data-sleuth
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when
npx skills add majiayu000/claude-skill-registry --skill data-sleuth --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.
# Data Sleuth Advanced signal detection and correlation analysis for extracting non-obvious insights from datasets. ## Overview This skill transforms Claude into an investigative data analyst, applying techniques from data journalism, forensic accounting, and OSINT investigation to find patterns others miss. It pairs naturally with personality-profiler to enhance signal extraction from social media data, but works with any structured dataset. ## Core Principles ### The Investigative Mindset Adopt these cognitive stances from elite data journalists and investigators: 1. **Healthy Skepticism** — "There is no such thing as clean or dirty data, just data you don't understand." Challenge every assumption. 2. **Harm-Centered Pattern Recognition** — Study anomalies not as noise to remove, but as potential signals revealing system cracks. 3. **Naivete as Asset** — Remain naive enough to spot what domain experts miss due to habituation. 4. **Evidence Over Assumption** — Build confidence through evidence, never trust preconceived notions. ## Interview-First Workflow CRITICAL: Before any analysis, use `AskUserQuestion` to interview the user about potential analyses. Present proactively formul
- Overview
- Core Principles
- The Investigative Mindset
- Interview-First Workflow
- Step 1: Data Reconnaissance
- Step 2: Analysis Interview
- Step 3: Execute Selected Analysis
- Step 4: Present Findings with Evidence
- Signal Detection Techniques
- Quick Reference
- Multi-Dataset Correlation
- 1. Identify Common Keys
- 2. Cross-Reference Patterns
- 3. Document Correlations
What does the data-sleuth skill do?
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-sleuth --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 majiayu000/claude-skill-registry, a repository with 534 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.