Borrowing it
Nothing to install: this file belongs to julianobarbosa/azure-finops-mcp-server. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/julianobarbosa/azure-finops-mcp-server/main/.claude/commands/BMad/tasks/document-project.mdgit clone --depth 1 https://github.com/julianobarbosa/azure-finops-mcp-serverWrote 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/commands/julianobarbosa/azure-finops-mcp-server/document-project)<a href="https://agentmods.dev/commands/julianobarbosa/azure-finops-mcp-server/document-project"><img src="https://agentmods.dev/badge/commands/julianobarbosa/azure-finops-mcp-server/document-project/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/commands/julianobarbosa/azure-finops-mcp-server/document-project"><img src="https://agentmods.dev/badge/commands/julianobarbosa/azure-finops-mcp-server/document-project.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 | $0.00000 | $0.02938 |
| Opus 5 | $0.00000 | $0.01469 |
| Sonnet 5 | $0.00000 | $0.00588 |
| Haiku 4.5 | $0.00000 | $0.00294 |
Grade A, and why
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 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
94% identical to document-project — 2 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 — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/document-project Task
When this command is used, execute the following task:
Document an Existing Project
Purpose
Generate comprehensive documentation for existing projects optimized for AI development agents. This task creates structured reference materials that enable AI agents to understand project context, conventions, and patterns for effective contribution to any codebase.
Task Instructions
1. Initial Project Analysis
CRITICAL: First, check if a PRD or requirements document exists in context. If yes, use it to focus your documentation efforts on relevant areas only.
IF PRD EXISTS:
- Review the PRD to understand what enhancement/feature is planned
- Identify which modules, services, or areas will be affected
- Focus documentation ONLY on these relevant areas
- Skip unrelated parts of the codebase to keep docs lean
IF NO PRD EXISTS: Ask the user:
"I notice you haven't provided a PRD or requirements document. To create more focused and useful documentation, I recommend one of these options:
-
Create a PRD first - Would you like me to help create a brownfield PRD before documenting? This helps focus documentation on relevant areas.
-
Provide existing requirements - Do you have a requirements document, epic, or feature description you can share?
-
Describe the focus - Can you briefly describe what enhancement or feature you're planning? For example:
- 'Adding payment processing to the user service'
- 'Refactoring the authentication module'
- 'Integrating with a new third-party API'
-
Document everything - Or should I proceed with comprehensive documentation of the entire codebase? (Note: This may create excessive documentation for large projects)
Please let me know your preference, or I can proceed with full documentation if you prefer."
Based on their response:
- If they choose option 1-3: Use that context to focus documentation
- If they choose option 4 or decline: Proceed with comprehensive analysis below
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 · 350 lines · 0 tokens per session scan A 762a3ee0448e
document-project is a command published in the GitHub repository julianobarbosa/azure-finops-mcp-server (2 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,938 tokens. A static security scan graded it A with 0 findings. It is 94% identical to document-project, differing in 2 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.