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 paiml/paiml-mcp-agent-toolkit --skill pmat-contextgit clone --depth 1 https://github.com/paiml/paiml-mcp-agent-toolkitWrote 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/paiml/paiml-mcp-agent-toolkit/pmat-context)<a href="https://agentmods.dev/skills/paiml/paiml-mcp-agent-toolkit/pmat-context"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-context/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/paiml/paiml-mcp-agent-toolkit/pmat-context"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-context.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00112 | $0.02617 |
| Opus 5 | $0.00056 | $0.01308 |
| Sonnet 5 | $0.00022 | $0.00523 |
| Haiku 4.5 | $0.00011 | $0.00262 |
Grade A, and why
Deep Context Generation with PMAT 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.
How it starts
The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PMAT Deep Context Generation Skill
You are an expert at generating comprehensive codebase context using PMAT (Pragmatic AI Labs MCP Agent Toolkit).
When to Activate
This skill should automatically activate when:
- User asks for codebase overview, architecture, or "how does this work?"
- Starting work on unfamiliar code ("I'm new to this project")
- Need to understand project structure before making changes
- Onboarding scenarios ("walk me through this codebase")
- Creating technical documentation or specifications
Core Command: pmat context
pmat context --output context.md --format llm-optimized
What it does:
- Scans entire codebase (respects .gitignore)
- Generates highly compressed markdown (60-80% smaller than raw code)
- Includes architecture diagrams (ASCII art tree structure)
- Provides complexity heatmaps and hotspot identification
- Extracts key abstractions (classes, functions, modules)
- Maps dependencies and import relationships
Performance:
- Small projects (<100 files): <500ms
- Medium projects (100-1K files): <2s
- Large projects (10K+ files): 5-15s
Usage Workflow
Step 1: Generate Full Context
When user needs comprehensive understanding:
pmat context --output deep_context.md --format llm-optimized
Then read the generated file:
cat deep_context.md
Step 2: Analyze the Context
Look for these key sections in the generated context:
-
Project Overview
- Language distribution
- Total files/lines of code
- Main entry points
-
Architecture Tree
- Directory structure
- Module organization
- Key components
-
Complexity Heatmap
- Files sorted by complexity score
- Hotspots requiring attention
- Maintainability indicators
-
Dependency Graph
- Import relationships
- Cross-module dependencies
- External libraries
-
Key Abstractions
- Classes and interfaces
- Public APIs
- Core data structures
Step 3: Answer User Questions
Use the generated context to:
- Explain architecture decisions
- Identify where to make changes
- Suggest refactoring targets
- Map feature implementation paths
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 · 345 lines · 112 tokens per session scan A c8dba9562a69
Deep Context Generation with PMAT is a skill published in the GitHub repository paiml/paiml-mcp-agent-toolkit (164 stars, last pushed today), licensed MIT. It adds 112 tokens to every session and 2,617 once invoked, about $0.0006 per session on Opus 5. 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 2026-08-30.
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