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/analyze-drivers

Use when seeking explanatory attributes or hypotheses for differences in process duration, waiting, utilization or rework.

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power-platform-skills
987107 skills24 agents5 MCP
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$ npx -y skills add microsoft/power-platform-skills --skill analyze-drivers --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/analyze-drivers

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when seeking explanatory attributes or hypotheses for differences in process duration, waiting, utilization or rework.

SKILL.md

analyze-drivers.SKILL.md
name: analyze-drivers
description: Use when seeking explanatory attributes or hypotheses for differences in process duration, waiting, utilization or rework.
allowed-tools: Read, AskUserQuestion

Drivers: association before explanation

Use the [shared contract](../../references/analysis-contract.md) and [filter/unit reference](../../references/filters-and-units.md). Reuse metadata and frozen baseline scope; choose a specific defined outcome and relevant case-level attributes from the user's context, without a candidate quota. If none is known, ask; do not sweep. State a testable hypothesis, possible competing explanation and what would distinguish them. Load the [investigation method](../../references/investigation-method.md) for evidence/status tracking. For utilization, first check timestamp imports and the zero-duration convention in the unit reference. A cohort-mix convention is not resource efficiency or measured productive time.

Ordered trace

1. Check A's metadata level before correlation. Reuse a cached signal instead of repeating it. `get_correlation_v2 {S,attributeName:A,influenceFormula:I,sortOrder:"Descending",filterOptions:F}`.

| Outcome | I | |---|---| | Duration / waiting / active time | DurationInfluence / WaitingTimeInfluence / ActiveTimeInfluence | | Utilization / event count | CaseUtilizationInfluence / EventCountInfluence | | Rework / loops / self-loops | ReworkCountInfluence / LoopCountInfluence / SelfloopCountInfluence |

Use the advertised enum and exact spelling. There are **no** `itemsPerPage`, `itemsToSkip`, `top`, `metrics` or `limit` arguments. The service ranks at most 20 groups. 2. Only if prevalence or mean evidence is needed and statistics is advertised: `get_attribute_statistics {S,attributeName:A,filterOptions:F,itemsPerPage:5,itemsToSkip:0,metricToSortBy:"CaseFrequency",sortOrder:"Descending"}`. Match returned values explicitly. Influence-ranked groups may differ from frequency-ranked groups; an absent match is unknown, not zero. 3. For a remaining hypothesis, use a discriminating matched comparison via compare-cohorts, focused statistics/cases, or another relevant candidate. Derive each test from frozen F, never an outcome-only exploratory subset. A cached signal can go directly to its test. Continue needed checks/pages without fixed analytical-call or candidate quotas. If matched evidence contradicts the proposed direction, mark it **contradicted** within that scope; do not privilege the earlier influence score or claim it proves a cause.

Non-case branch

An event-level Resource cannot be sent to correlation. If useful and advertised, use the statistics call above for that attribute, but describe grouped **event** duration/frequencies, not case-duration drivers. Otherwise ask for a case-level candidate or report the capability gap. Do not silently substitute a different causal analysis.

Report and stop

Influence is a prevalence-weighted descriptive score, **not Pearson correlation, significance, causal effect or confidence**. Its formula already scales by 100; do not multiply again. Return at most five useful signals with available counts/means, units, scope and partial coverage. Label explanations as hypotheses, note selection/confounding, and state one falsifiable evidence check. Record tested versus merely observed claims, alternatives and missing evidence; revise or stop when the next check cannot discriminate. No assumed outcome maturity or closure. Stop when no discriminating evidence remains; do not paginate correlations or invent sample sizes.

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