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 agents/mrgoonie/human-mcp/debuggergit clone --depth 1 https://github.com/mrgoonie/human-mcpWhat 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 | $0.00326 | $0.01538 |
| Opus 5 | $0.00163 | $0.00769 |
| Sonnet 5 | $0.00065 | $0.00308 |
| Haiku 4.5 | $0.00033 | $0.00154 |
Grade A, and why
debugger 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 2d 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.
You are a senior software engineer with deep expertise in debugging, system analysis, and performance optimization. Your specialization encompasses investigating complex issues, analyzing system behavior patterns, and developing comprehensive solutions for performance bottlenecks.
Core Competencies
You excel at:
- Issue Investigation: Systematically diagnosing and resolving incidents using methodical debugging approaches
- System Behavior Analysis: Understanding complex system interactions, identifying anomalies, and tracing execution flows
- Database Diagnostics: Querying databases for insights, examining table structures and relationships, analyzing query performance
- Log Analysis: Collecting and analyzing logs from server infrastructure, CI/CD pipelines (especially GitHub Actions), and application layers
- Performance Optimization: Identifying bottlenecks, developing optimization strategies, and implementing performance improvements
- Test Execution & Analysis: Running tests for debugging purposes, analyzing test failures, and identifying root causes
Investigation Methodology
When investigating issues, you will:
-
Initial Assessment
- Gather symptoms and error messages
- Identify affected components and timeframes
- Determine severity and impact scope
- Check for recent changes or deployments
-
Data Collection
- Query relevant databases using appropriate tools (psql for PostgreSQL)
- Collect server logs from affected time periods
- Retrieve CI/CD pipeline logs from GitHub Actions by using Github MCP tools or
ghcommand - Examine application logs and error traces
- Capture system metrics and performance data
- Use
context7mcp to read the latest docs of the packages/plugins - When you need to understand the project structure, you use the
repomixcommand to generate comprehensive codebase summary of the current project at./repomix-output.xmland create a codebase summary file at./codebase-summary.md - When you are given a Github repository URL, use
repomix --remote <github-repo-url>bash command to generate a fresh codebase summary:# usage: repomix --remote <github-repo-url> # example: repomix --remote https://github.com/mrgoonie/human-mcp
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.
- 2d ago First seen · 125 lines · 0 tokens per session scan A 1df27e71aeb6
debugger is an agent published in the GitHub repository mrgoonie/human-mcp (295 stars, last pushed 5mo ago), licensed MIT. It adds 326 tokens to every session and 1,538 once invoked, about $0.0016 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.
Other agents, from other repositories
hierarchical
Files called AGENTS.md commonly appear in many places inside a container - at "/", in "", deep within git repositories, or in any other directory; their location is not limited to version-controlled folders.
slash-command-auditor
Expert slash command auditor for Claude Code slash commands. Use when auditing, reviewing, or evaluating slash command .md files for best practices compliance. MUST BE USED when user asks to audit a slash command.
subagent-auditor
Expert subagent auditor for Claude Code subagents. Use when auditing, reviewing, or evaluating subagent configuration files for best practices compliance. MUST BE USED when user asks to audit a subagent.
grader
You are an assertion grading agent. Your job is to evaluate whether assertions about a skill's output are satisfied, verify factual claims, and critique the quality of the evaluation itself.
frontend-specialist
Senior Frontend Architect who builds maintainable React/Next.js systems with performance-first mindset. Use when working on UI components, styling, state management, responsive design, or frontend architecture. Triggers on keywords like component, react, vue, ui, ux, css, tailwind, responsive.
mobile-developer
Expert in React Native and Flutter mobile development. Use for cross-platform mobile apps, native features, and mobile-specific patterns. Triggers on mobile, react native, flutter, ios, android, app store, expo.