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 agentmods add skills/flanliulf/speclite/bmad-document-projectnpx skills add flanliulf/SpecLite --skill bmad-document-projectgit clone --depth 1 https://github.com/flanliulf/SpecLiteWrote 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/flanliulf/speclite/bmad-document-project)<a href="https://agentmods.dev/skills/flanliulf/speclite/bmad-document-project"><img src="https://agentmods.dev/badge/skills/flanliulf/speclite/bmad-document-project.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00555 |
| Opus 5 | $0.00015 | $0.00278 |
| Sonnet 5 | $0.00006 | $0.00111 |
| Haiku 4.5 | $0.00003 | $0.00056 |
Grade A, and why
bmad-document-project 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 5d 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
84% identical to bmad-checkpoint-preview — 34 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Document Project Workflow
Goal: Document brownfield projects for AI context.
Your Role: Project documentation specialist.
Conventions
- Bare paths (e.g.
instructions.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 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
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 {workflow.activation_steps_prepend} in order before proceeding.
Step 3: Load Persistent Facts
Treat every entry in {workflow.persistent_facts} as foundational context you carry for the rest of the workflow run. Entries prefixed file: are paths or globs under {project-root} — load the referenced contents as facts. All other entries are facts verbatim.
Step 4: Load Config
Load config from {project-root}/_bmad/bmm/config.yaml and resolve:
- Use
{user_name}for greeting - Use
{communication_language}for all communications - Use
{document_output_language}for output documents - Use
{planning_artifacts}for output location and artifact scanning - Use
{project_knowledge}for additional context scanning
Step 5: Greet the User
Greet {user_name} (if you have not already), speaking in {communication_language}.
What ships with it
13 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.
- checklist.md 9.9 KB
- customize.toml 1.6 KB
- documentation-requirements.csv 7.9 KB
- instructions.md 5.0 KB
- templates/deep-dive-template.md 5.7 KB
- templates/index-template.md 4.4 KB
- templates/project-overview-template.md 1.9 KB
- templates/project-scan-report-schema.json 4.7 KB
- templates/source-tree-template.md 2.1 KB
- workflows/deep-dive-instructions.md 12 KB
- workflows/deep-dive-workflow.md 979 B
- workflows/full-scan-instructions.md 42 KB
- workflows/full-scan-workflow.md 970 B
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.
- 5d ago First seen · 63 lines · 29 tokens per session scan A efe25c8c2711
bmad-document-project is a skill published in the GitHub repository flanliulf/SpecLite (4 stars, last pushed 2mo ago), licensed MIT. It adds 29 tokens to every session and 555 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to bmad-checkpoint-preview, differing in 34 lines, and is treated as a copy.
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