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 receive a structured, evidence-bounded OMH operating surface instead of ad hoc narration.
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_cardwith 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.
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
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.
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
Use the current host's own tools and subagent/task mechanism when available;
otherwise run the same lanes sequentially or name the unavailable capability.
A prepared plan, handoff, checklist, or skill installation is not execution,
review, CI, merge-readiness, or merge evidence. Report actual tool results or
not_observed / not_available; never invent dispatch or host accounting.
Treat supplied context as advisory, not proof of hidden memory reads or writes.
State scope, constraints, verification, and the stop condition before work.
Supporting paths are relative to this skill directory; sibling skill paths are
relative to its parent. Resolve them from the host-provided skill base directory
({baseDir} on hosts that provide it), never a hardcoded install location.
A named workflow not installed here is unavailable, not permission to emulate
its host-specific capabilities. Verify through the real surface before done.
