Borrowing it
Nothing to install: this file belongs to 2233admin/design-pipeline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/2233admin/design-pipeline/main/.agents/skills/bmad-review/SKILL.mdgit clone --depth 1 https://github.com/2233admin/design-pipelineWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/2233admin/design-pipeline/bmad-review)<a href="https://agentmods.dev/skills/2233admin/design-pipeline/bmad-review"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/bmad-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/2233admin/design-pipeline/bmad-review"><img src="https://agentmods.dev/badge/skills/2233admin/design-pipeline/bmad-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00083 | $0.01661 |
| Opus 5 | $0.00042 | $0.00830 |
| Sonnet 5 | $0.00017 | $0.00332 |
| Haiku 4.5 | $0.00008 | $0.00166 |
Grade A, and why
bmad-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- bmad-review — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BMad Review
Review content through lenses — each a distinct method and stance — and report findings in one canonical shape. Report what is real — never pad to look thorough. Each lens sets its own stance toward the content and toward zero findings: for most an empty result is valid; the adversarial lens requires at least ten concrete findings and treats an empty list as a signal to re-check; the editorial lenses hold content sacrosanct and critique only how it is organized and expressed.
The lens set is whatever {workflow.lenses} resolves to, not a fixed list — overrides add lenses and replace shipped ones. Never claim a capability from this file; read the resolved lenses and work from those.
Inputs
- content — what to review: a diff, branch, uncommitted changes, file, spec, story, or any document. Args:
[path]. - lenses (optional) — one or more lens codes or names, however the caller expresses them: a spoken request, or a directive of the form
skill:bmad-review lenses=<code>[,<code>...](the form bmm'sdoc_standardsuses). Default: every applicable lens (a full review). - also_consider (optional) — areas to keep in mind alongside each lens's normal analysis.
- pre-resolved customization (optional) —
[workflow]field values supplied by a forwarding caller. See Execution step 1.
Conventions
- Bare paths (e.g.
references/lens-edge-case-hunter.md) resolve from{skill-root}— this skill's installed directory, wherecustomize.tomllives.{project-root}resolves to the project working directory. {workflow.<name>}resolves to fields incustomize.toml's[workflow]table (overrides win per BMad merge rules).- In
style_guide,review_guidance, andpersistent_facts, a value prefixedfile:is a path or glob — load that file's contents. If afile:value cannot be read, name the failed file in the output header and continue: the shipped baseline forstyle_guide, the remaining entries otherwise.
Execution
- Resolve customization:
uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow. On failure, read{skill-root}/customize.tomldirectly and use defaults. Forwarded activation: if a caller invoked you with pre-resolved customization fields (e.g. thebmad-editorial-reviewshim), honor them verbatim for those named fields — they already carry the user's overrides — and resolve only the remaining fields from your owncustomize.toml. Then execute each{workflow.activation_steps_prepend}entry in order, hold{workflow.persistent_facts}as standing context for the session, and treat{workflow.review_guidance}entries as standing review directives for every lens. - Load the content. If it is empty or cannot be decoded as text: when the caller expects the raw findings JSON array (e.g. the legacy edge-case forwarder), return
[{"location":"N/A","trigger_condition":"Input empty or undecodable","guard_snippet":"Provide valid content to review","potential_consequence":"Review skipped — no analysis performed"}](nolensfield) and stop; otherwise say what's wrong and ask for reviewable content. Classify the content — diff, source file, function, or document — and whether it is code or docs; scope rules and lens applicability both depend on it. A document that defines behavior (spec, requirements, plan, story) isdocsthat a behavioral lens may still apply to; judge bywhen. - Select lenses from
{workflow.lenses}. A lens with an emptyinstructionis disabled. If the user or caller named lenses, run exactly those only —applies_toandwhendo not filter an explicit request. Otherwise run every enabled lens whoseapplies_tocovers the content class (anyalways covers) and whosewhenapplies. - Announce the plan in one line before running anything: the content class, the lenses about to run, and — when any lens has
afterset — that it runs on top of the named lens's findings. Skip the announcement entirely when the caller pinned an exact output contract (the legacy forwarders that demand raw JSON or one exact line) — their contract covers everything you emit, not just the findings block. Then execute each{workflow.activation_steps_append}entry in order. - Run the independent lenses — every selected lens without
after. Each sees the content andalso_consider, never another lens's findings. Follow each lens'sinstruction; the shipped lenses load their reference file just-in-time, so load only what runs. When subagents are available, spawn one per lens in parallel: give it the lensinstructionwith{skill-root}and paths resolved absolute, the content or where to read it, anyalso_considerareas, the standing review directives, and the constraint "Return ONLY your findings — no other output." Otherwise run the lenses sequentially yourself, completing one before starting the next. - Run the dependent lenses — every selected lens with
after, once the lens it names has completed, passing that lens's findings in. A lens whoseaftertarget was not selected or produced nothing still runs, with no prior findings. Dependent lenses that name different targets are independent of each other and may run in parallel. - Assemble and present per Output below. Keep every lens's findings — overlap between lenses is signal, not duplication; note it in the markdown report rather than deduping. Execute
{workflow.on_complete}if set.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- customize.toml 5.8 KB
- references/editorial-common.md 5.7 KB
- references/lens-adversarial.md 779 B
- references/lens-edge-case-hunter.md 3.8 KB
- references/lens-prose.md 1.3 KB
- references/lens-structure.md 1.5 KB
- references/lens-verification-gap.md 9.3 KB
- references/structure-models.md 1.9 KB
- scripts/tests/test_word_metrics.py 1.5 KB runs code
- scripts/word_metrics.py 3.4 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 50 lines · 83 tokens per session scan A 144e6c46d915
bmad-review is a skill published in the GitHub repository 2233admin/design-pipeline (9 stars, last pushed 7d ago), licensed MIT. It adds 83 tokens to every session and 1,661 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
ss-learn
Capture a human-approved UI design lesson as a privacy-minimized local StyleSeed candidate, review it, and prepare an opt-in share package without transmitting project code, prompts, screenshots, or brand data. Use when a person asks StyleSeed to remember, learn from, generalize, review, or prepare a reusable rule…
ss-lint
Quick automated lint — detects common design system violations in seconds.
ss-review
Review UI code for design system compliance, accessibility, and best practices.
component-api-validator
Audit component APIs for consistency, breaking changes, TypeScript coverage, and contract compliance across a component library. Trigger when someone says: component prop review, verify component types are exported, component API audit, check our component interfaces, are our props consistent, API consistency check…
pr-review
Review one or all open pull requests in saadeghi/daisyui with read-only GitHub data and isolated base-versus-PR checks. Use when Codex must verify a PR, find concrete regressions, decide whether it is safe to merge, and write a concise report under tmp/pr/.
designlang-tokens
Use when styling UI for cal.com — references the extracted design system tokens instead of inventing colors, spacing, or typography.