anomaly-investigation
Use when the user has a known, already-observed anomaly in their data — a metric spike or drop, an outlier, an unexpected number — and wants its root cause diagnosed, not guessed. Forms a slate of candidate causes, tests each against the data, and eliminates the ones the data refutes, narrowing the live candidates until exactly one survives refutation and passes a positive confirming test. The result is an investigation log with the confirmed root cause and the evidence that ruled out the alternatives. Not for open-ended discovery over a dataset with no specific anomaly in hand (that is data-a
npx skills add gaasher/Agent-Loop-Skills --skill anomaly-investigation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Anomaly Investigation Loop A **form → test → eliminate → confirm** loop — root-cause analysis as a search. The artifact is an investigation log; the feedback signal is the count of **live candidate explanations**, driven down toward a single cause that is **confirmed**, not merely consistent. Each iteration you test one candidate against the data and drop the ones the data refutes, narrowing the field until one survives. The discipline this enforces: a cause is "root" only when it both **survives an honest attempt to refute it** and makes a **positive prediction that checks out** (e.g. "if this is the cause, removing it restores normal" — and it does). A story that merely *could* explain the anomaly is a hypothesis, not a finding. ## When to use Use this when an anomaly is already in hand — you know roughly what looks wrong and want the cause diagnosed by elimination against the data. Default to a broad initial slate of mutually distinguishable causes, then test the one that splits the field fastest; if the anomaly is vague, your first job is to make it precise (iteration 0). Not for open-ended exploration of a dataset with no anomaly to chase (use data-analysis), and not for ver
- When to use
- Setup
- The loop
- Ledger
- Constraints
- Stops
What does the anomaly-investigation skill do?
Use when the user has a known, already-observed anomaly in their data — a metric spike or drop, an outlier, an unexpected number — and wants its root cause diagnosed, not guessed. Forms a slate of candidate causes, tests each against the data, and eliminates the ones the data refutes, narrowing the live candidates until exactly one survives refutation and passes a positive confirming test. The result is an investigation log with the confirmed root cause and the evidence that ruled out the alternatives. Not for open-ended discovery over a dataset with no specific anomaly in hand (that is data-a
How do I install it?
Run `npx skills add gaasher/Agent-Loop-Skills --skill anomaly-investigation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gaasher/Agent-Loop-Skills, a repository with 146 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.