Agent skill · marketing growth · aiskillstore
performance-analyzer
Use when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion-attribution breakdowns, and ranked learnings. Not for dollar-level return math — use roi-calculator.
Why this skill is useful
Provides detailed performance analysis metrics and benchmarks for influencer campaigns that the AI wouldn't reliably generate on its own.
What it needs
About 5k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Performance Analyzer Analyze influencer campaign performance past surface metrics — score results vs target/benchmark, rank platforms/creators/content, read engagement quality and sentiment, attribute conversions, and write ranked learnings. Cross-discipline (paid ads): this is also the cross-channel paid-ads scorecard/anomaly lens — account-wide metric rollups vs target/benchmark that feed ad-test-designer (what to test) and paid-measurement-loop (what to read back). Save paid runs under memory/ad/performance-analyzer/. Quick Start Compare creators within one campaign: Skill Contract Reads: campaign name and date range; native platform analytics (reach, views, engagement); influencer-supplied reports or screenshots; website/GA traffic and conversion data; sales and promo-code redemption data; targets and benchmarks if the user has them; per-creator performance baselines from memory/creators/<handle-slug>.md (creator-registry roster records) when present. Writes: a performance analysis to memory/influencer/performance-analyzer/YYYY-MM-DD-<campaign>.md covering core-metric scorecards, platform/influencer/content rankings, engagement-quality and sentiment reads, conversion attribution, and ranked learnings. Promotes: durable facts (top-performing creators, winning formats, platform ROI splits, roster renew/drop calls) to memory/hot-cache.md. Done when: Core metrics are scored against target and benchmark with a performance verdict. Top and bottom performers are ranked with reasons, and content patterns that worked are named. Conversions are attributed by method (promo code / UTM / direct / estimated) and 3-5 learnings are written. Primary next skill: roi-calculator — turn measured performance into dollar-level return. Handoff Summary Emit the standard shape from skill-contract.md §Handoff Summary Format. Data Sources This family needs no live integrations (Tier 1). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/performance-analyzer