| name | performance-analyzer |
| slug | performance-analyzer |
| displayName | Performance Analyzer · 效果分析 |
| summary | 活动效果分析:达成 vs 目标、平台与创作者维度拆解、优化建议 |
| description | 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. |
| version | 17.0.0 |
| license | Apache-2.0 |
| compatibility | Claude Code and compatible agent-skill hosts |
| homepage | https://github.com/aaron-he-zhu/aaron-marketing-skills |
| when_to_use | Use mid-flight or post-campaign when a user wants to evaluate influencer results, compare creators against each other, find top-performing content or formats, judge engagement quality and comment sentiment, connect influencer activity to conversions, or build performance benchmarks for future planning. |
| argument-hint | <campaign name> [platform or influencer handles] |
| metadata | {"author":"aaron-he-zhu","version":"17.0.0","discipline":"influencer","phase":"measure","family":"influencer-marketing","hermes":{"tags":["marketing","influencer","measure"],"category":"influencer"},"openclaw":{"emoji":"📣","homepage":"https://github.com/aaron-he-zhu/aaron-marketing-skills"}} |
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
Analyze performance of [campaign name] influencer campaign
Compare creators within one campaign:
Compare performance of these influencers from [campaign]: @handle1, @handle2, @handle3
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
