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/lancegui/causal-powers/wrong-number-debuggingnpx skills add lancegui/causal-powers --skill wrong-number-debugginggit clone --depth 1 https://github.com/lancegui/causal-powersWhat 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 | $0.00150 | $0.02445 |
| Opus 5 | $0.00075 | $0.01222 |
| Sonnet 5 | $0.00030 | $0.00489 |
| Haiku 4.5 | $0.00015 | $0.00245 |
Grade A, and why
wrong-number-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 yesterday.
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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wrong-Number Debugging
Overview
A surprising number is data trying to tell you something. The instinct is to patch it — add a dropna, a distinct, a filter — until it looks reasonable. That instinct is how a symptom gets hidden and the real bug ships. Routing: code THROWS or a test fails → superpowers:systematic-debugging. Code runs CLEAN but the number is wrong → this skill. Either way, a remedy that changes design/sample/spec is analysis-checkpoints territory, not a fix.
Core principle: Locate the bug by bisecting the pipeline, not by guessing at fixes. The number is wrong somewhere specific — find where, then you'll know why.
Why analytics debugging is its own thing
In software a bug announces itself with a stack trace pointing near the cause. In analysis there is no trace — the pipeline ran clean, and the only signal you have is that the output is wrong. Work backward through the chain of joins, filters, groupings, and recodes, checking the number at each stage, until you find where it stopped being right. That stage contains the bug.
The loop
REPRODUCE → LOCATE (bisect) → EXPLAIN → FIX AT THE SOURCE → RE-CONTRACT
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REPRODUCE — Pin the wrong number down to a deterministic, minimal case. Same input, same seed, same result every time. If it's intermittent, you have hidden state (ordering, randomness, a mutated global) and that is the bug. Shrink to the smallest subset of rows that still shows it — debugging on 50 rows beats debugging on 50 million.
Then state the diagnostic roadmap and get a quick nod before running scans. A bisection is a multi-step plan — state it in 2–4 lines: the stages you'll check, in what order, where you'll start. "Roadmap: (1) pull the flagged records, (2) check X, (3) scan the panel for the pattern, (4) trace how it's produced. Stays a diagnosis — any drop/merge comes back to you. Good, or reprioritize?" Get agreement once, then execute autonomously — only re-stopping if a step turns into a design/sample/spec change. This is where the user's local knowledge reorders your search cheaply. Skip it for a one-or-two-step check.
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.
- yesterday First seen · 92 lines · 150 tokens per session scan A 1b4afe7fff9f
wrong-number-debugging is a skill published in the GitHub repository lancegui/causal-powers (2 stars, last pushed 8d ago), licensed MIT. It adds 150 tokens to every session and 2,445 once invoked, about $0.0007 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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