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-retrospectivegit 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-retrospective)<a href="https://agentmods.dev/skills/huangjia2019/sdd-in-action/bmad-retrospective"><img src="https://agentmods.dev/badge/skills/huangjia2019/sdd-in-action/bmad-retrospective/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-retrospective"><img src="https://agentmods.dev/badge/skills/huangjia2019/sdd-in-action/bmad-retrospective.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 | $0.00038 | $0.14499 |
| Opus 5 | $0.00019 | $0.07250 |
| Sonnet 5 | $0.00008 | $0.02900 |
| Haiku 4.5 | $0.00004 | $0.01450 |
Grade A, and why
bmad-retrospective 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 9d 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
100% identical to bmad-retrospective — 0 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 — 1,513 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retrospective Workflow
Goal: Post-epic review to extract lessons and assess success.
Your Role: Developer facilitating retrospective.
- No time estimates — NEVER mention hours, days, weeks, months, or ANY time-based predictions. AI has fundamentally changed development speed.
- Communicate all responses in {communication_language} and language MUST be tailored to {user_skill_level}
- Generate all documents in {document_output_language}
- Document output: Retrospective analysis. Concise insights, lessons learned, action items. User skill level ({user_skill_level}) affects conversation style ONLY, not retrospective content.
- Facilitation notes:
- Psychological safety is paramount - NO BLAME
- Focus on systems, processes, and learning
- Everyone contributes with specific examples preferred
- Action items must be achievable with clear ownership
- Two-part format: (1) Epic Review + (2) Next Epic Preparation
- Party mode protocol:
- ALL agent dialogue MUST use format: "Name (Role): dialogue"
- Example: Amelia (Developer): "Let's begin..."
- Example: {user_name} (Project Lead): [User responds]
- Create natural back-and-forth with user actively participating
- Show disagreements, diverse perspectives, authentic team dynamics
Conventions
- Bare paths 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 Workflow Block
Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow
If the script fails, resolve the workflow 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
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.
- 9d ago First seen · 1,513 lines · 38 tokens per session scan A 997308d99954
bmad-retrospective is a skill published in the GitHub repository huangjia2019/sdd-in-action (147 stars, last pushed 18d ago), licensed MIT. It adds 38 tokens to every session and 14,499 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bmad-retrospective, differing in 0 lines, and is treated as a copy.
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