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BC bmad-method

Use BMad (Breakthrough Method of Agile AI Driven Development) framework for AI-driven development. Use for: architecture analysis, sprint planning, story generation, PRD creation, and full development workflows. Requires coding-agent skill with Claude Code.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 30 files body ≈ 5 381 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use BMad (Breakthrough Method of Agile AI Driven Development) framework for AI-driven development.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

ProcedureAI and agentsSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
99
Quality 40%
68
Run on models
none yet
Process rating
C
59/100
Has gaps
Inputs and preconditions w 11
0
Result and completion w 14
40
Consistency w 8
40
the three weakest of ten parameters · all ten

How to improve

  1. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security docs/explanation/advanced-elicitation.md:39
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
    - **Red Team vs Blue Team** - Attack your own work, then defend it

Files scanned: 30. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5381 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 59/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 40Result and completion. Does not say what the result is
  • 40Consistency. Frontmatter name (bmad-method) differs from the folder (lb-bmad-skill)
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, read, web, git) that frontmatter does not declare
  • 70Execution cost. Instruction body is 5381 tokens
  • 85Steps. 97 steps, 3 vague phrases
  • 100Failures and branches. 2 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 22 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -219 emoji in the instructions: noise for the model
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +3Description length 257: enough signal without eating the budget
  • +4Structure: 53 headings
  • +3Step-by-step instructions: 97 items
  • +4Has examples (35 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.