/omh-data-analysis

name: "omh-data-analysis"
description: "[omh] Dataset or table to analyze: scope supplied data with provenance, causal-claim, and hallucination guards. Use when the user says: data-analysis, data analysis, dataset analysis, csv analysis, json analysis, log analysis, table analysis, analyze csv."
metadata:
  hermes:
    tags: [workflow, oh-my-hermes, analysis]
    category: analysis
    phase: data-task
    role: guide
    quality_tier: workflow-surface-gated

Data Analysis

This is a Hermes-native `data-analysis` workflow skill.

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

• If the data itself is missing, ask for the smallest dataset sample, schema, or query output needed.
• If the user wants datasets found online, route to source-finder before analysis.
• If the user wants a PPT/PDF/XLSX report generated from data, route to materials-package or deliverable-package after analysis scope is clear.

Workflow Lane

• Current lane: **Research and company ops** (`product-docs`, `source-finder`, `web-research`, `research`, `model-optimization`, `inference-serving`, `model-finetuning`, `research-brief`, `+20 more`) - research, signals, ops, and briefings.
• If intent belongs to another lane, hand back to `oh-my-hermes` or name the adjacent workflow.
• Shared product, routing, compatibility, and evidence rules: `omh-routing/references/skill-common-rail.md`.

Use When

Use when Hermes should prepare supplied structured, unstructured, or mixed data analysis without unsupported numeric or causal claims.

Strong routing signals: `data-analysis`, `data analysis`, `dataset analysis`, `csv analysis`, `json analysis`, `log analysis`, `table analysis`, `analyze csv`, `analyze this csv`, `analyze json`, `analyze logs`, `summarize anomalies`, `anomaly analysis`, `trend analysis`, `segment analysis`, `column analysis`, `schema check`, `table to chart`, `chart with an executive summary`, `spreadsheet delta analysis`, `cohort analysis`, `retention analysis`, `correlation analysis`, `causal analysis`, `causality check`, `데이터 분석`, `csv 분석`, `json 분석`, `로그 분석`, `이상치 분석`, `추세 분석`, `오류 패턴`, `컬럼 분석`, `전환율 델타`, `차트 요약`, `상관관계 분석`, `인과 분석`, `인과관계`

Catalog Metadata

Category: `analysis` Phase: `data-task` Hermes role: `guide` Quality tier: `workflow-surface-gated` Reasoning demand: `standard`

Quality bar:

• Name the user-facing workflow objective, required context, next action, and stop condition.
• Separate prepared guidance from observed platform, runtime, connector, file, memory, or delivery evidence.
• Expose missing tools, credentials, targets, or observations as user-visible gaps.

Handoff policy:

Keep this as Hermes-facing orchestration guidance first. Prepare executor, connector, gateway, or host-runtime handoff only when the user accepts that next step and observed evidence can be recorded.

Required inputs:

• user request
• target context
• delivery or status expectation
• known missing evidence

Expected outputs:

• data_analysis_task_card/v1
• dataset_scope/v1
• analysis_method_plan/v1
• operations_data_harness/v1
• product_evidence_loop/v1
• analysis_result_summary/v1 when observed
• next action
• prepared-vs-observed boundary

Artifact expectations:

• data_analysis_task_card/v1 metadata-only wrapper card when prepared
• dataset_scope/v1 with source, row/record scope, columns or schema, filters, and stop condition
• analysis_method_plan/v1 naming summary, anomaly, trend, segment, schema, or log-pattern methods
• operations_data_harness/v1 for relationship and causal boundaries
• product_evidence_loop/v1 for prepared opaque data reference metadata
• analysis_result_summary/v1 only from observed data, calculations, query output, or supplied evidence

Safety rules:

• A data analysis card is not file extraction, query execution, chart generation, statistical proof, data correctness, hallucination-safe numeric evidence, association, or causality unless observed data and method evidence records it.
• Do not claim connector, gateway, runtime, file generation, memory mutation, or host automation evidence from prepared guidance.

Runtime Evidence

Preferred harness for this skill: `data-analysis`.

omh runtime record --skill data-analysis --harness data-analysis --status started

Record observed delegation results; otherwise return `not_available` or `not_observed`. Prepared OMH routing is not execution, review, CI, me