Agent skill

Bmad Spec

by delorenj in delorenj/mcp-server-trello

Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work.

MITAuto-check passed

Install Bmad Spec

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

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

GitHub CLI
$ gh skill install delorenj/mcp-server-trello bmad-spec --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-spec .claude/skills/bmad-spec && 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-spec
GitHub stars
445
Used in
1 other repo
Token cost
~4.3k tokens
SKILL.md length
2,278 words
Files
5 (incl. assets)
Skills in repo
64
Repo updated
First seen
Licence
MIT

At a glance

Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work.

  • Works in 4 steps: Resolve customization: uv run… → Run {workflow.activation_steps_prepend}.… → Resolve config: uv run… → …
  • The user says create a spec
  • SKILL.md covers Overview, Conventions, On Activation and Workspace, plus 11 more sections
  • Calls uv

What it does

Bmad Spec is an agent skill from delorenj/mcp-server-trello. Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work. Use when the user says "create a spec", "distill this into a spec", "validate this spec", "update the spec", or "break this into stories".

Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including assets (for example `assets/headless-schemas.md`, `assets/spec-template.md` and `assets/stories-schema.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 a spec
  • Distill this into a spec
  • Validate this spec
  • Update the spec

Example prompts

  • “create a spec”
  • “distill this into a spec”
  • “validate this spec”
  • “/bmad-spec”

Workflow steps

4 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. Run {workflow.activation_steps_prepend}. Treat {workflow.persistent_facts} as foundational context (file: entries are loaded).
  3. Resolve config: uv run {project-root}/_bmad/scripts/resolve_config.py --project-root {project-root} (merges _bmad/config.toml…
  4. Detect mode. Headless when any of: no TTY, programmatic caller (another skill or non-interactive runner), or the first message…

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

    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 Spec loads about 4.3k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 2,278 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from delorenj/mcp-server-trello at commit 737292f, republished under its MIT licence (© delorenj). 2,278 words, ~4,279 tokens.

Download SKILL.mdSave it as .claude/skills/bmad-spec/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
bmad-spec
description
Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work. Use when the user says "create a spec", "distill this into a spec", "validate this spec", "update the spec", or "break this into stories".

BMad Spec

Overview

Canonical transformer for the BMad spec-kernel ecosystem. Takes any intent input — vague idea, brain dump, PRD, GDD, RFC, brief, Slack thread, customer email, meeting transcript, mockups, mixed multi-source — and produces SPEC.md carrying the five-field kernel (Why, Capabilities, Constraints, Non-goals, Success signal) plus companion files for load-bearing content that does not fit or would bloat the kernel with expansive line-item detail. Together they are the machine contract every downstream BMad skill consumes.

Multiple skills may call to update the same spec over time.

Conventions

  • Bare paths (e.g. assets/spec-template.md) resolve from the skill root.
  • {skill-root} is this skill's install dir; {project-root} is the working dir.
  • {workflow.<name>} resolves to fields in customize.toml.

On Activation

  1. Resolve customization: uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow. On failure, read {skill-root}/customize.toml directly.
  2. Run {workflow.activation_steps_prepend}. Treat {workflow.persistent_facts} as foundational context (file: entries are loaded).
  3. 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}, {output_folder} (under core), and {date}.
  4. Detect mode. Headless when any of: no TTY, programmatic caller (another skill or non-interactive runner), or the first message pre-supplies all inputs and asks for an artifact path back. Interactive otherwise. In interactive mode, greet by {user_name} in {communication_language}, stay in that language, and mention that bmad-party-mode and bmad-advanced-elicitation are available for deeper exploration on any field.

Run {workflow.activation_steps_append}.

Activation is complete. If activation_steps_prepend or activation_steps_append were non-empty, confirm every entry was executed in order before proceeding. Do not begin the main workflow until all activation steps have been completed.

Workspace

The spec is always a folder named {workflow.spec_output_path}/{workflow.run_folder_pattern}, resolving by default to {output_folder}/specs/spec-{slug}/.

{slug} describes the thing being specced, not the input shape:

  • Source artifact already carries a slug (e.g., prd-foo-bar-2026-05-23/): inherit (foo-bar).
  • Sparse, in-chat, or multi-source input: interactive asks; headless caller provides it as part of the input. If absent and underivable, headless blocks with error_code: "missing_slug".
  • Same slug = same folder. A second invocation with the same {slug} lands at the existing spec folder and updates in place, preserving capability IDs.

No input. Interactive: ask the user to share a file path, paste content, explain the idea in detail, or point to a source. Headless: respond with JSON containing error_code: "insufficient_intent".

Inside the spec folder:

<spec-folder>/
  SPEC.md                  ← uppercase, the kernel — DERIVED from .memlog.md, never hand-edited
  <companion-1>.md         ← optional, content-typed (e.g. glossary.md); spec-authored ones are derived too
  <companion-2>.md
  stories.yaml             ← optional, written only by Story Breakdown — fixed name, never in companions:
  .memlog.md               ← canonical, append-only memory; what SPEC.md is distilled from

Memory and derivation

.memlog.md is canonical — an append-only, chronological record of every decision, constraint, capability (with its stable CAP-N), assumption, open question, and bit of user direction, one line each in the order it happened, never edited or reordered. SPEC.md and every spec-authored companion are derived on each run from the memlog (the decision-of-record) plus the sources it cites for raw content — never hand-patched.

Deriving the contract from a living log instead of editing the contract in place is what lets the steps around the spec (PRD, UX, architecture, epics) run in any order and feed the same spec without merge drift: the log only accumulates, the artifact is re-rendered. So the spec is updated only by re-deriving it here — bmad-spec is its single writer; a hand-edit to SPEC.md from outside is unsupported and is overwritten on the next derive.

Writes go through the shared script — {project-root}/_bmad/scripts/memlog.py, the same location as resolve_customization.py (atomic; never read it back except to resume):

  • uv run {project-root}/_bmad/scripts/memlog.py init --workspace {spec-folder} --field topic="<what is being specced>" — once, at create.
  • uv run {project-root}/_bmad/scripts/memlog.py append --workspace {spec-folder} --type <decision|constraint|capability|assumption|question|direction|note|event> --text "<one-line gist, reason included>" — as each lands.
  • Terminal moments (a validation verdict, "spec finalized") are --type event entries; the memlog carries no status field.

The Operation

Read the input and its ancillary linked materials. If there is no input, follow the no-input branch in Workspace (ask or block). If a prior .memlog.md exists at the target folder, read it — the operation becomes an update, and the memlog (not the rendered SPEC.md) is the authority on what was decided and on capability IDs. Preserve those IDs; new capabilities get the next unused CAP-N; never reuse retired IDs. Otherwise this is a create, and the first move is memlog.py init.

When the input is structured and pre-sorted (a PRD with an addendum, a GDD, a brief produced by an upstream BMad skill), trust the authored separation: lift kernel-fitting content into SPEC.md, lift overflow into appropriately-named companions. When the input is mixed (a brain dump, a transcript, an RFC, a customer email), do the sorting yourself: walk each claim, apply the three-lens load-bearing test (Spec Law rule 7), and route to the kernel field or a companion.

Distill the input into the five-field kernel using {workflow.spec_template} as the skeleton. When input is rich, extract directly — no elicitation. When input is sparse, choose: express (best-effort distill, every gap becomes an open_questions[] entry) or guided (walk the five fields with the user one at a time). Headless defaults to express and logs the choice. Interactive asks.

A recognized domain implication the input leaves unaddressed is such a gap — name it as an open_questions[] entry (healthcare input silent on PHI/HIPAA, payments silent on PCI, control systems silent on fail-safe) and move on. Flag it; never invent the answer or coach toward it. If these dominate, the input is too thin — suggest bmad-prd.

Write lean from the first pass: every sentence must earn its place. Decoration costs tokens and dilutes downstream readers.

Log each decision, capability, constraint, and accepted change to .memlog.md as it is made — that running record is what the render reads. Because the log is append-only, a later entry supersedes an earlier one on the same point while the history stays intact. When two currently-live sources or companions disagree on the same field, or an either/or never got resolved, surface it to the user rather than silently choosing — the resolution is itself a new memlog entry.

If the input is genuinely too thin to distill (e.g. "an app for hikers" with no surrounding context), stop and suggest bmad-prd (or sibling ceremony skill). This skill distills; it does not coach.

Load-bearing

A claim is load-bearing if any consumer (downstream skill, implementing agent, verification pass) would change a decision without it.

Companions

When load-bearing content does not fit the five-field kernel, it lives in a companion. The kernel cites it; the companion holds it. Companions are part of the contract; every consumer reads companions: in SPEC.md frontmatter to discover them. Companions follow the same lean discipline as SPEC.md (Spec Law rule 8).

Spawn a companion when the content needs more than one kernel-shape line: multi-item catalogs (per-entity matrices like archetypes, drinks, modes, routes), tables, diagrams (always), editorial voice rules, long-form reference material the kernel cites by name (glossary, brownfield notes, project conventions). Single-line decision-benders stay in Constraints; intent+success pairs stay in Capabilities. If a kernel field is starting to bullet into sub-bullets, the content has outgrown the kernel and wants a companion.

Companions are either:

  • Spec-authored companions are written by bmad-spec and live as siblings of SPEC.md (e.g., glossary.md, patron-archetypes.md). bmad-spec owns them and may edit them on update operations.
  • Adopted companions are load-bearing artifacts written by an upstream skill that downstream still needs to read. bmad-spec references them into companions: by relative path but does NOT edit them (e.g., a DESIGN.md or EXPERIENCE.md from a UX run, an integration partner's API spec). The originating skill owns them.

Two rules govern companions:

  1. Name spec-authored companions for the content type they hold. glossary.md, <entity-class>.md (e.g. patron-archetypes.md, medication-routes.md, flight-modes.md), stack.md, conventions.md, brownfield.md, architecture-diagrams.md, state-machines.md, failure-modes.md, compliance-references.md. The principle: "a reader should know what is inside before opening it." Adopted companions keep whatever name their originating skill gave them.
  2. Diagrams always land in a companion, regardless of size. SPEC.md kernel holds prose only. Mermaid blocks, ASCII diagrams, and image references all live in a companion (e.g. architecture-diagrams.md), with sibling image files referenced from there.

Pre-existing project-wide docs (e.g. project-context.md) that downstream needs are listed as adopted companions, never duplicated into SPEC.md or a spec-authored companion.

stories.yaml, when produced, is spec-authored but deliberately not a companion — see Story Breakdown below.

Show full SKILL.md (955 more words)Show less

Spec Law

Every spec must satisfy these eight rules. The operation aims for them; the self-validate sweep enforces them.

  1. Each capability has both intent and success. Missing either = not a capability.
  2. Intents describe WHAT, not HOW. Implementation prescription belongs in a companion (stack, conventions).
  3. Constraints actually bend design decisions. A "constraint" that rules nothing out is decoration.
  4. Non-goals are explicit. At least one. Absence means downstream skills fill the vacuum.
  5. Success signal is concrete enough to test or demonstrate against. "Users love it" doesn't qualify.
  6. Capability IDs are stable and unique. Never reused, never renumbered.
  7. Preservation. Every load-bearing source claim lands in SPEC.md or a companion. Wrapper ceremony does not.
  8. Lean prose. Every sentence carries load-bearing content. Cut decoration, hedges, backstory, throat-clearing. Applies to SPEC.md, companions, and .memlog.md.

Self-Validate

After every create or update, sweep the resulting artifact in two passes before presenting.

Pass 1 — Coherence. Judge the spec against Spec Law rules 1–6 and 8. For anything that fails or feels weak, attempt to fix it without inventing content the input did not support. Calls made without direct confirmation become assumptions[]; gaps that could not be filled become open_questions[].

Pass 2 — Preservation. Walk the source claim by claim. Confirm each load-bearing claim landed in SPEC.md or a companion. Wrapper-ceremony drops are logged under "Wrapper-only content" so the drop is on the record, not silent.

Record the verdict for each pass to .memlog.md (append --type event). In interactive mode, review it with the user. In headless mode, .memlog.md is one of the files returned, so the caller (or its downstream LLM) reads the verdict there.

Spec with no change signal

When the user points the skill at an existing spec folder (or its SPEC.md) with no change signal, offer to review assumptions or open questions, or determine what they want to do.

Story Breakdown (optional, interactive-only)

Requires SPEC.md on disk — run the normal Operation first if it doesn't exist yet. Headless runs never do this, even when the invocation text asks for it: if mode detection (On Activation, step 4) resolved headless, skip this section entirely and proceed with the normal headless response. In interactive mode, offer it at most once per run when the input reads as multiple independently shippable slices; a decline ends the offer for this run, not forever. Also run it on direct request ("break this into stories") whenever SPEC.md exists. When a spec update runs and stories.yaml exists, check the story descriptions against the updated spec; if any no longer matches, say so and offer to re-run Story Breakdown. The update itself never rewrites stories.yaml.

Either way, walk the capabilities and constraints with the user and propose a story per independently reviewable slice — this is a conversation, not a silent render. For each story, ask the user for spec_checkpoint, done_checkpoint, and any invoke_dev_with note rather than defaulting them silently; capturing that human judgment is what the fields are for. If the conversation surfaces load-bearing detail beyond dispatch notes (a constraint, a design decision), route it into SPEC.md or a companion — invoke_dev_with carries dispatch notes only (Spec Law rule 7 still applies).

The output is stories.yaml, a sibling of SPEC.md inside the spec folder, discovered by that fixed name — same convention as SPEC.md and .memlog.md. Never list it in companions: and never point a frontmatter key at it: companions carry the what-to-build contract every consumer reads; stories.yaml is input for whichever tool dispatches the stories.

Field definitions, the validity rules, and a worked example live in assets/stories-schema.md. Before writing or re-writing the file, check every entry against those rules; fix violations rather than presenting a file that fails them. Record the check's verdict to .memlog.md (append --type event), the same discipline as Self-Validate.

Derive stories.yaml from .memlog.md exactly like any other spec-authored artifact: log each proposed story (--type decision) as the user agrees to it, then render. On a later run against the same spec folder, re-derive the same way, handling ids per the schema's update semantics.

Output

Interactive — share the spec folder path conversationally. Name the capability count, the companions produced, and the verdict in one or two sentences. Name the story count too if stories.yaml was written this run. If assumptions[] or open_questions[] are non-empty, list them (short — one line each) and invite the user to walk through them. Make clear that addressing them can update the source input (if it was a file), the spec, or both — whichever combination the user prefers. Do not dump JSON or present a wall of output.

Headless — return JSON per assets/headless-schemas.md.

Run {workflow.on_complete} if set.

After Spec is Output

Any update to the spec — resolved assumptions, answered open questions, other changes — is appended to .memlog.md as it happens. When a change overrides something that came from a source input, offer to update that source too, so upstream and the spec don't silently diverge.

Frontmatter conventions

  • companions: array of .md files downstream MUST read alongside SPEC.md to have the full contract. Paths may point inside the spec folder (spec-authored companions like glossary.md) or outside it (adopted companions like ../planning-artifacts/ux-designs/ux-foo-bar-2026-05-23/DESIGN.md). The split between spec-authored and adopted is implicit by path; downstream treats both the same.
  • sources: array of paths to files that were fully absorbed into the SPEC, with no remaining downstream value (e.g., a PRD whose every load-bearing claim is now in the kernel). Listed for audit and for bmad-spec to re-read on update. Downstream does NOT read these. Files that downstream still needs to read belong in companions:, not here.
  • Do not list the memlog, README files, organizational artifacts, or any operational record of how upstream skills produced their artifacts. Those are not source content; they are process metadata that downstream consumers don't need.

© 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 4 other files (assets) in .agent/skills/bmad-spec of delorenj/mcp-server-trello.

  • SKILL.md
  • assets/headless-schemas.md
  • assets/spec-template.md
  • assets/stories-schema.md
  • customize.toml

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 Spec 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.

Bmad Spec compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bmad Spec this skilldelorenj/mcp-server-trello4451 repos~4.3kAutomated safety check: PassMIT
Intent Requirements IntakeYeachan-Heo/oh-my-claudecode40k—~1.5kAutomated safety check: PassMIT
Kernel Organizationsgl-project/sglang37k—~1.3kAutomated safety check: PassApache-2.0
Metal Kernelpytorch/pytorch104k—~4.9kAutomated safety check: PassCustom licence
Paperclip Distillpaperclipai/paperclip99k—~2.8kAutomated safety check: PassMIT
Search Inputthedaviddias/Front-End-Checklist74k—~402Automated safety check: PassMIT

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Questions about Bmad Spec

What does Bmad Spec do?

Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work. Bmad Spec is an agent skill from delorenj/mcp-server-trello. Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work.

When should I use Bmad Spec?

Bmad Spec fits situations like: the user says create a spec; distill this into a spec; validate this spec; update the spec.

How do I install Bmad Spec in Claude Code?

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

How do I install Bmad Spec in Codex?

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

Can I use Bmad Spec 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-spec -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-spec, .gemini/skills/bmad-spec, .github/skills/bmad-spec and .opencode/skills/bmad-spec in your project.

What does Bmad Spec need to run?

Going by SKILL.md and its folder, Bmad Spec needs the command-line tools its instructions call (uv).

Does Bmad Spec 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 Spec 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. Review the folder before installing.

What licence does Bmad Spec use?

Bmad Spec 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 Spec use?

About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bmad Spec?

Skills that share tags, products or a category with Bmad Spec: Intent Requirements Intake (Yeachan-Heo/oh-my-claudecode, 40k stars), Kernel Organization (sgl-project/sglang, 37k stars), Metal Kernel (pytorch/pytorch, 104k stars) and Paperclip Distill (paperclipai/paperclip, 99k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bmad Spec?

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.