history-analyze

name	history-analyze
description	Analyze actual local Copilot skill activations without reading conversation content.

History Analyze

Use /history-analyze to request this local metadata-only history report.

Resolve the filters before execution. Accepted windows are 7d, 30d, 90d, and all, either positionally or through --window/--since; accepted project scopes are current and all, either positionally or through --project. Expand omitted filters to the defaults 30d and all.

Run exactly one normalized command, in this stable option order:

omp history analyze --window WINDOW --project SCOPE --json

Replace WINDOW and SCOPE with the resolved values. Never run the shorthand command after normalization.

Counts come only from actual skill tool execution-start events; never read conversation content, prompt text, assistant text, or tool output, and never infer invocations from mentions. Preserve every warning from the command. Usage attribution is session-level-only: never describe token, duration, AI-credit, or model totals as per-skill values.

Usage totals cover skill sessions only. Present single-skill associations separately from the one shared-skill bucket; never split or copy shared telemetry. Do not inspect session files directly. Every numeric usage total includes metricSessions, the number of telemetry sessions that contributed that specific metric; do not substitute the broader sessionsWithTelemetry count.

Present the history

Require a successful schema-version-1 report before presentation. Present the history only after a successful schema-version-1 report. On schema or analyzer failure, stop without presenting history.

Do not print or paste raw JSON. Make the summary decision first: put the selected candidate from the top-level skills array first, before supporting detail. The skills array ranks arbitrary observed skills from metadata only; it does not imply a supported benchmark, a semantic task, or failure cause. Metadata-only history may rank observed skills or models, but must not claim semantic task or failure content. Then, within the available space:

• compact the exact normalized filter, coverage, and every present sessionUsage value with its matching metricSessions value;
• present the observed skill ranking compactly in returned order;
• summarize single-skill associations and the shared-skill bucket by exact counts, with only a small bounded sample rather than every row; and
• preserve every warning exactly as returned.

Before offering a handoff, use at most 12 compact bullets or lines and at most 2000 characters. Warnings may exceed the character bound only when necessary to preserve them verbatim; they still must not expose raw JSON. Omit lower-value detail rather than exceeding either bound. Copy numeric values exactly as returned. Do not round, abbreviate, rescale, or recalculate numeric values, and do not describe session-level usage as per-skill cost.

Offer the next step

Resolve the next-step placeholders from the report and filters. If a candidate exists, Offer /skill-bench SELECTED_SKILL for the selected arbitrary skill. Always also Offer /skill-bench WINDOW SCOPE for guided discovery using the same filters.

Do not ask a consent question here and do not start a benchmark or live run from this skill. On silence, empty answer, non-answer, refusal, ambiguity, analyzer failure, or unavailable handoff, stop without starting a live run and print the valid handoff commands.