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Bmad Spec

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aj-geddes
bmad-spec

Distills ANY messy input — brain dump, transcript, long PRD, stakeholder notes, feature request, voice memo — into a tight five-field SPEC.md kernel that any downstream planning skill can consume. The five fields are: Problem, Capabilities, Constraints, Non-Goals, Success Metrics. Use when the user says "create a spec", "write a spec for", "distill this into a spec", "I have a brain dump", "turn this into something structured", "clean up these notes", "make a SPEC from", "I want to define the problem", "help me scope this", "summarize what we're building", "I have a PRD but need a kernel", "what are we actually solving?", or drops raw text/transcript and asks for structure. Also use when starting any new initiative and a clean shared definition is missing.

Overview

Publisheraj-geddes
Repositoryclaude-code-bmad-skills
Skill namebmad-spec
Stars
492
Forks
77
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by aj-geddes on GitHub. Read the source before you install it.

Installation

Install the Bmad Spec 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/aj-geddes/claude-code-bmad-skills/tree/main/bmad-planning-orchestrator/skills/bmad-spec
  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/aj-geddes/claude-code-bmad-skills.git /tmp/claude-code-bmad-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-bmad-skills/bmad-planning-orchestrator/skills/bmad-spec .claude/skills/bmad-spec
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bmad Spec 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 Bmad Spec 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 Bmad Spec 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.

BMAD Spec — Five-Field Kernel Distiller

Function: Accept messy, unstructured, or verbose input and produce a lean SPEC.md kernel (five fields, no more) that anchors every downstream planning workflow. This is a planning skill. It never writes application code, runs tests, or builds anything.

What it produces

A single file under the configured output folder (default bmad-output/):

bmad-output/
└── SPEC.md    # the five-field kernel

The kernel is intentionally small — a SPEC is not a PRD, not a brief, not an architecture doc. It is the shared definition of what is being built and why. Every downstream skill (PRD, tech-spec, architecture) loads it as the ground truth for scope.

The five fields

FieldPurpose
ProblemThe one thing that hurts right now and why it matters
CapabilitiesWhat the solution must be able to do (outcome-framed)
ConstraintsHard limits that are not negotiable (budget, tech, time, compliance)
Non-GoalsScope that is explicitly out — prevents creep and confusion
Success MetricsObservable signals that confirm the problem is solved

See templates/spec.template.md for the exact template with guidance and examples.

Workflow

1. Ingest

Accept whatever the user hands over:

  • Paste of raw text, notes, or a transcript
  • A path to an existing file (Read it)
  • Verbal description in the chat

If the input exceeds a few hundred words, briefly acknowledge what you received before proceeding. Do not ask the user to reformat it — that is the skill's job.

2. Extract (silent analysis)

Read the input and locate signal for each field:

  • Problem: What pain, gap, or failure state is described? Who experiences it?
  • Capabilities: What outcomes or abilities are requested? Rephrase features as capabilities ("users can …", "the system supports …").
  • Constraints: What is fixed? Look for budget figures, tech mandates, deadlines, regulatory mentions, team-size limits.
  • Non-Goals: What is the input silent about that a reader might assume? What is explicitly ruled out? What is deferred?
  • Success Metrics: What does "done" or "working" look like? Numbers, observable behaviors, thresholds.

3. Draft and present

Write a concise draft of all five fields and show it to the user in the chat before writing to disk. Keep each field tight:

  • Problem: 2–5 sentences max.
  • Capabilities: 3–7 bullet points, outcome-framed.
  • Constraints: 2–6 bullet points, hard limits only.
  • Non-Goals: 2–6 bullet points, clear and unambiguous.
  • Success Metrics: 2–5 bullet points, each with a measurable signal.

After presenting, ask one targeted question: "Does anything here need to change before I write SPEC.md?"

4. Write

Once the user confirms (or revises), write SPEC.md using the template:

${CLAUDE_PLUGIN_ROOT}/skills/bmad-spec/templates/spec.template.md

Output path: <outputFolder>/SPEC.md (read bmad-output/config.yaml if it exists to find the configured output folder; fall back to bmad-output/).

Append a new entry to bmad-output/decision-log.md (create it if absent):

markdown
## SPEC created — <ISO date>
- Source: <one-line description of the input, e.g. "stakeholder brain dump">
- Key scope decision: <the single most important Non-Goal or Constraint>

5. Hand off

After writing, tell the user what the SPEC unlocks:

  • Quick Flow track → hand off to bmad-tech-spec to turn the kernel into a deployable spec.
  • BMad Method / Enterprise track → hand off to bmad-product-brief or the PM role (bmad-prfaq) for a full PRD.
  • If no workspace exists yet, suggest running /bmad-planning-orchestrator:bmad-init first to pick a track.

Three intents

  • Create — distill fresh input into a new SPEC.md (the common case).
  • Update — the user has new information or changed scope. Read the existing SPEC.md, apply the changes, present a diff-style summary, confirm, then overwrite. Record the change in decision-log.md.
  • Validate — review SPEC.md against the five-field contract: are all fields present and non-empty? Is the Problem one coherent statement? Are Capabilities outcome-framed (not feature-list)? Are Non-Goals unambiguous? Flag any gaps and offer to fix them.

Distillation heuristics

Follow these when mapping noisy input to the five fields:

Input patternMaps to
"we need to fix / users complain / it's broken"Problem
"it should / users can / the system supports"Capabilities
"we can't / no budget / must use / by deadline"Constraints
"not in scope / later / out of v1 / won't do"Non-Goals
"if X% then / we'll know it works when / target"Success Metrics

When a constraint sounds aspirational (e.g., "we'd like to finish in Q3"), move it to Non-Goals or flag it as a soft constraint and note the ambiguity.

When capabilities sound like features rather than outcomes, rephrase: "add a search bar" → "users can find any record within 3 keystrokes".

When no success metrics appear in the input, use the Problem statement to derive proxy metrics: if the problem is "users can't find X", a metric is "time-to-find X reduced by Y%".

Guardrails

  • Lean by design. Resist adding a sixth field. If important information does not fit the five fields, note it as context in the file header and direct the user to expand it in the PRD or tech-spec stage.
  • No solutioning. The Capabilities field names outcomes, not implementation choices. Architecture, tech stack, and approach live downstream.
  • No story points, velocity, or burndown anywhere in the SPEC.
  • Scope law. This skill's last artifact is SPEC.md. It does not create stories, write code, or produce acceptance criteria. Hand those to downstream skills.

See REFERENCE.md for extended distillation patterns and edge cases.


Part of the BMAD Planning & Orchestrator plugin — a Claude Code harness for the BMAD Method by the BMAD Code Organization (https://github.com/bmad-code-org/BMAD-METHOD). Implements the spirit of bmad-spec. All methodology credit belongs to the BMAD Code Organization.

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 Bmad Spec AI skill do?

Distills ANY messy input — brain dump, transcript, long PRD, stakeholder notes, feature request, voice memo — into a tight five-field SPEC.md kernel that any downstream planning skill can consume. The five fields are: Problem, Capabilities, Constraints, Non-Goals, Success Metrics. Use when the user says "create a spec", "write a spec for", "distill this into a spec", "I have a brain dump", "turn this into something structured", "clean up these notes", "make a SPEC from", "I want to define the problem", "help me scope this", "summarize what we're building", "I have a PRD but need a kernel",...

Why use Bmad Spec on TypingMind?

Because you install it once and use it with any model. Bmad Spec 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 Bmad Spec in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aj-geddes/claude-code-bmad-skills/tree/main/bmad-planning-orchestrator/skills/bmad-spec. 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 Bmad Spec?

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 Bmad Spec?

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

Is the Bmad Spec AI skill free?

It is published on GitHub by aj-geddes. 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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