| 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 | 18.0.0 |
| 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":"18.0.0","discipline":"influencer","phase":"report","geo-relevance":"low","hermes":{"tags":["marketing","influencer","report"],"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
This family needs no live integrations (Tier 1). The skill runs entirely on inputs you provide — paste platform exports, influencer report screenshots, GA numbers, and promo-code redemption counts, and it builds the full analysis. Ask the user for whatever is missing rather than blocking.
Where a connector could speed the work, the skill marks it with a ~~ placeholder:
~~social platform analytics — native reach/engagement/video metrics per post.~~web analytics — site traffic, click-through, and on-site conversion data.
Measured YouTube post-performance (free key): when campaign content lives on YouTube, python3 "${CLAUDE_PLUGIN_ROOT}/scripts/connectors/youtube.py" videos @creator --limit 20 pulls the actual per-video views/likes/comments for the campaign window — Measured platform metrics without waiting for the creator's screenshot export. Keep both labels honest: API numbers are Measured, creator-supplied numbers are User-provided, and the two can legitimately disagree (display rounding, timing). Free YOUTUBE_API_KEY. See scripts/connectors/README.md.
~~ecommerce / sales platform — revenue, orders, AOV, promo-code redemptions.~~influencer database — historical creator benchmarks for comparison.
No placeholder is required to run. See CONNECTORS.md for the verified free/keyless data recipe per category.
Instructions
Work the steps in order. Each fill-in template lives in references/analysis-templates.md — copy the matching block and populate it.
- Gather performance data — log campaign/period/influencers/platforms and the available sources (native analytics, influencer reports, web analytics, sales, promo codes). Template: step 1.
- Analyze core metrics — score reach, impressions, engagements, ER, video views, clicks, promo uses, conversions, and revenue against target and benchmark; assign a performance verdict and call out over/underperformers. Template: step 2.
- Analyze by platform — compare platforms on reach/ER/clicks/conversions/CPA, name the best and worst with reasons, and break out platform-specific formats (IG feed/Reels/Stories, TikTok watch time/completion). Template: step 3.
- Analyze by influencer — rank creators on reach/ER/conversions/ROI, deep-dive top performers (why they won, content anatomy, renew call), and explain underperformers. Template: step 4.
- Content performance analysis — rank top content, compare formats and themes, and name the winning hook/messaging/visual patterns. Template: step 5.
- Engagement quality analysis — break engagement by type and intent, run comment sentiment, surface purchase-intent signals, and score quality /10. Template: step 6.
- Conversion & attribution analysis — draw the funnel, score conversion metrics vs benchmark, attribute by method (promo / UTM / direct / estimated), and table promo-code performance. Template: step 7.
- Generate insights & recommendations — write the top-5 learnings, what worked / what didn't, optimization opportunities, roster renew/drop calls, and future-campaign guidance. Template: step 8.
Before naming any creator/format/platform a real winner, clear the significance bar in measurement-protocol.md — otherwise mark it Keep-testing. When a structured score is needed, apply per-dimension STAR analysis (Suitability/Trust/Appeal/Return dimension reads) from star-benchmark.md, and hand the measured inputs to roi-calculator for the measured Return (R) evidence — this skill contributes the inputs but does not compute the SQS (the creator-content-auditor gate does).
Example
User: "Analyze performance of our summer skincare campaign with 10 influencers"
Output (abridged — full version in references/analysis-templates.md):
# Summer Skincare Campaign Performance Analysis — Above Average (7.5/10)
| Metric | Result | Target | Status |
|--------|--------|--------|--------|
| Total Reach | 2.4M | 2M | ✅ +20% |
| Engagement Rate | 4.2% | 3.5% | ✅ +20% |
| Conversions | 1,847 | 2,000 | ⚠️ -8% |
| Revenue | $142,500 | $150,000 | ⚠️ -5% |
| ROI | 2.8:1 | 3:1 | ⚠️ -7% |
**Top 3**: @skincaresarah (ROI 4.2:1), @glowwithgrace (ER 6.8%), @beautyreview (reach/$).
**Key learning**: TikTok beat Instagram (3.5:1 vs 2.1:1 ROI) — shift 20% of IG budget to TikTok.
**Recommendation**: Renew top 5; replace bottom 2 with TikTok-native creators.
Reference Materials
- references/analysis-templates.md — the eight fill-in step templates plus the full worked example.
- skill-contract.md — shared contract and handoff format.
- state-model.md — memory tiers and save-path conventions.
- CONNECTORS.md — verified free/keyless data recipes per connector category.
- measurement-protocol.md — preregistered readback windows, outcome unit, alpha, practical-effect boundary, multiplicity/sequential policy, guardrails, and decision owner. Report statistical and practical flags separately; use
experiment.py for deterministic Calculated evidence, and never substitute a universal p-value/lift rule or attribute a business action to the helper.
- The STAR benchmark at references/star-benchmark.md — scoring architecture when a structured score is needed.
- Sibling skills: roi-calculator, report-generator, fit-scorer, campaign-planner.
Next Best Skill
Primary: roi-calculator — convert measured performance into dollar-level ROI, cost-per-result, and payback math.
Alternates (same Report family):
- report-generator — package the analysis into a formal stakeholder report.
- fit-scorer — feed proven performers back into creator scoring for the next round.
Termination note: Maintain a visited-set. If a skill has already been invoked this session, stop and report chain-complete rather than re-running it. Cap the chain at max-depth 3 hops; if results are inconclusive after that, surface the open loops to the user instead of continuing.
