Agent skill

Bmad Architecture

by delorenj in delorenj/mcp-server-trello

Produce the architecture: a lean spine of invariants that keeps everything built from it consistent, projected into whatever format the work needs.

MITAuto-check passed

Install Bmad Architecture

skills CLI
$ npx skills add delorenj/mcp-server-trello --skill bmad-architecture -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install delorenj/mcp-server-trello bmad-architecture --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/delorenj/mcp-server-trello.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agent/skills/bmad-architecture .claude/skills/bmad-architecture && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bmad-architecture
GitHub stars
445
Used in
1 other repo
Token cost
~3.5k tokens
SKILL.md length
1,938 words
Files
7 (incl. scripts, references, assets)
Skills in repo
64
Repo updated
First seen
Licence
MIT

At a glance

Produce the architecture: a lean spine of invariants that keeps everything built from it consistent, projected into whatever format the work needs.

  • Works in 6 steps: Resolve customization: uv run… → Resolve config: uv run… → Headless (no interactive user) → follow… → …
  • The user says create the architecture
  • SKILL.md covers Overview, How you work, Read the input to know the job and How a run works, plus 6 more sections
  • Runs Python scripts from its folder; calls uv

What it does

Bmad Architecture is an agent skill from delorenj/mcp-server-trello. Produce the architecture: a lean spine of invariants that keeps everything built from it consistent, projected into whatever format the work needs. Use when the user says "create the architecture", "create technical architecture", "architecture spine", or "create a solution design".

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/spine-template.md`, `references/headless.md` and `references/reviewer-gate.md`).

The repository describes itself as: A Model Context Protocol (MCP) server that provides tools for interacting with Trello boards. The licence is MIT.

When your agent uses it

  • The user says create the architecture
  • Create technical architecture
  • Architecture spine
  • Create a solution design

Example prompts

  • “create the architecture”
  • “create technical architecture”
  • “architecture spine”
  • “/bmad-architecture”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Resolve customization: uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow (on failure read…
  2. Resolve config: uv run {project-root}/_bmad/scripts/resolve_config.py --project-root {project-root} (merges _bmad/config.toml…
  3. Headless (no interactive user) → follow references/headless.md for the whole run. Otherwise greet {user_name} in {communication_language}…
  4. If a run folder for this target already exists under {workflow.spine_output_path}, offer to resume from its memlog rather than restart.
  5. Interactive create: offer the working mode in {communication_language} — Coaching path (default) or Fast path (see How you work) — before…
  6. Mandatory, both paths, before drafting: ask whether the spine is the only deliverable — and if not, draw out the purpose and audience…

What it can do on your machine

Read from SKILL.md and the folder at commit 737292f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bmad Architecture loads about 3.5k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 1,938 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~75
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from delorenj/mcp-server-trello at commit 737292f, republished under its MIT licence (© delorenj). 1,938 words, ~3,484 tokens.

Download SKILL.mdSave it as .claude/skills/bmad-architecture/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
bmad-architecture
description
Produce the architecture: a lean spine of invariants that keeps everything built from it consistent, projected into whatever format the work needs. Use when the user says "create the architecture", "create technical architecture", "architecture spine", or "create a solution design".

BMad Architecture

Overview

You produce an architecture spine: a consistency contract that fixes only the invariants keeping independently-built units from diverging — the design paradigm, the boundary and dependency rules, how state is mutated, who owns shared data — the durable calls a future builder can't read off compliant code. Everything structural (stack, tree, full data shape) is seed: true at cold-start, owned by the code once it exists. Lead with a named paradigm — it carries a whole model for free — and keep the seed minimal.

One test decides what belongs:

If two units one level down built this independently, could they choose incompatibly? Fix it here only when the answer is yes, and the call is non-obvious, and it's a real trade-off. Otherwise name it under Deferred and move on.

Default output is a build substrate — terse and convergent, so small agents and humans on small intents don't drift. When the goal is instead to align people, lead with a discussion doc that keeps the open questions in front. Match the spine to what's in front of you: a few decisions for a small thing, comprehensive for a platform; the whole system or the one slice a feature touches.

Record decisions, not rationale (rationale lives in the memlog). Carry shape in diagrams, not prose. Verify any named technology's current version and fit on the web before binding it.

How you work

You're a coach, and the Coaching path is the default — the elicitation is the value, and it cuts against the instinct to just produce an architecture, so hold the line. Offer the choice as an Activation step, in the user's language, before any drafting: Coaching path (we work it together — open-ended questions, I pull the decisions out of you and push back where one is thin) or Fast path (I draft the whole spine fast with [ASSUMPTION] tags you correct in review). Unless the user clearly wants speed, coach; don't silently draft. The load-bearing calls — paradigm, stack or starter, the major boundaries — are shown, not silently made: lay out the realistic alternatives you weighed and why you lean one way, then let the user choose. That rationale lives in the conversation and the memlog, never in the terse spine.

Elicit, don't quiz: open-ended "how are you thinking about X?" beats a multiple-choice menu; reserve a crisp either/or for a genuinely binary fork. On the Fast path, inferring and tagging is the job.

When the stack is open — greenfield, or a small/beginner project that could sit on a paved path — recommend a well-known current starter (verify the going choice on the web first): a good one pre-decides a coherent slab of the architecture for free and beats hand-rolling for a less-experienced user. For brownfield, investigate before you decide — read enough of the real code (and {workflow.persistent_facts}) to ratify the conventions already there rather than invent new ones — and don't re-tell the user what the scan already shows.

Read the input to know the job

The input itself tells you what kind of job this is — read it rather than quizzing the user about it. A spec package (SPEC.md + its memlog) is the richest start and the spine's home, so fold the spine back into it. But you'll also get a raw idea, a sprawling architecture document to distill down, an existing codebase to derive a spine from (ratify the conventions the code already shows — don't re-document them), the slice of one a new feature touches, or an existing spine to extend or pressure-test. Prefer a .memlog.md over re-reading the source it came from. Distill whatever you're given; mark real gaps as open questions instead of inventing answers. The spine's altitude mirrors what it augments and keeps the level below coherent — initiative→features, feature→epics, epic→stories. Inherit what's already settled — whether by the input (a spec, prd) or the standing {workflow.persistent_facts} — silently; don't re-decide or re-ask it. If the input is too thin to build on, suggest bmad-spec first; else capture the missing answers into a shared spec workspace through the same memlog.py, so bmad-spec can later derive SPEC.md without drift.

Inheriting a parent spine (e.g. pointed at one epic of a spec whose feature/initiative spine already exists): load the parent ARCHITECTURE-SPINE.md first and treat its ADs, conventions, and paradigm as binding, read-only constraints — log each as a constraint entry, list them under the spine's Inherited Invariants (parent AD IDs, never renumbered), and don't re-derive them. Your job is only what the parent left open: its Deferred items plus the divergences this epic's stories could hit. A new AD that contradicts or weakens an inherited one is a conflict to surface, not a local override. An epic spine fixes the invariants the epic's stories must share — it does not expand per-story detail.

How a run works

The memlog (.memlog.md) is the run's working memory: every decision, constraint, version, assumption, and open question lands as one append-only line — for a decision, capture what it binds and the divergence it prevents. It carries no lifecycle status — terminal moments are logged as event entries, not a frontmatter flag. The spine file itself is distilled from the memlog at the end, not written as you go. Each surviving decision becomes an AD-n (stable ID, Binds/Prevents/Rule, [ADOPTED] when the user or existing reality already settled it); a decision that lives only in a diagram still gets logged. Resume a prior run by reloading its memlog.

Writes go through the shared script (don't read the file back except on resume):

  • uv run {project-root}/_bmad/scripts/memlog.py init --workspace {doc_workspace} --field scope="…" --field purpose="…" --field altitude="…"
  • uv run {project-root}/_bmad/scripts/memlog.py append --workspace {doc_workspace} --type <decision|constraint|version|assumption|question|direction|event> --text "…"

Resolution rules

  • Bare paths and {skill-root} (e.g. references/headless.md) resolve from this skill's installed directory.
  • {project-root} → the project working directory; {skill-name} → the skill directory's basename.
  • {workflow.<name>} → a merged customize.toml field; {doc_workspace} → the bound run folder.
  • Forward slashes only. Config variables already contain {project-root} in their resolved values — never double-prefix.

On Activation

Forwarded activation: if a caller invoked you with a stated intent and pre-resolved customization fields, honor them verbatim — skip your own intent inference, use the supplied values for those named fields, and resolve only the remaining fields from your own customize.toml.

  1. Resolve customization: uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow (on failure read {skill-root}/customize.toml, use defaults). Run {workflow.activation_steps_prepend}, then {workflow.activation_steps_append}. Hold {workflow.persistent_facts} as standing context — the default loads project-context.md, load-bearing for brownfield — and consult {workflow.external_sources} on demand.
  2. Resolve config: uv run {project-root}/_bmad/scripts/resolve_config.py --project-root {project-root} (merges _bmad/config.toml, _bmad/config.user.toml, and the _bmad/custom/ overrides). From the merged JSON resolve {user_name}, {communication_language}, {document_output_language}, {project_name} (under core), {planning_artifacts} (under modules.bmm), and {date}; missing keys take neutral defaults, never block.
  3. Headless (no interactive user) → follow references/headless.md for the whole run. Otherwise greet {user_name} in {communication_language}. Detect the intent from the conversation and input — create (the default), update an existing spine, or validate one (see those sections). If the real ask is requirements / UX / a capability contract / epic breakdown / an agent, invoke the bmad-prd, bmad-ux, bmad-spec, bmad-create-epics-and-stories, or bmad-workflow-builder (if the BMad Builder module is installed) skill instead.
  4. If a run folder for this target already exists under {workflow.spine_output_path}, offer to resume from its memlog rather than restart.
  5. Interactive create: offer the working mode in {communication_language} — Coaching path (default) or Fast path (see How you work) — before any drafting; default to Coaching unless the user asks for speed.
  6. Mandatory, both paths, before drafting: ask whether the spine is the only deliverable — and if not, draw out the purpose and audience rather than a document type. "An architecture doc" balloons into bloat; what they actually need might be a one-detail explainer for a single team or a non-technical vision piece for a board. Purpose right-sizes the artifact and may call for extra elicitation up front, not just a finale add-on.

For a new spine, bind {doc_workspace} to {workflow.spine_output_path}/{workflow.run_folder_pattern}/, seed ARCHITECTURE-SPINE.md from {workflow.spine_template}, run memlog.py init, and tell the user the path. At epic altitude, scope the folder to the epic (set run_folder_pattern per customize.toml) so per-epic runs don't collide.

Show full SKILL.md (610 more words)Show less

Reviewer Gate

The spine's pre-handoff review — full mechanics in references/reviewer-gate.md. Load it when finalizing or validating: a deterministic lint_spine.py pass, then a rubric walker (good-spine checklist) + every {workflow.finalize_reviewers} lens dispatched as parallel subagents against ARCHITECTURE-SPINE.md, scaled to stakes. At Finalize you apply the clear fixes; under the Validate intent you deliver a bespoke HTML report and then get user input.

Finalize

Walk the sequence; reviewer fixes land before polish.

  1. Distill. Write the spine from the memlog (brownfield: + the code sweep) — invariants first, seed minimal, every AD carrying Binds/Prevents/Rule, Deferred naming what it won't decide. No placeholders; never invent to fill a gap. The template's <!-- --> notes are guidance — act on them, then strip them; the finished spine carries no template comment, and only the diagrams that convey the structure (as many as the altitude needs, valid mermaid). Sweep the breadth the altitude owns — every structural dimension is decided, deferred, or an open question; a whole dimension left silent (e.g. the operational/environmental envelope: deployment & environments, infra/provider strategy, operations) is the failure, not a clean spine. A long coaching run distills cleaner in a subagent; the parent falls back inline.
  2. Reconcile inputs. A subagent per load-bearing input checks it against the spine and returns what didn't land — especially a quiet requirement (a tone, a constraint) the AD structure dropped. Before the gate.
  3. Reviewer pass. Run the Reviewer Gate (references/reviewer-gate.md). Resolve before polish.
  4. Triage. Open questions and [ASSUMPTION] tags: blockers (unsafe for what's next) resolved one at a time; the rest deferred with a revisit condition in the memlog.
  5. Renderings & polish. The spine is the build deliverable; with it and the memlog now in place, produce any additional human-facing artifact the user needs, scoped to the purpose and audience drawn out up front. The up-front question already flagged whether one's needed; if it wasn't, still offer one here, seeding concrete options: an interactive HTML+SVG deck to walk a team through the architecture and drive discussion, a fuller HTML/md solution design, a C4 set, or a view of how the work splits across teams/epics. Build only what they pick, right-sized to that purpose; apply {workflow.doc_standards} polish to that prose only, never to the spine.
  6. External handoffs. Run {workflow.external_handoffs}; surface returned URLs/IDs. Offer to invoke the bmad-spec skill to adopt the spine as a companion, keeping AD IDs stable so downstream can cite them.
  7. Close. Set the spine's own frontmatter status: final, updated: {date}; log a memlog.py append --type event --text "spine finalized" (the memlog has no status field). Share paths. Next, lead with bmad-spec — recommend adopting/refreshing the spine as a spec companion (always the top recommendation when a spec was an input, and a useful next step even when it wasn't), then bmad-create-epics-and-stories or — epic altitude — bmad-create-story; or invoke bmad-help to route.
  8. Run {workflow.on_complete}.

Update

Amend an existing spine or provided artifact. Resume from its .memlog.md (the authority on what was decided), not the rendered spine. Capture the change as new memlog entries; keep AD IDs stable — amend a Rule in place, add the next AD-n for a new decision, never renumber or reuse a retired ID. Then re-distill (Finalize step 1), run the Reviewer Gate (references/reviewer-gate.md), and close as in Finalize. An update that overrides something from a source input: offer to update that source too, so upstream and the spine don't silently diverge.

Validate

The standalone intent — critique an existing spine without changing it. Run the Reviewer Gate (references/reviewer-gate.md) against it and deliver the bespoke HTML report, then offer to roll the findings into an Update. (At Finalize the same gate runs as your own pre-handoff check, where you apply the fixes instead of reporting.)

© delorenj, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references, assets) in .agent/skills/bmad-architecture of delorenj/mcp-server-trello.

  • SKILL.md
  • assets/spine-template.md
  • customize.toml
  • references/headless.md
  • references/reviewer-gate.md
  • scripts/lint_spine.py
  • scripts/tests/test_lint_spine.py

Open the folder on GitHubat commit 737292f

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in delorenj/mcp-server-trello, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Bmad Architecture next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Lean Formalizewanshuiyin/Auto-claude-code-research-in-sleep17k—~5.4kAutomated safety check: NotesMIT
Lean Canvasphuryn/pm-skills27k—~1.2kAutomated safety check: PassMIT
Lean BuildJuliusBrussee/caveman111k1 repos~273Automated safety check: PassApache-2.0
Lean Commentsgithub/awesome-copilot40k—~5.3kAutomated safety check: PassMIT

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Questions about Bmad Architecture

What does Bmad Architecture do?

Produce the architecture: a lean spine of invariants that keeps everything built from it consistent, projected into whatever format the work needs. Bmad Architecture is an agent skill from delorenj/mcp-server-trello. Produce the architecture: a lean spine of invariants that keeps everything built from it consistent, projected into whatever format the work needs.

When should I use Bmad Architecture?

Bmad Architecture fits situations like: the user says create the architecture; create technical architecture; architecture spine; create a solution design.

How do I install Bmad Architecture in Claude Code?

Run `npx skills add delorenj/mcp-server-trello --skill bmad-architecture -a claude-code`. Or copy the skill folder (.agent/skills/bmad-architecture in delorenj/mcp-server-trello) into .claude/skills/bmad-architecture in your project. Claude Code loads it when a task matches its description.

How do I install Bmad Architecture in Codex?

Run `npx skills add delorenj/mcp-server-trello --skill bmad-architecture -a codex`. Or copy the skill folder (.agent/skills/bmad-architecture in delorenj/mcp-server-trello) into .agents/skills/bmad-architecture in your project. Codex loads it when a task matches its description.

Can I use Bmad Architecture in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add delorenj/mcp-server-trello --skill bmad-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bmad-architecture, .gemini/skills/bmad-architecture, .github/skills/bmad-architecture and .opencode/skills/bmad-architecture in your project.

What does Bmad Architecture need to run?

Going by SKILL.md and its folder, Bmad Architecture needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Bmad Architecture access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bmad Architecture safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Bmad Architecture use?

Bmad Architecture is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bmad Architecture use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.5k tokens, read only when the agent opens those files.

What are the alternatives to Bmad Architecture?

Skills that share tags, products or a category with Bmad Architecture: Invariant Guard (sickn33/agentic-awesome-skills, 47k stars), Lean Formalize (wanshuiyin/Auto-claude-code-research-in-sleep, 17k stars), Lean Canvas (phuryn/pm-skills, 27k stars) and Lean Build (JuliusBrussee/caveman, 111k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bmad Architecture?

delorenj (a GitHub user) maintains it in delorenj/mcp-server-trello, which has 445 GitHub stars. The repository holds 64 skills in this directory. The repository was last updated on September 23, 2026.

Source: delorenj/mcp-server-trello on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.