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-tech-debtgit 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-tech-debt)<a href="https://agentmods.dev/skills/paiml/paiml-mcp-agent-toolkit/pmat-tech-debt"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-tech-debt/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-tech-debt"><img src="https://agentmods.dev/badge/skills/paiml/paiml-mcp-agent-toolkit/pmat-tech-debt.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 | $0.00123 | $0.03543 |
| Opus 5 | $0.00062 | $0.01772 |
| Sonnet 5 | $0.00025 | $0.00709 |
| Haiku 4.5 | $0.00012 | $0.00354 |
Grade A, and why
Technical Debt Tracking 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 11d 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 — 404 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PMAT Technical Debt Tracking Skill
You are an expert at identifying, quantifying, and managing technical debt using PMAT (Pragmatic AI Labs MCP Agent Toolkit).
When to Activate
This skill should automatically activate when:
- User mentions "technical debt", "tech debt", or "TD"
- User asks about TODO, FIXME, HACK comments in the codebase
- Planning sprint work and need debt repayment estimates
- Conducting code quality audits or assessments
- Tracking debt trends over time (sprint-to-sprint comparison)
Core Concept: SATD (Self-Admitted Technical Debt)
Definition: Comments explicitly admitting suboptimal implementation Types:
- TODO: Deferred work, future enhancements
- FIXME: Known bugs or issues requiring fixes
- HACK: Temporary workarounds needing proper solutions
- XXX: Critical issues requiring immediate attention
- NOTE: Important context or warnings
Scientific Foundation: Based on research by Potdar & Shihab (2014) on self-admitted technical debt in code comments.
Available PMAT Commands
1. Detect Technical Debt
pmat analyze satd --path <file_or_directory> --output debt_report.json
Output: All SATD annotations with location, type, and context
2. Quantify Debt Hours
pmat analyze tech-debt --path <directory> --estimate-hours --output debt_hours.json
Output: Estimated hours to resolve each debt item
3. Track Debt Trends
# Baseline measurement
pmat analyze satd --path . --output debt_baseline.json
# After sprint work
pmat analyze satd --path . --output debt_current.json
# Compare trends
pmat compare-debt --baseline debt_baseline.json --current debt_current.json
4. Generate Debt Report
pmat analyze satd --path . --format markdown --output TECH_DEBT_REPORT.md
Output: Markdown report suitable for documentation or stakeholder review
Usage Workflow
Step 1: Initial Debt Assessment
Establish baseline understanding of technical debt:
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.
- 11d ago First seen · 404 lines · 123 tokens per session scan A 33a8836b807d
Technical Debt Tracking with PMAT is a skill published in the GitHub repository paiml/paiml-mcp-agent-toolkit (164 stars, last pushed yesterday), licensed MIT. It adds 123 tokens to every session and 3,543 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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