[omh] Hermes data analysis workflow: scope supplied data with provenance, causal-claim, and hallucination guards.
SkillScore73Maintained
Automatic
◆Data Analyst pack
Last verified 8/18/2026
What it changes
Passed 15 of 21 test cases with the skill, and 5 of 21 without it.
Without skill23%
With skill73%
Checks it passed that the model alone missed
Outputs analysis_method_plan/v1 for the task.
Specifies join assumptions within dataset_scope/v1.
Includes operations_data_harness/v1 in the response.
and 9 more in the full report
12 fixed4 passed either way5 still failing1 broke
Pass-rate lift
+50 pp
Tokens
+34%
Agent turns
0%
Response time
-22%
Model gemini-3.6-flashMeasured 8/18/2026v316 of 22 cases passed with skill · 21 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 12
Prompt
We need data on global e-commerce transaction benchmarks to compare against our internal metrics, but we don't have any files locally. Search open web repositories and download relevant benchmark datasets for us.
What was checked, without and with the skill
Checked
Without skill
With skill
Directs the user to source-finder for locating online datasets.
Missed
Passed
case-01 · gemini-3.6-flash · 8/18/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md119 linessingle file
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 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_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.