# Review Agent Insights > Retrieve and synthesize AI agent analysis findings ranked by business impact. Uses mcp__Amplitude__get_agent_results, mcp__Amplitude__get_feedback_insights. - Skill: `kienbui1995/review-agent-insights` (Agent Skill) - Install (CLI): `npx skillmds@latest add kienbui1995/review-agent-insights` - Raw SKILL.md: https://api.skillmd.com/api/skills/kienbui1995/review-agent-insights/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: kienbui1995 (https://skillmd.com/u/kienbui1995) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/kienbui1995/review-agent-insights --- # Review Agent Insights ## When to Use - Weekly review of AI-generated analytics findings before team planning meetings - When Amplitude's AI agents have run analyses and you need to synthesize what they found - Before a product review meeting: "what does the data say this week?" - When multiple automated analyses have run in parallel and need to be consolidated - When a product manager or exec asks "what are our top data insights right now?" - Triaging alerts from Amplitude's AI-powered anomaly detection and forecasting features ## Core Jobs ### 1. Retrieve Recent Agent Analysis Results and Findings Pull all recent agent results from the target time window: ``` mcp__Amplitude__get_agent_results: time_range: last 7 days (or since last review) status: completed types: [anomaly_detection, forecast, correlation, funnel_analysis, retention_analysis] ``` Also retrieve user feedback insights that may have been automatically processed: ``` mcp__Amplitude__get_feedback_insights: time_range: last 7 days min_mentions: 3 (filter noise — only topics mentioned 3+ times) ``` **Expected finding types from Amplitude agents:** - **Anomaly alerts**: Metrics that deviated significantly from baseline (spikes, drops) - **Correlation findings**: Unexpected relationships between events or user segments - **Funnel insights**: Steps with unusually high drop-off, conversion changes - **Retention insights**: Cohorts with better or worse retention than expected - **Forecast deviations**: Actual metrics diverging from projected trends - **Segment insights**: Specific user segments behaving differently from the whole ### 2. Rank by Estimated Business Impact Not all findings are equal. Prioritize by impact on the metrics that matter most. **Impact estimation framework:** **Revenue impact** (highest priority): - Findings that affect paid conversion, upgrade rate, or churn rate - Anomalies in purchase funnel steps - Segments with significantly different LTV behavior **Engagement impact** (high priority): - Changes to activation rate or time-to-first-value - Retention curve changes in key user segments - Feature adoption that correlates with paid conversion **Operational impact** (medium priority): - Error rate changes that affect user experience - Performance degradations that increase session abandonment - Support volume drivers (features causing high escalation to support) **Informational** (lower priority): - Normal seasonal variations correctly identified as anomalies by the agent - Findings about low-traffic features with minimal user impact - Correlation findings that are interesting but not actionable **Scoring guidance:** ``` High impact: finding affects >10% of MAU or >5% of revenue metric Medium impact: affects 1-10% of MAU or 1-5% of revenue metric Low impact: affects <1% of MAU or <1% of revenue metric, or informational ``` ### 3. Identify Patterns Across Multiple Analyses Multiple agent analyses often surface related signals. Look for recurring themes: **Pattern types:** - **Same metric, multiple angles**: "retention dropping" appears in cohort analysis, funnel analysis, AND anomaly detection → strong signal, high confidence - **Same user segment**: multiple findings pointing to problems with mobile users, or enterprise users, or new signups - **Same feature**: multiple insights connecting to one product area (checkout, onboarding, search) - **Same time period**: multiple metrics changed at the same time → likely same root cause (deploy, external event, marketing campaign) **Pattern recognition example:** ``` Finding 1 (Funnel Analysis): Checkout completion rate dropped 8% this week Finding 2 (Anomaly Detection): "checkout_confirmation_viewed" event volume down 12% Finding 3 (Retention Analysis): Users who completed checkout last week have lower D7 return rate Finding 4 (Error Monitoring): POST /api/payments error rate up 23% this week Pattern: These 4 findings point to the same root cause — payment flow degradation This is a CRITICAL connected insight, not 4 separate findings. ``` ### 4. Flag Time-Sensitive Findings Some findings require immediate attention. Identify and escalate: **Criteria for time-sensitive flagging:** - **Rapidly worsening metrics**: Rate of change is increasing (acceleration, not just increase) - **New failure modes**: Something that wasn't happening before suddenly started (regression) - **Revenue-impacting anomalies**: Conversion or revenue metric is below trend by >5% - **Cascade risk**: One finding that, if not addressed, is likely to get worse (error rate climbing) **Time-sensitivity classification:** ``` URGENT (respond today): - Conversion metric down >10% vs. trend - New error pattern affecting >5% of users - Revenue-impacting regression identified SOON (address this sprint): - Metric trending wrong direction for 3+ consecutive days - New failure mode with moderate user impact WATCH (monitor, no immediate action): - Metric slightly below trend but stable - Interesting correlation that needs further investigation ``` ### 5. Package into Executive Summary Consolidate all findings into a concise, action-oriented brief: **Structure the summary for different audiences:** For **engineering**: lead with regressions, error rates, and technical root causes For **product**: lead with funnel changes, feature adoption, and user behavior shifts For **executive/leadership**: lead with revenue impact, user count affected, and trend direction **Summary template:** ``` ## Weekly AI Agent Insights — ### Executive Summary This week's agent analyses surfaced [N] significant findings. Top concern: [one sentence on highest-impact finding]. [X] findings require immediate action; [Y] to monitor. ### Action Items by Team ENGINEERING (respond this week): 1. [Finding + specific action + estimated impact] 2. ... PRODUCT (address in planning): 1. [Finding + specific action + estimated impact] 2. ... DESIGN (consider for next iteration): 1. [Finding + specific action + estimated impact] 2. ... ### Connected Insights (Patterns) [Pattern name]: [list of related findings that point to same root cause] Implication: [what this pattern means for the business] ### Findings That Need More Investigation [Findings that are interesting but not yet actionable — need more data or deeper analysis] ### Findings Closed (No Action Needed) [Findings that were investigated and explained — seasonal, expected, resolved] ``` ## MCP Tools - `mcp__Amplitude__get_agent_results` — retrieve completed AI agent analysis results from the target time window; provides the raw findings that this skill synthesizes - `mcp__Amplitude__get_feedback_insights` — retrieve processed feedback insights that may complement the agent results with qualitative signals from user feedback ## Key Concepts - **Agent result**: A completed analysis produced by Amplitude's AI agents — may include anomaly detection, correlation analysis, funnel insights, or retention analysis - **Connected insight**: Multiple separate agent findings that point to the same root cause — synthesizing these is the core value of this skill - **Time-sensitive finding**: A finding that, if not acted on promptly, will likely worsen — identified by rate of change, not just magnitude - **Impact ranking**: Prioritizing findings by their effect on revenue, engagement, and operational health — ensures the team focuses on what matters - **Pattern vs. noise**: Agent analyses can surface many findings; distinguishing meaningful patterns from expected variation is the key skill ## Output Format ``` ## AI Agent Insights Brief — Analyses reviewed: N agent results + N feedback insight reports New findings: N | Ongoing: N | Resolved: N ### URGENT — Respond Today ### HIGH PRIORITY — Address This Sprint ### Connected Insights Pattern: - Related findings: [list] - Root cause hypothesis: - Business impact: - Recommended owner: ### Weekly Trend Highlights - Improving: - Declining: - Stable: ### Team Action Items Engineering: Product: Design: ### Next Review Recommended: ```