evo-trend-anomaly-causal-inference
From the author
Complete pipeline for identifying anomalous product category spending patterns in e-commerce data using Prophet counterfactual forecasting, and explaining anomalies through demographic factors via Difference-in-Differences causal inference.
Install
npx skills add OpenLAIR/OpenSkill --skill evo-trend-anomaly-causal-inference -a claude-code
Installs to .claude/skills/evo-trend-anomaly-causal-inference/ · Each command fetches the skill straight from GitHub with the open skills CLI.
Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.
Want your agent to suggest skills while you type? See how to use skills from your IDE.
Safety checks on SKILL.md
- Ships scripts we have not reviewedLow
Why 75
aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.
Similar skills
Closest to this one by what they do and when they are used.
- metrics-anomaly-analysisnaodeng/awesome-qa-skillsUse this skill when you need to identify, contextualize, and investigate metric anomalies from observability evidence; triggers include 指标异常分析 and metrics anomaly analysis.DevOps★ 279
- discoversuboss87/FDEOpsTrace a customer workflow and identify the problem, baseline and evidence gaps. Use for discovery or an unclear customer brief, before choosing a solution.Productivity★ 940
- investigating-anomalous-resultsK-Dense-AI/science-superpowersUse when a result is surprising, impossible, contradicts a sanity check, a pipeline fails, a model won't converge, or a replication fails - before adjusting anythingFinance★ 362
- anomaly-investigationgaasher/Agent-Loop-SkillsUse 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-analysis), and not for checking an external claim against sources (that is claim-verify) — this is reactive diagnosis of one anomaly you already know about.Finance★ 177
- evo-taxonomy-data-pipelineOpenLAIR/OpenSkillLoad, normalize, and output e-commerce category taxonomy data using Pandas 2.2.0-safe patterns.Data★ 97
- diagnoseavibebuilder/claude-primeInvestigate unexpected behavior and mysterious bugs. Use when the cause of a problem is unknown and the user needs to understand WHY something is happening — symptoms like: sudden unexplained changes in metrics or behavior, works locally but not in staging/production, inconsistent or intermittent failures, correct code producing wrong results, operations succeeding but having no effect, environment-specific failures, duplicate executions, stale data, or any "why did this change?" or "why is this happening?" situation. Covers infrastructure anomalies (cache hit rates dropping, latency spikes, queue behavior shifts) as well as code bugs. The key signal is confusion about root cause, not a request to implement a known fix. Do NOT use for feature requests, known fixes, planning, or documentation tasks.DevOps★ 120
Ratings & reviews
—
no reviews
Tried this skill? Rate it to help others. Sign in
Discussion
Join the discussion. Sign in