Data Analysis

This is an OMH data-analysis workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

data-analysis exists so Hermes users can ask for this workflow in chat and get a structured, checkable answer instead of an improvised one.

Do Not Use When

• The request is already handled by a narrower explicit skill with stronger evidence.
• The user asks OMH to secretly run external platforms, connectors, schedulers, file exports, or runtime agents.
• The only safe answer is to ask for missing authority, credentials, target, or observed evidence first.

Examples

Good example:

• Prompt: data-analysis analyze this CSV and summarize anomalies by segment.
• Expected behavior: Produce prepare_data_analysis_card with required context, wrapper actions, and not-evidence boundaries.
• Why: The prompt names a real workflow surface that Hermes can orchestrate without hiding execution.

Bad example:

• Prompt: data-analysis invent trends from an unavailable spreadsheet.
• Expected behavior: Report the missing observed evidence or authority instead of claiming the external step happened.
• Why: Prepared OMH guidance is not platform, runtime, connector, file, memory, or delivery evidence.

Completion Checklist

• Dataset or corpus source, record scope, schema or extraction method, join assumptions, analysis question, method, and stop condition are explicit.
• Numeric claims, anomalies, trends, segments, and log patterns are reported only from observed data or supplied evidence.
• Causal claims require observed identification evidence.
• Source acquisition, file conversion, report generation, and code fixes are routed to the narrower workflow when stronger.

Recovery Notes