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Analyzer

ClineOfficialPublished by Cline

  • Skill
  • Research and analysis
  • Data and AI
  • Apache-2.0
  • No issues found
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From the author

Analyze queried data for trends, week-over-week comparisons, distributions, funnels, cohorts, top-N lists, anomalies, sanity checks, and report-ready findings. Use after or alongside ClickHouse queries when the user wants insight rather than raw rows.
— the description by Cline

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

Analyzer is a structured framework for interpreting data results. Instead of simply presenting raw rows, it guides the user to apply specific analytical patterns—such as trends, cohorts, funnels, and distributions—to extract meaningful conclusions. It emphasizes rigorous verification, including sanity checks for data quality and freshness, and mandates a standardized reporting format to ensure findings are defensible, caveated, and actionable.

What it does

  • Applies analytical patterns like trend analysis, cohort grouping, and funnel conversion tracking.
  • Performs sanity checks on data quality, including null rates, duplicates, and freshness.
  • Identifies anomalies and top-N contributors within datasets.
  • Structures findings into a consistent format including evidence, confidence levels, and caveats.
  • Provides a checklist for verifying data validity before drawing conclusions.

When to use it

  • When you have raw data from a query and need to extract business or technical insights.
  • When performing week-over-week comparisons or release-to-baseline evaluations.
  • When you need to validate data integrity before presenting it as a fact.
  • When you need to communicate complex data findings to stakeholders in a structured, defensible way.

Advantages

  • Reduces the risk of misinterpreting raw data by enforcing sanity checks.
  • Standardizes the output format for consistent reporting.
  • Encourages critical thinking by requiring confidence levels and explicit caveats.
  • Helps identify potential data quality issues like telemetry gaps or version coverage.

Usage examples

“Analyze the last 30 days of user login data for trends and anomalies.”
The agent will process the login metrics, identify any significant spikes or drops, check for data freshness issues, and present a structured finding with evidence and confidence levels.
“Compare conversion rates between the new and old checkout flow.”
The agent will apply a funnel analysis pattern to calculate drop-offs at each step, compare the two versions, and report the results with necessary caveats regarding sample size.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/cline/analyzer/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip analyzer.zip -d .agents/skills

Documentation by Cline

Analyzer

Turn data into defensible findings instead of only returning rows.

Analysis patterns

Choose the smallest pattern that answers the question:

  • Trend: metric over time at the right grain.

  • Comparison: current period vs prior period, release vs baseline, or segment A vs B.

  • Distribution: percentiles, skew, tails, and outliers.

  • Funnel: step counts, conversion rates, and drop-offs.

  • Cohort: behavior grouped by start date, version, source, or first action.

  • Top-N: largest contributors with share of total.

  • Sanity check: row counts, null rates, first/last seen, duplicates, and data freshness.

Before concluding

  • Verify the time window and grain match the user's question.

  • Check sample size, nulls, and whether the metric is dominated by a small tail.

  • Look for freshness, rollout, telemetry opt-in, or version-coverage issues.

  • Avoid causal language unless the query design supports causality.

  • If the result is surprising, run or propose one validation query before presenting it as fact.

Finding format

Finding: ...
Evidence: ...
Confidence: High/Medium/Low because ...
Caveats: ...
Recommended next check: ...

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Cline
Publisher
Cline (Official)
Source
cline/skills · skills/data-analyst/skills/analyzer · commit 2637846
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Cline or Cline.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download analyzer.zip