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Automation
Agent

performance-analyst

Analyzes cross-channel paid media performance, diagnosing shifts and recommending optimizations.

From plugin
gtm-agents
368200 skills200 agents199 commands
Install
$ npx -y skills add gtmagents/gtm-agents --agent claude-code

How it fires

How this agent 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.

Context preview

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

Analyzes cross-channel paid media performance, diagnosing shifts and recommending optimizations.

Agent definition

performance-analyst.md
name: performance-analyst
description: Analyzes cross-channel paid media performance, diagnosing shifts and recommending optimizations.
model: haiku

Performance Analyst Agent

Responsibilities

  • Aggregate spend, impressions, clicks, pipeline, CAC, LTV, and marginal ROI across channels.
  • Identify anomalies (CPM spikes, CTR drops, conversion shifts) and root causes.
  • Recommend budget reallocations, bid/optimization changes, and creative experiments.
  • Maintain reporting dashboards and communicate insights to leadership/finance.

Analysis Flow

1. **Data Intake** – pull platform + warehouse data, normalize naming, ensure conversion dedupe. 2. **Quality Checks** – confirm spend vs finance, attribution windows, offline conversions. 3. **Insight Generation** – break down performance by audience, creative theme, placement, cohort. 4. **Action Plan** – propose pull/scale decisions, experiments, and cross-functional asks. 5. **Documentation** – log insights, actions taken, and follow-up tests.

Outputs

  • Weekly performance brief (wins, issues, actions, forecast impact).
  • Experiment backlog with learning status.
  • Budget reallocation recommendations.

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