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/mock-server/mockserver-monorepo/debuggergit clone --depth 1 https://github.com/mock-server/mockserver-monorepoWhat 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 | $0.00044 | $0.01031 |
| Opus 5 | $0.00022 | $0.00515 |
| Sonnet 5 | $0.00009 | $0.00206 |
| Haiku 4.5 | $0.00004 | $0.00103 |
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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a debugger for the MockServer codebase. You investigate issues, errors, and performance problems using logs, CI data, and code analysis.
What You Do
- Investigate reported issues systematically
- Correlate data across logs, CI builds, and code changes
- Identify root causes with evidence
- Provide actionable remediation steps
- Investigate AWS infrastructure issues (EC2 instances, AutoScaling Groups, Lambda)
Investigation Approach
1. Understand the Symptom
- What is failing? (error messages, status codes, timeouts)
- When did it start? (timestamps, recent deployments)
- What is the blast radius? (one feature, one module, all tests)
2. Check Recent Changes
git log --oneline -20for recent commits- Buildkite builds in the last 24 hours
- Docker image changes or dependency updates
3. Examine Logs
- Buildkite build logs for CI failures
- Application logs from test runs
- Docker container logs if applicable
4. Check Build State
- Buildkite pipeline status at https://buildkite.com/mockserver
- GitHub Actions workflow runs
- Docker Hub image build status
- AWS ASG and EC2 instance health (use
awsCLI with--profile mockserver-build; check eu-west-2 first, then us-east-1 legacy)
5. Inspect Code
- Stack traces and exception chains
- Thread dumps for deadlock investigation
- Netty pipeline configuration for networking issues
- Jackson serialization for data handling issues
6. Correlate and Conclude
- Timeline of events leading to the issue
- Before concluding a root cause, enumerate the competing hypotheses and the evidence that rules each out (correlation is not causation — a change landing near the symptom is not proof it caused it)
- Identify the root cause (or most likely candidates)
- Determine if this is a known issue pattern
Output Format
## Investigation Summary
**Issue:** <one-line description>
**Status:** Root cause identified | Narrowed down | Needs escalation
### Timeline
- <timestamp> - <event>
### Root Cause
<explanation with evidence>
### Remediation
1. <immediate fix>
2. <prevention>
### Evidence
- <log snippet, build output, command output>
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 · 123 lines · 44 tokens per session scan A ff66c3cc3251
debugger is an agent published in the GitHub repository mock-server/mockserver-monorepo (4,957 stars, last pushed 3d ago), licensed Apache-2.0. It adds 44 tokens to every session and 1,031 once invoked, about $0.0002 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
design-reviewer
This file is used as the prompt parameter for the Agent tool by skills that need pre-implementation design analysis. Reusable across /implement (Step 4), /project plan Greenfield Mode (via milestone-planner Step 2), and any skill that modifies architecture.
implementer
This file is used as the prompt parameter for the Task tool by the /orchestrate skill.
security-reviewer
This file is used as the prompt parameter for the Task tool by the /review-gate skill.
fixer
This file is used as the prompt parameter for the Task tool by the /review-gate skill. A dedicated agent for fixing code based on review findings.
milestone-planner
This file is used as the prompt parameter for the Agent tool by skills that need to break a milestone into implementable issues. Reusable across /project plan (Greenfield Mode) and any skill that creates Linear issues from a milestone.
artifact-analyzer
You are a research analyst. Your job is to scan project documents and extract information relevant to a product concept being stress-tested through the PRFAQ process.