Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/HezaoHezao/poirotnpx agentmods add skills/hezaohezao/poirot/systematic-debuggingWrote 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/hezaohezao/poirot/systematic-debugging)<a href="https://agentmods.dev/skills/hezaohezao/poirot/systematic-debugging"><img src="https://agentmods.dev/badge/skills/hezaohezao/poirot/systematic-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/hezaohezao/poirot/systematic-debugging"><img src="https://agentmods.dev/badge/skills/hezaohezao/poirot/systematic-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00016 | $0.02592 |
| Opus 5.5 | $0.00006 | $0.01037 |
| Sonnet 5.5 | $0.00003 | $0.00518 |
| Haiku 4.5 | $0.00002 | $0.00259 |
Grade A, and why
systematic-debugging scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
2. **HTTP script / curl** against a running dev server. How it starts
The opening of the file, as written. The whole thing — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging
Overview
Random fixes waste time and create new bugs. Quick patches mask underlying issues.
Core principle: ALWAYS find root cause before attempting fixes. Symptom fixes are failure.
The Iron Law
NO FIXES WITHOUT ROOT CAUSE INVESTIGATION FIRST
If you haven't completed Phase 1, you cannot propose fixes.
The Feedback Loop Rule
The feedback loop is the debugging work. Before reading code to build a theory, create or identify a tight command that can go red on the user's exact symptom and green when the bug is fixed. A tight loop is fast, deterministic, agent-runnable, and specific enough to catch this bug — not merely "doesn't crash".
When a clean repro is hard, spend disproportionate effort building the loop.
When to Use
Use for ANY technical issue: test failures, bugs in production, unexpected behavior, performance problems, build failures, integration issues.
Use ESPECIALLY when:
- Under time pressure (emergencies make guessing tempting)
- "Just one quick fix" seems obvious
- You've already tried multiple fixes
- Previous fix didn't work
- You don't fully understand the issue
The Four Phases
You MUST complete each phase before proceeding to the next.
Phase 1: Root Cause Investigation
BEFORE attempting ANY fix:
1. Read Error Messages Carefully
- Don't skip past errors or warnings
- They often contain the exact solution
- Read stack traces completely
- Note line numbers, file paths, error codes
Action: Use read_file on the relevant source files. Use bash with
grep to find the error string in the codebase.
2. Build a Tight Feedback Loop
- Can you trigger the user's exact symptom with one command?
- Does the command fail for this bug and only pass once the bug is fixed?
- Is it fast enough to run repeatedly?
- Is it deterministic?
- If not reproducible → gather more data, don't guess.
Ways to construct a loop — try in roughly this order:
- Failing test at the seam that reaches the bug: unit, integration, or e2e.
- HTTP script / curl against a running dev server.
- CLI invocation with fixture input, diffing stdout/stderr against expected.
- Headless browser script (Playwright/Puppeteer) asserting on DOM/console/network.
- Replay a captured trace: HAR, request payload, event log, webhook body.
- Throwaway harness that boots the smallest useful slice of the system.
- Property / fuzz loop when the bug is intermittent wrong output.
- Bisection harness suitable for
git bisect run. - Differential loop comparing old vs new version, two configs, or two datasets.
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 · 347 lines · 16 tokens per session scan A 386ba0fe023e
systematic-debugging is a skill published in the GitHub repository HezaoHezao/poirot (251 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 2,592 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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