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 commands/cafreeman/foundry-mcp/foundry_analyze_projectgit clone --depth 1 https://github.com/cafreeman/foundry-mcpWrote 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/cafreeman/foundry-mcp/foundry_analyze_project)<a href="https://agentmods.dev/commands/cafreeman/foundry-mcp/foundry_analyze_project"><img src="https://agentmods.dev/badge/commands/cafreeman/foundry-mcp/foundry_analyze_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 | $0.00000 | $0.00952 |
| Opus 5 | $0.00000 | $0.00476 |
| Sonnet 5 | $0.00000 | $0.00190 |
| Haiku 4.5 | $0.00000 | $0.00095 |
Grade A, and why
foundry_analyze_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.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Project With Foundry
Overview
Analyze an existing codebase and create comprehensive Foundry project documents using MCP tools.
Context Gathering Phase
Before Starting Analysis:
- Repository Structure Scan: Examine README, package.json/Cargo.toml/pyproject.toml, docker files, CI configs
- Foundry System Check: Use list_projects to avoid naming conflicts and understand existing portfolio
- Deployment Context: Look for infrastructure configs, deployment scripts, environment files
Detailed Analysis Workflow
Step 1: Repository Discovery
Technical Infrastructure:
- Languages and frameworks (check package managers, import patterns, build configs)
- Build tools and deployment infrastructure (CI/CD, containerization, cloud configs)
- Service architecture and component relationships (microservices, monolith, APIs)
- Documentation quality and project maturity indicators
Step 2: Document Drafting (No Boilerplate Content)
Vision Document (200+ chars minimum):
- Clear problem statement with specific user pain points
- Target audience and their core motivations
- Unique value proposition and competitive advantages
- High-level roadmap priorities and success metrics
Tech Stack Document (150+ chars minimum):
- Primary languages and frameworks with specific rationale
- Infrastructure and deployment platform choices with reasoning
- Database and data storage decisions with justification
- Development tools, testing, and CI/CD approach
- External integrations and API dependencies
Summary Document (100+ chars minimum):
- 2-3 sentences capturing project essence for immediate context loading
- Should enable future AI assistants to understand project purpose instantly
Step 3: User Collaboration & Refinement
Review Process:
- Present all three documents simultaneously for holistic feedback
- Ask specific clarifying questions about technical decisions and business context
- Validate assumptions about user needs, market positioning, and technical constraints
- Ensure all content meets quality standards and minimum length requirements
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 · 100 lines · 0 tokens per session scan A 5784eeefd5c9
foundry_analyze_project is a command published in the GitHub repository cafreeman/foundry-mcp (4 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 952 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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.
constitution
Create or update the project constitution from interactive or provided principle inputs.
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.