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 zsutxz/ClaudeLearning --skill bmad-quick-devgit clone --depth 1 https://github.com/zsutxz/ClaudeLearningWrote 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/zsutxz/claudelearning/bmad-quick-dev)<a href="https://agentmods.dev/skills/zsutxz/claudelearning/bmad-quick-dev"><img src="https://agentmods.dev/badge/skills/zsutxz/claudelearning/bmad-quick-dev/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/zsutxz/claudelearning/bmad-quick-dev"><img src="https://agentmods.dev/badge/skills/zsutxz/claudelearning/bmad-quick-dev.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 | $0.00063 | $0.01310 |
| Opus 5 | $0.00032 | $0.00655 |
| Sonnet 5 | $0.00013 | $0.00262 |
| Haiku 4.5 | $0.00006 | $0.00131 |
Grade A, and why
bmad-quick-dev 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 7d 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.
This is a copy
73% identical to bmad-code-review — 47 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quick Dev New Preview Workflow
Goal: Turn user intent into a hardened, reviewable artifact.
CRITICAL: If a step says "read fully and follow step-XX", you read and follow step-XX. No exceptions.
Subagents, when the capability is available, are an important part of this workflow. Use them as directed by the workflow steps. If you need an explicit user instruction to run them, ask once now for the whole workflow run.
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.
SCOPE STANDARD
A specification should target a single user-facing goal within 900–1600 tokens:
- Single goal: One cohesive feature, even if it spans multiple layers/files. Multi-goal means >=2 top-level independent shippable deliverables — each could be reviewed, tested, and merged as a separate PR without breaking the others. Never count surface verbs, "and" conjunctions, or noun phrases. Never split cross-layer implementation details inside one user goal.
- Split: "add dark mode toggle AND refactor auth to JWT AND build admin dashboard"
- Don't split: "add validation and display errors" / "support drag-and-drop AND paste AND retry"
- 900–1600 tokens: Optimal range for LLM consumption. Below 900 risks ambiguity; above 1600 risks context-rot in implementation agents.
- Neither limit is a gate. Both are proposals with user override.
Conventions
- Bare paths (e.g.
step-01-clarify-and-route.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.
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.
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.
- 7d ago First seen · 116 lines · 63 tokens per session scan A 18c2002a48de
bmad-quick-dev is a skill published in the GitHub repository zsutxz/ClaudeLearning (5 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 1,310 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 73% identical to bmad-code-review, differing in 47 lines, and is treated as a copy.
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