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 agentmods add skills/adityawrk/analytics-with-claude-code/systematic-debugnpx skills add adityawrk/analytics-with-claude-code --skill systematic-debuggit clone --depth 1 https://github.com/adityawrk/analytics-with-claude-codeWrote 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/adityawrk/analytics-with-claude-code/systematic-debug)<a href="https://agentmods.dev/skills/adityawrk/analytics-with-claude-code/systematic-debug"><img src="https://agentmods.dev/badge/skills/adityawrk/analytics-with-claude-code/systematic-debug.svg" alt="Measured on agentmods" 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 | $0.00064 | $0.01856 |
| Opus 5 | $0.00032 | $0.00928 |
| Sonnet 5 | $0.00013 | $0.00371 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
systematic-debug 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 4d 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Systematic Debugging for Analytics
A 4-phase structured debugging methodology. Follow these phases in strict order. No shortcuts. No cargo-cult debugging. No "just try this and see."
Ground Rules
Read these first. They are non-negotiable.
- NEVER skip Phase 1. Do not attempt a fix before reproducing the error and reading the full output. No exceptions.
- NEVER make multiple changes at once. One variable at a time. If you change two things and the problem goes away, you do not know which one fixed it.
- 3-Strike Rule. After 3 failed fix attempts, STOP. Summarize what you have tried, what you have learned, and escalate to the user. Do not keep guessing.
- State your hypothesis. Before every fix attempt, tell the user what you believe is wrong and why. "I think X because Y, so I will try Z."
- Clean up after yourself. Remove all debug instrumentation (extra logging, LIMIT clauses, temp tables, print statements) before declaring the issue resolved.
Phase 1: Reproduce and Gather Evidence
Goal: See the failure with your own eyes. Understand exactly what is happening before forming any opinion about why.
Steps
-
Run the failing query or script exactly as reported.
- Use the same database, schema, and role if possible.
- Do not modify the query before running it.
- Capture the full error output — not just the first line.
-
Read the complete error message.
- Copy the full stack trace or error output.
- Identify the specific line, column, or object that failed.
- Note the error code if one is provided.
-
Check the environment.
- What database/warehouse is this running against?
- What schema or dataset is active?
- Are there any session-level settings (timezone, role, warehouse size)?
-
Check data freshness. (Analytics-specific)
- When was the source data last updated?
- Is the failure caused by stale or missing data rather than a code bug?
- Run
SELECT MAX(updated_at)or equivalent on key source tables.
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.
- 4d ago First seen · 197 lines · 64 tokens per session scan A c5555331b000
systematic-debug is a skill published in the GitHub repository adityawrk/analytics-with-claude-code (5 stars, last pushed 6mo ago), licensed MIT. It adds 64 tokens to every session and 1,856 once invoked, about $0.0003 per session on Opus 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.
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