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 gabriele-mastrapasqua/codex-lean-team --skill root-cause-debugginggit clone --depth 1 https://github.com/gabriele-mastrapasqua/codex-lean-teamWrote 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/gabriele-mastrapasqua/codex-lean-team/root-cause-debugging)<a href="https://agentmods.dev/skills/gabriele-mastrapasqua/codex-lean-team/root-cause-debugging"><img src="https://agentmods.dev/badge/skills/gabriele-mastrapasqua/codex-lean-team/root-cause-debugging/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/gabriele-mastrapasqua/codex-lean-team/root-cause-debugging"><img src="https://agentmods.dev/badge/skills/gabriele-mastrapasqua/codex-lean-team/root-cause-debugging.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 | $0.00031 | $0.00150 |
| Opus 5.5 | $0.00012 | $0.00060 |
| Sonnet 5.5 | $0.00006 | $0.00030 |
| Haiku 4.5 | $0.00003 | $0.00015 |
Grade A, and why
root-cause-debugging 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.
What it actually says
Use an evidence-first debugging loop:
- Restate the observed failure and separate facts from assumptions.
- Identify the smallest reproducible path.
- Trace state and data across boundaries.
- Rank hypotheses by likelihood and discriminating evidence.
- Run or propose the cheapest test that eliminates the most hypotheses.
- Fix the root cause rather than masking the symptom.
- Add a regression test and verify adjacent failure modes.
Escalate to a specialist only when evidence points to that domain. Keep a short hypothesis ledger; do not dump exhaustive speculation.
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 · 17 lines · 31 tokens per session scan A 48bb9d3e19ae
root-cause-debugging is a skill published in the GitHub repository gabriele-mastrapasqua/codex-lean-team (3 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 150 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 2026-08-31.
Other skills, from other repositories
agenticx-agent-obs
Configure AgenticX observability (Traces, Observations, Sessions, Token & latency metrics) for Near desktop, Python/TypeScript/Go agents, LangChain/LangGraph, and OpenAI-compatible apps, or diagnose failed traces and slow spans. Use when the user asks to enable trace reporting, connect AgenticX observability, inspect…
openlore-debug
Debug with OpenLore structural context, an explicit root-cause hypothesis, and RED/GREEN verification. Use when a bug, failure, or regression needs diagnosis and repair.
openlore-analyze-codebase
Run a full static OpenLore analysis and summarize architecture, call graph, refactoring issues, and duplicate code. Use when asked to analyze, map, or assess a codebase without LLM inference.
ap-depth-prober
Report the compatibility redirect to ap-worker; this retired role cannot perform new work.
ap-preflight-probe
Report the compatibility redirect to C0; this retired role cannot perform new work.
profile-memory
Profile memory usage in sidecar using Go pprof, system tools, and heap analysis. Covers identifying memory leaks, goroutine leaks, file descriptor accumulation, and CPU profiling. Use when investigating memory issues, profiling performance, debugging memory leaks, or diagnosing unresponsive plugins.