gh-gtmagents-gtm-agents-plugins-personalization-engine/commands/monitor-personalization.md

name, description, usage

name	description	usage
monitor-personalization	Audits personalization performance, governance compliance, and experiment results.	/personalization-engine:monitor-personalization --initiative "PLG Onboarding" --window 14d --detail full

Command: monitor-personalization

Inputs

• initiative – personalization program or campaign to analyze.
• window – time frame (7d, 14d, 30d) for pulling metrics.
• detail – summary | full to control report depth.
• dimension – optional breakdown (profile, channel, cohort).
• alert_threshold – optional KPI threshold to trigger incident items.

GTM Agents Pattern & Plan Checklist

Mirrors GTM Agents orchestrator blueprint @puerto/plugins/orchestrator/README.md#112-325.

• Pattern selection: Monitoring usually runs pipeline (data aggregation → governance scan → experiment readout → issue detection → action plan). If governance + experiments review can run concurrently, capture a diamond block with merge gate in the plan header.
• Plan schema: Save .claude/plans/plan-<timestamp>.json capturing initiative, data feeds, dependency graph (data eng, privacy, experimentation), error handling, and success metrics (lift %, incident response time, consent adherence).
• Tool hooks: Reference docs/gtm-essentials.md stack—Serena for schema diffs, Context7 for governance/experiment SOPs, Sequential Thinking for retro cadence, Playwright for experience QA evidence.
• Guardrails: Default retry limit = 2 for failed data pulls or anomaly jobs; escalation ladder = Testing Lead → Personalization Architect → Data Privacy Lead.
• Review: Run docs/usage-guide.md#orchestration-best-practices-puerto-parity before distribution to ensure dependencies + approvals are logged.

Workflow

1. Data Aggregation – pull engagement, conversion, and revenue impact by profile/channel plus decision tree health signals.
2. Governance Scan – verify consent flags, fallback rates, and rule change logs for compliance.
3. Experiment Readout – summarize live/completed tests with statistical confidence and recommended actions.
4. Issue Detection – flag anomalies (data freshness, variant suppression, performance dips) and suggest playbooks.
5. Report Distribution – publish recap with dashboards, backlog items, and owners.

Outputs

• Performance dashboard snapshot segmented by profile/channel/variant.
• Governance checklist status with any violations or pending approvals.
• Experiment memo with next steps + rollout guidance.
• Plan JSON entry stored/updated in .claude/plans for audit trail.

Agent/Skill Invocations

• testing-lead – interprets experiments and recommends rollouts.
• personalization-architect – validates experience integrity.
• governance skill – enforces policy checks and approvals.

GTM Agents Safeguards

• Fallback agents: document substitutes (e.g., Governance covering Testing Lead) when leads unavailable.
• Escalation triggers: escalate if alert_threshold breached twice, consent violations appear, or anomaly alerts repeat; log remediation steps in plan JSON.
• Plan maintenance: update plan JSON/change log when metrics, thresholds, or monitoring cadences change to keep audits accurate.

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