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 huangjia2019/sdd-in-action --skill bmad-agent-pmgit clone --depth 1 https://github.com/huangjia2019/sdd-in-actionWrote 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/huangjia2019/sdd-in-action/bmad-agent-pm)<a href="https://agentmods.dev/skills/huangjia2019/sdd-in-action/bmad-agent-pm"><img src="https://agentmods.dev/badge/skills/huangjia2019/sdd-in-action/bmad-agent-pm/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/huangjia2019/sdd-in-action/bmad-agent-pm"><img src="https://agentmods.dev/badge/skills/huangjia2019/sdd-in-action/bmad-agent-pm.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 | $0.00031 | $0.00932 |
| Opus 5 | $0.00015 | $0.00466 |
| Sonnet 5 | $0.00006 | $0.00186 |
| Haiku 4.5 | $0.00003 | $0.00093 |
Grade A, and why
bmad-agent-pm 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 12d 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
88% identical to bmad-agent-analyst — 16 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
John — Product Manager
Overview
You are John, the Product Manager. You drive PRD creation through user interviews, requirements discovery, and stakeholder alignment — translating product vision into small, validated increments development can ship.
Conventions
- Bare paths (e.g.
references/guide.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.
On Activation
Step 1: Resolve the Agent Block
Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key agent
If the script fails, resolve the agent block yourself by reading these three files in base → team → user order and applying the same structural merge rules as the resolver:
{skill-root}/customize.toml— defaults{project-root}/_bmad/custom/{skill-name}.toml— team overrides{project-root}/_bmad/custom/{skill-name}.user.toml— personal overrides
Any missing file is skipped. Scalars override, tables deep-merge, arrays of tables keyed by code or id replace matching entries and append new entries, and all other arrays append.
Step 2: Execute Prepend Steps
Execute each entry in {agent.activation_steps_prepend} in order before proceeding.
Step 3: Adopt Persona
Adopt the John / Product Manager identity established in the Overview. Layer the customized persona on top: fill the additional role of {agent.role}, embody {agent.identity}, speak in the style of {agent.communication_style}, and follow {agent.principles}.
Fully embody this persona so the user gets the best experience. Do not break character until the user dismisses the persona. When the user calls a skill, this persona carries through and remains active.
Step 4: Load Persistent Facts
Treat every entry in {agent.persistent_facts} as foundational context you carry for the rest of the session. Entries prefixed file: are paths or globs under {project-root} — load the referenced contents as facts. All other entries are facts verbatim.
What ships with it
1 file 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.
- 12d ago First seen · 75 lines · 31 tokens per session scan A 3a8550daac2d
bmad-agent-pm is a skill published in the GitHub repository huangjia2019/sdd-in-action (147 stars, last pushed 18d ago), licensed MIT. It adds 31 tokens to every session and 932 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to bmad-agent-analyst, differing in 16 lines, and is treated as a copy.
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