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_load_specgit 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_load_spec)<a href="https://agentmods.dev/commands/cafreeman/foundry-mcp/foundry_load_spec"><img src="https://agentmods.dev/badge/commands/cafreeman/foundry-mcp/foundry_load_spec.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 | $0.00000 | $0.01251 |
| Opus 5 | $0.00000 | $0.00626 |
| Sonnet 5 | $0.00000 | $0.00250 |
| Haiku 4.5 | $0.00000 | $0.00125 |
Grade A, and why
foundry_load_spec 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 4d 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Load Spec With Foundry
Overview
Load a complete specification using intelligent fuzzy matching to enable focused development work.
Context Gathering & Parameter Resolution
Discovery Workflow for Missing Parameters:
- Project Name Missing: Execute list_projects to display available options with metadata and guide user selection
- Spec Query Missing: Execute list_specs for the target project and present specs in user-friendly format
- Both Present: Proceed directly to fuzzy matching and loading workflow
Validation & Preparation:
- Verify target project exists in Foundry system before attempting spec operations
- Set user expectations about fuzzy matching capabilities and potential disambiguation needs
- Prepare for potential multiple matches requiring user selection
Comprehensive Loading Workflow
Step 1: Intelligent Spec Discovery
Parameter Resolution Strategy:
- Missing Project: Show list_projects with creation dates and brief descriptions for informed selection
- Missing Spec: Display list_specs results with feature names, dates, and completion status
- Fuzzy Matching: Use provided spec query with intelligent pattern matching
Step 2: Spec Loading with Fuzzy Matching
Execute Load Operation:
{"name":"load_spec","arguments":{"project_name":"$1","spec_name":"$2"}}
Match Quality Assessment:
- High Confidence Single Match: Load directly and proceed to content analysis
- Multiple Matches: Present candidates with feature names, creation dates, and similarity scores
- Low Confidence: Show alternatives and ask for clarification or more specific search terms
- No Matches: Suggest creating new spec or provide spelling/naming guidance
Step 3: Comprehensive Content Analysis
Specification Overview:
- Feature Summary: Extract and highlight main feature purpose, scope, and value proposition
- Development Status: Analyze task completion percentage and identify remaining work
- Context Assessment: Review requirements, acceptance criteria, and implementation approach
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.
- 4d ago First seen · 125 lines · 0 tokens per session scan A 19f390edc21f
foundry_load_spec 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 1,251 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
git
Git operations with intelligent commit messages and workflow optimization.
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.