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 furkangonel/cowrangler --skill systematic-debugginggit clone --depth 1 https://github.com/furkangonel/cowranglerWrote 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/furkangonel/cowrangler/systematic-debugging)<a href="https://agentmods.dev/skills/furkangonel/cowrangler/systematic-debugging"><img src="https://agentmods.dev/badge/skills/furkangonel/cowrangler/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/furkangonel/cowrangler/systematic-debugging"><img src="https://agentmods.dev/badge/skills/furkangonel/cowrangler/systematic-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 | $0.00014 | $0.02529 |
| Opus 5.5 | $0.00006 | $0.01012 |
| Sonnet 5.5 | $0.00003 | $0.00506 |
| Haiku 4.5 | $0.00001 | $0.00253 |
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.
curl -v -X POST http://localhost:3000/api/orders \ How it starts
The opening of the file, as written. The whole thing — 359 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging SOP
A methodical process for diagnosing bugs, production incidents, and unexpected behavior — without random guessing.
When to Use
- User has a bug with unexpected behavior to diagnose
- User has a production incident or error to investigate
- User has a test that is failing for an unclear reason
- User is spending more than 15 minutes on a bug without a clear hypothesis
- User says "I have no idea why this is happening"
The Debugging Process
1. REPRODUCE → 2. ISOLATE → 3. HYPOTHESIZE → 4. TEST → 5. FIX → 6. VERIFY
Never skip to step 5. Fixing a symptom without a root cause always creates more bugs.
Phase 1 — Reproduce
Goal: Produce the bug on demand, reliably.
Questions to answer:
- Can you make it happen every time, or is it intermittent?
- What are the exact steps to trigger it?
- What did you expect to happen? What actually happened?
- When did it start happening? What changed around that time?
- Does it happen in all environments, or only production/staging/local?
Reproduce checklist:
- Run the failing code and capture the exact error message and stack trace
- Note the exact input that triggers the bug
- Confirm it was working before (check git log, recent deployments)
- Test in the same environment where the bug appears
# Capture full error output
python app.py 2>&1 | tee bug_repro.log
# Reproduce with exact same inputs
curl -v -X POST http://localhost:3000/api/orders \
-H "Content-Type: application/json" \
-d '{"item_id": 42, "quantity": 0}' \
2>&1 | tee repro.log
If intermittent: Add logging before the suspected failure point and wait for it to happen again. Do not proceed without a reliable reproduction path.
Phase 2 — Isolate
Goal: Narrow the failing code to the smallest possible unit.
Binary Search Debugging
Cut the problem space in half with each step:
Full system fails
→ Does the API layer fail? (yes)
→ Does it fail with all requests? (no, only POST /orders)
→ Does it fail for all users? (no, only when quantity = 0)
→ ROOT: input validation doesn't reject zero quantity
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 · 359 lines · 14 tokens per session scan A ca35e2937ae8
systematic-debugging is a skill published in the GitHub repository furkangonel/cowrangler (1 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 2,529 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 2026-08-31.
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