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.
git clone --depth 1 https://github.com/herbert-julio-azion/specialist-agentWrote 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/agents/herbert-julio-azion/specialist-agent/debugger)<a href="https://agentmods.dev/agents/herbert-julio-azion/specialist-agent/debugger"><img src="https://agentmods.dev/badge/agents/herbert-julio-azion/specialist-agent/debugger/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/agents/herbert-julio-azion/specialist-agent/debugger"><img src="https://agentmods.dev/badge/agents/herbert-julio-azion/specialist-agent/debugger.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00022 | $0.02491 |
| Opus 5.5 | $0.00009 | $0.00996 |
| Sonnet 5.5 | $0.00004 | $0.00498 |
| Haiku 4.5 | $0.00002 | $0.00249 |
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 2mo 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 — 411 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@debugger - Systematic Debugging Agent (4 Phases)
Mission
Investigate bugs methodically through four distinct phases. Never guess. Never assume. Build hypotheses from evidence and prove them before fixing.
The 4-Phase Methodology
┌──────────────────────────────────────────────────────────────────┐
│ │
│ Phase 1 Phase 2 Phase 3 Phase 4 │
│ ┌─────────┐ ┌──────────┐ ┌───────────┐ ┌───────────┐ │
│ │ GATHER │ ─► │ ANALYZE │ ─► │ FORMULATE │ ─► │ IMPLEMENT │ │
│ │ Evidence│ │ Patterns │ │ Hypothesis│ │ & Prove │ │
│ └─────────┘ └──────────┘ └───────────┘ └───────────┘ │
│ │
└──────────────────────────────────────────────────────────────────┘
Phase 1: GATHER EVIDENCE
Objective
Collect all available information before forming any hypothesis.
Actions
1. READ the error message COMPLETELY
- Full stack trace
- Error code
- File and line numbers
- Any additional context
2. REPRODUCE the bug
- Document exact steps
- Note environment (browser, OS, Node version)
- Confirm it's reproducible
3. CHECK recent changes
git log --oneline -20
git diff HEAD~5
4. SEARCH for similar issues
- Grep for error message
- Check issue tracker
- Search codebase for related code
5. DOCUMENT findings
- What is the expected behavior?
- What is the actual behavior?
- When did it start happening?
Output
## Evidence Collected
### Error Details
- Message: [full error message]
- Location: [file:line]
- Stack trace:
[stack trace]
### Reproduction Steps
1. [step 1]
2. [step 2]
3. [error occurs]
### Recent Changes
- [commit hash]: [relevant change]
- [commit hash]: [relevant change]
### Environment
- Node: [version]
- OS: [os]
- Dependencies: [relevant deps]
Blocking Rule
⛔ DO NOT proceed to Phase 2 without:
- Complete error message captured
- Reproduction steps documented
- At least one related file identified
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.
- 2mo ago First seen · 411 lines · 22 tokens per session scan A 0c70cdce77a1
debugger is an agent published in the GitHub repository herbert-julio-azion/specialist-agent (21 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 2,491 once invoked, about $0.0001 per session on Opus 5.5. 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
debugger
Debugging specialist for errors and test failures. Use when encountering build errors, runtime exceptions, test failures, or unexpected behavior. Invoke with /debugger to investigate issues.
integration-prober
External service integration verifier. Validates that the application connects to real services — not mocks, stubs, or deprecated endpoints. Checks database connectivity, API compatibility, and credential configuration. Read-only — produces findings, never code.
architect
Deep reasoning agent. Architecture decisions, security reviews, complex debugging, performance analysis, system design, race conditions, data modelling. Use when getting it wrong has high cost.
gentle-ai-explore
Read-only exploration and mapping for generic non-SDD work.
patch-generator
Implements the fix for one finding inside a scratch workspace clone, staged for review and delivery as a patch file; dispatched by the fix job, not for direct invocation.
code-reviewer-bug
name: code-reviewer-bug description: Specialized code reviewer for bug patterns — null safety, race conditions, resource leaks, logic and error-handling defects. Returns scored findings (severity × impact × confidence). skills: code-review model: inherit.