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.
npx skills add mmornati/leanproxy-mcp --skill bmad-loopgit clone --depth 1 https://github.com/mmornati/leanproxy-mcpWrote 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/mmornati/leanproxy-mcp/bmad-loop)<a href="https://agentmods.dev/skills/mmornati/leanproxy-mcp/bmad-loop"><img src="https://agentmods.dev/badge/skills/mmornati/leanproxy-mcp/bmad-loop/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/mmornati/leanproxy-mcp/bmad-loop"><img src="https://agentmods.dev/badge/skills/mmornati/leanproxy-mcp/bmad-loop.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 | $0.00048 | $0.01240 |
| Opus 5.5 | $0.00019 | $0.00496 |
| Sonnet 5.5 | $0.00010 | $0.00248 |
| Haiku 4.5 | $0.00005 | $0.00124 |
Grade A, and why
bmad-loop 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 1mo 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.
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.
Loop Workflow
Goal: Deliver each story in an input list through the full pipeline: implement, review, branch, PR, CI-check, merge — then move to the next.
Your Role: Automated delivery orchestrator. You coordinate subagents, manage git, and drive PRs to merge. No human interaction inside the loop body.
HALT
To HALT with a final status and optional blocking condition:
- If applicable, append a loop-status entry to
{loop_status_file}. - Run:
python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow.on_complete - If the resolved
workflow.on_completeis non-empty, follow it as the final instruction before exiting. - 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. 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.
Conventions
- Bare paths (e.g.
steps/step-01-ingest-input.md) resolve from the skill root. {skill-root}resolves to this skill's installed directory (wherecustomize.tomllives).{project-root}-prefixed paths resolve from the project working directory.{skill-name}resolves to the skill directory's basename.
Input Resolution
Parse the invocation prompt for space-separated story keys (e.g. "4-1 4-2 4-3"). Store as {story_keys} array, preserving order. If the prompt contains no recognizable story keys, HALT with status blocked and blocking condition no story keys provided.
Supported patterns per element:
N-N— story key like4-1epic-N— epic key likeepic-4(expanded later by step-01)
What ships with it
3 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.
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.
- 1mo ago First seen · 103 lines · 48 tokens per session scan A f527fb2fd9de
bmad-loop is a skill published in the GitHub repository mmornati/leanproxy-mcp (5 stars, last pushed 25d ago), licensed MIT. It adds 48 tokens to every session and 1,240 once invoked, about $0.0002 per session on Opus 5.5. 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.
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