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 jpoutrin/product-forge --skill debug-orchestratorgit clone --depth 1 https://github.com/jpoutrin/product-forgeWrote 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/jpoutrin/product-forge/debug-orchestrator)<a href="https://agentmods.dev/skills/jpoutrin/product-forge/debug-orchestrator"><img src="https://agentmods.dev/badge/skills/jpoutrin/product-forge/debug-orchestrator/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/jpoutrin/product-forge/debug-orchestrator"><img src="https://agentmods.dev/badge/skills/jpoutrin/product-forge/debug-orchestrator.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 | $0.00047 | $0.01768 |
| Opus 5.5 | $0.00019 | $0.00707 |
| Sonnet 5.5 | $0.00009 | $0.00354 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
debug-orchestrator 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 1mo 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 — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Debug Orchestrator
Coordinate complex debugging investigations by spawning and managing specialized debug expert agents. Use this when a debugging issue spans multiple domains or requires deep expertise in specific areas.
When to Use
Invoke this skill when:
- Debugging issue involves multiple layers (UI, API, database, network)
- Root cause is unclear and requires systematic investigation
- Issue requires domain-specific expertise (e.g., React performance, SQL query optimization, network protocol analysis)
- Multiple hypotheses need to be tested in parallel
- Previous debugging attempts have failed or been incomplete
Do NOT Use For
- Simple, single-domain bugs (use direct debugging instead)
- Issues with obvious root causes
- Syntax errors or compilation failures
- Basic troubleshooting that doesn't require expert coordination
Orchestration Strategy
1. Issue Analysis
First, analyze the debugging request to understand:
- Symptoms: What's broken or behaving incorrectly?
- Context: What was the user doing when the issue occurred?
- Scope: Which systems/layers are potentially affected?
- Evidence: Logs, error messages, screenshots, network traces
2. Expert Agent Selection
Based on the analysis, spawn appropriate specialist agents:
Available Debug Experts
| Agent Type | When to Use | Example Issues |
|---|---|---|
product-design:web-debugger |
Browser-based issues, DOM manipulation, JavaScript errors | React component not rendering, event handlers failing, XHR errors |
product-design:console-debugging |
JavaScript runtime errors, console warnings, client-side logs | Uncaught exceptions, deprecation warnings, third-party library errors |
product-design:network-inspection |
API calls, HTTP requests, network failures | 404 errors, CORS issues, slow API responses, failed requests |
python-experts:django-expert |
Django-specific backend issues | ORM queries, middleware errors, view logic, template rendering |
python-experts:fastapi-expert |
FastAPI async issues, request validation | Async handler errors, Pydantic validation, dependency injection |
devops-data:cto-architect |
System design issues, architecture decisions | Distributed system failures, scaling issues, design flaws |
security-compliance:mcp-security-expert |
Security-related bugs, authentication issues | Auth failures, permission errors, input validation bypasses |
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 216 lines · 47 tokens per session scan A 19b5fe554a5a
debug-orchestrator is a skill published in the GitHub repository jpoutrin/product-forge (16 stars, last pushed 7mo ago), licensed MIT. It adds 47 tokens to every session and 1,768 once invoked, about $0.0002 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 2026-09-03.
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