bmad-dev-auto

bmad-dev-auto is a skill for Claude Code, Codex from mmornati/leanproxy-mcp. It costs 19 tokens per session (1,120 once invoked), scanned A, original, MIT.

One unattended pass through a BMad development workflow, where BMad is a set of coding-agent instructions and tools. It turns an intention into an implementation result and records whether the run completed or was blocked.

In plain words
What is it for?
Use it for a single automated development iteration. It can process a known specification or create a result file describing the outcome.
Why use it?
It lets the workflow continue without waiting for human input at each step. It also requires a clear final status when the run must stop.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/mmornati/leanproxy-mcp/bmad-dev-auto
Any agent
npx skills add mmornati/leanproxy-mcp --skill bmad-dev-auto
Clone the repo
git clone --depth 1 https://github.com/mmornati/leanproxy-mcp

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bmad-dev-auto

README.md
[![agentmods](https://agentmods.dev/badge/skills/mmornati/leanproxy-mcp/bmad-dev-auto.svg)](https://agentmods.dev/skills/mmornati/leanproxy-mcp/bmad-dev-auto)
Your own site
<a href="https://agentmods.dev/skills/mmornati/leanproxy-mcp/bmad-dev-auto"><img src="https://agentmods.dev/badge/skills/mmornati/leanproxy-mcp/bmad-dev-auto.svg" alt="Measured on agentmods" height="20"></a>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,120 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00019 $0.01120
Opus 5 $0.00010 $0.00560
Sonnet 5 $0.00004 $0.00224
Haiku 4.5 $0.00002 $0.00112

Measured 4d ago against content hash f4243eae074a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

bmad-dev-auto 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 4d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.agents/skills/bmad-dev-auto/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Dev Auto Workflow

Goal: Turn intent into a hardened, reviewable artifact, without human interaction.

CRITICAL: If a step says "read fully and follow step-XX", you read and follow step-XX. No exceptions.

HALT

To HALT with a final status and optional blocking condition:

  1. If {spec_file} is known and exists, update status in frontmatter and append missing result details under ## Auto Run Result.
  2. If {spec_file} is unknown or missing, create {implementation_artifacts}/bmad-dev-auto-result-<slug-or-timestamp>.md with:
    ---
    status: <final status>
    ---
    
    # BMad Dev Auto Result
    
    Status: <final status>
    Blocking condition: <blocking condition, if any>
    
  3. Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow.on_complete
  4. If the resolved workflow.on_complete is non-empty, follow it as the final instruction before exiting.
  5. Stop the workflow.

Subagents

Using subagents when instructed is mandatory. If you cannot, HALT with status blocked and blocking condition no subagents.

Invoke every subagent synchronously: launch it, wait for it to return within the same turn, then continue with its result. When a step says to run subagents "in parallel" (e.g. the reviewers), that means several blocking calls awaited together in one turn — not detached execution. Never run a subagent in the background / detached / async (e.g. run_in_background: true), and never end your turn to "await a completion notification." This workflow runs unattended: there is no event loop to resume a yielded turn, so a backgrounded subagent never hands control back and the run stalls. The only sanctioned way to end a turn is the HALT protocol above with an explicit terminal status.

READY FOR DEVELOPMENT STANDARD

A specification is "Ready for Development" when:

  • Actionable: Every task has a file path and specific action.
  • Logical: Tasks ordered by dependency.
  • Testable: All ACs use Given/When/Then.
  • Complete: No placeholders or TBDs.
  • Sufficient: No known requirement, acceptance, dependency, or implementation gaps remain unresolved.
  • Coherent: No unresolved ambiguities or internal contradictions.

Read the full file on GitHub · 103 lines

Files

What ships with it

7 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.

Changes

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.

  1. 4d ago First seen · 103 lines · 19 tokens per session scan A f4243eae074a

Subscribe to this mod's changes

bmad-dev-auto is a skill published in the GitHub repository mmornati/leanproxy-mcp (5 stars, last pushed 5d ago), licensed MIT. It adds 19 tokens to every session and 1,120 once invoked, about $0.0001 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.

Related

Other skills, from other repositories

go-swagger3-docs

Documents existing Go HTTP APIs with go-swagger3 godoc @ annotations and struct tags, then generates OpenAPI 3 specs. Use when the user asks to document an API, add Swagger/OpenAPI comments, generate oas.json/yml, or mentions go-swagger3.

parvez3019/go-swagger3 · 63 tokens

metrics-instrumentation

Specification for instrumenting an opik-backend workflow with operational OpenTelemetry metrics — per-stage throughput/latency/error counters and native histograms, dimensioned per-customer (workspace). Use when a pipeline (scoring, ingestion, experiments, jobs) needs per-stage visibility. Covers metric emission only…

comet-ml/opik · 93 tokens

notifications

Send notifications through the unified notification router.

vellum-ai/vellum-assistant · 9 tokens

telegram-setup

Connect a Telegram bot to the Vellum Assistant gateway with automated webhook registration and credential storage.

vellum-ai/vellum-assistant · 22 tokens

chat-complex-documents

Chat with and search your complex documents — ask questions, extract tables and fields, and get answers grounded in the source. Connects the hosted Unstructured Transform MCP server to parse, structure, and enrich PDFs, Word/Excel/PowerPoint, images, scanned files, emails, and 60+ other formats into clean, AI-ready…

vellum-ai/vellum-assistant · 90 tokens

plano-filter-guardrails

Harden Plano filter chains and guardrails. Use for MCP filter setup, prompt guard responses, and safe filter ordering.

katanemo/plano · 31 tokens