with pass/fail criteria

  • References: links back to skills/<projectSlug>/SKILL.md and any deep dives

  • Special Scaffold Case — embed-aihero-radioactive: When requested, adapt skills/<projectSlug>/workflows/radioactive/SKILL.md to run the 14-phase lifecycle embedding Matt Pocock's skills, and scaffold skills/<projectSlug>/workflows/mattpocock/ containing wayfinder, grilling, domain-modeling, research, prototype, and to-spec. Ensure Phase 1 ALWAYS executes the primary project skill (skills/<projectSlug>/SKILL.md) FIRST for request classification and constraint extraction before executing /wayfinder.

Optionally, for each workflow, add workflows/<workflowName>/template.json when an external tool needs a standalone command descriptor; keep it minimal (name, entry, parent).

4) Wire everything into the root doc

Update existingRootDoc to include:

  • "Start here": link to skills/<projectSlug>/SKILL.md
  • Under "Skills" (or similar), list:
    • the project skill link
    • internal workflow skills links
    • runbook links

Do not duplicate workflow contents in the root doc.

5) Optional: deprecate legacy skill locations (wrapper)

If there are existing skills in other directories:

  • Keep the file
  • Add a top banner:
    • "Moved: canonical workflow is at skills/<projectSlug>/workflows/..."
  • Leave the rest as a deep dive reference
6) Consistency verification (required)

Before declaring the scaffold done:

  • Verify every link path exists (.md).
  • Verify workflow ids are consistent:
    • projectSlug.workflow.* matches the file it lives in.
  • Verify root doc points only to canonical locations.
  • Verify template.json commands[].name values exist under workflows/ and appear in Command routing + routing-matrix.md.
  • Verify router-only behavior is documented in the project entry SKILL.md (no undocumented flat aliases).

Review gate (must pass)

  • Root doc remains minimal and only links out.
  • Each workflow has the full contract sections (Goal..References).
  • Constraints are explicit and testable (no vague "best practices").
  • No duplication between root, project skill, and workflows.
  • All links resolve (.md).
  • template.json is valid JSON and consistent with workflowsWanted.
  • Command routing resolves every listed subcommand to exactly one workflow contract.

Notes

  • This blueprint is intentionally stack-agnostic. For stack-specific rules (logging, ORM patterns, testing rules, network policies, IaC standards, and OS baselines), keep them in the project skill and link them from workflows.
  • Installable skill contract with frontmatter: see SKILL.md (name: workflow-blueprint).

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 Workflow Blueprint AI skill do?

Blueprint workflow to scaffold agent-oriented documentation with progressive disclosure and executable contracts.

Why use Workflow Blueprint on TypingMind?

Because you install it once and use it with any model. Workflow Blueprint 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 Workflow Blueprint in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/devton/agentic-workflow-blueprint/tree/main. 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 Workflow Blueprint?

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 Workflow Blueprint?

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

Is the Workflow Blueprint AI skill free?

It is published on GitHub by devton. 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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