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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/conte
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add aaron-he-zhu/aaron-marketing-skills --skill performance-analyzer -a cursor -y
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. 达人营销效果分析/投放复盘
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. 达人营销效果分析/投放复盘
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/.
Analyze performance of [campaign name] influencer campaign
Compare creators within one campaign:
Compare performance of these influencers from [campaign]: @handle1, @handle2, @handle3
evidence_refs; and any ROI/ROAS artifact already computed by roi-calculator. Reuse each explicit upstream opaque creator_ref or a verified creator-registry aggregate ID; a raw handle/name/URL/provider ID is transient lookup input only and never becomes a saved identity. Per-creator baselines come from memory/creators/<aggregate-id>.md only when an authorized artifact or verified registry link resolves that ref. Never derive the path from a raw locator.measured or closed, include the compact Campaign Retro Card from step 8 bound to that campaign, creator, measurement contract, and decision rule. Save the analysis and card together to memory/influencer/performance-analyzer/YYYY-MM-DD-<campaign>.md only with exact WARM-save authorization; saved tables, headings, evidence, and handoffs use creator_ref plus opaque source refs, never raw handles, names, profile URLs, email addresses, or provider IDs.memory/hot-cache.md; any ROI/ROAS value remains tied to its exact roi-calculator artifact. The Retro Card's qualitative renew | retest | retire | unknown decision, rationale, next hypothesis, and limitations remain WARM and are never promoted as registry truth. This skill makes no creator-registry proposal: after a creator row is closed, the existing boundary still permits only a separately authorized, evidence-backed actual rate, signed rights window/expiry, or measured performance baseline to be proposed by the owning workflow; creator-registry alone decides whether it becomes canonical.Unknown/NOT_SCORED, never an invented /10 score or adjective verdict.measured or closed state, each requested next-cycle decision has a scope-bound Campaign Retro Card with campaign/creator/state/measurement/decision-rule refs, evidence-backed rationale, evidence_refs, next-campaign hypothesis, and unresolved limitations; insufficient decision evidence resolves to unknown, while missing/forked state blocks the card.Emit the standard shape from skill-contract.md §Handoff Summary Format.
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 analyzes the supported fields. Missing inputs do not block a partial descriptive read, but any dependent score, verdict, rank, causal explanation, attribution total, or decision becomes Unknown/NOT_SCORED/NEEDS_INPUT rather than being filled in.
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.
Work the steps below as one dependency-aware pass. Each fill-in template lives in references/analysis-templates.md. Build the Step 2 shell after intake, but run Step 7 before populating or publishing Step 2 Conversions, Revenue, or any rate/cost that depends on them; those fields must cite Step 7's reconciled counted total or remain Unknown/NEEDS_INPUT.
creator_ref; rank only comparable rows under the declared rule. Consume ROI/ROAS only from a cited roi-calculator artifact, do not compute it here, and separate observed content anatomy from causal hypotheses. A renew/retest/retire call comes only from the Retro decision gate. Template: step 4.Unknown; emit no /10 quality score without a supplied rubric, inputs, and calculation. Template: step 6.measured or closed and the matching measurement-contract and decision-rule refs are supplied; a bare stage string never qualifies. Use only renew | retest | retire | unknown. Template: step 8.Before naming any creator/format/platform a real winner, clear the comparability, complete-scope, preregistered-rule, and significance bars in measurement-protocol.md — otherwise mark it Keep-testing or NOT_RANKED. When a structured score is needed, apply per-dimension STAR analysis (Suitability/Trust/Appeal/Return dimension reads) from star-benchmark.md, and hand financial inputs to roi-calculator for Return (R) math — this skill contributes inputs but does not compute ROI/ROAS or SQS (the creator-content-auditor gate computes SQS).
For the Retro Card, use renew only when comparable measured evidence clears the preregistered decision rule without a material unresolved limitation; use retest for a plausible but inconclusive or correctable test; use retire only when measured evidence or a documented hard constraint clears the declared stop rule; otherwise use unknown. This operating decision is not a STAR dimension, SQS, or creator-content-auditor verdict—do not simulate or carry forward one.
After an authorized WARM save, offer a handoff to campaign-planner to append the saved analysis/card reference to the relevant tracker row's evidence_refs; the tracker edit needs its own exact authorization, and neither the card nor this skill advances stage. Also offer fit-scorer as an explicit next-cycle handoff with the card's evidence references and hypothesis. Do not invoke it automatically, and do not translate the Retro decision into a STAR/SQS verdict.
User: "Analyze this dated summer-skincare export for 10 creators. It contains opaque creator refs, the metric/target table below, per-creator and per-platform results, one deduplicated attribution model, and a completed significance read. ROI comes from roi-calculator artifact roi-ref-01."
Output (abridged — full version in references/analysis-templates.md):
# Summer Skincare Campaign Performance Analysis — illustrative export-backed read
| 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% |
| ROAS (from `roi-ref-01`) | 2.8:1 | 3:1 | ⚠️ -7% |
**Top 3**: the three `creator_ref` rows that clear the declared ranking and significance rule, using only comparable metrics in the export.
**Key learning**: report the export-backed TikTok/Instagram delta only if the comparison windows and attribution bases match; otherwise mark it Keep-testing.
**Recommendation**: renew/drop and reallocation calls remain conditional on the predeclared decision rule rather than invented from the campaign count alone.
experiment.py for deterministic Calculated evidence, and never substitute a universal p-value/lift rule or attribute a business action to the helper.Primary: roi-calculator — convert measured performance into dollar-level ROI, cost-per-result, and payback math.
Alternates (same Report family):
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.