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
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
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 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.
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, benchmarks, and the preregistered decision rule/readback window if supplied; the optional lightweight campaign tracker and its 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.
Writes: return the performance analysis inline by default. When a current non-forked tracker-state artifact proves 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.
: only with separate exact authorization, promote durable evidence-backed campaign facts (verified metric results and descriptive format/platform associations) to ; any ROI/ROAS value remains tied to its exact roi-calculator artifact. The Retro Card's qualitative 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 , , or to be proposed by the owning workflow; alone decides whether it becomes canonical.
Core metrics are compared against compatible source-dated targets/benchmarks. Missing or incompatible context is Unknown/NOT_SCORED, never an invented /10 score or adjective verdict.
Creators/platforms/content are ranked only under a declared metric, compatible window/basis, complete candidate set, and preregistered decision rule; descriptive associations and causal hypotheses stay visibly separate.
Conversions use one declared attribution model with deduplicated, mutually exclusive counted buckets; overlapping promo/UTM/direct observations remain reconciliation evidence, and modeled influence stays Estimated outside the counted total.
With verified current 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.
Primary next skill: roi-calculator — turn measured performance into dollar-level return.
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.
~~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 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.
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 — compare reach, impressions, engagements, ER, video views, clicks, promo uses, conversions, and revenue against compatible source-dated targets/benchmarks. Emit field-level comparison states; do not invent an aggregate score or adjective verdict. Template: step 2.
Analyze by platform — compare platforms on compatible reach/ER/click/conversion/CPA windows and state observed differences. Put any explanation in a separately labeled hypothesis unless a designed comparison supports it. Template: step 3.
Analyze by creator — use opaque 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.
Content performance analysis — compare formats/themes under compatible exposure and attribution bases. Name observed higher/lower associations; describe a hook/message/visual as causal or "winning" only when the supplied design clears the measurement protocol. Template: step 5.
Engagement quality analysis — break engagement by type/intent, run evidenced comment sentiment, and surface purchase-intent signals. Use typed observations or Unknown; emit no /10 quality score without a supplied rubric, inputs, and calculation. Template: step 6.
Conversion & attribution analysis — draw the observed funnel and use one declared attribution model. Deduplicate events into mutually exclusive counted buckets; preserve promo/UTM/direct overlap as reconciliation evidence, and report Estimated influence outside the counted total. Template: step 7.
Generate insights & recommendations — write 3–5 evidence-backed observations, separately labeled hypotheses, and bounded next tests. Add one compact Campaign Retro Card per creator decision requested only when a verified current, non-forked tracker-state artifact proves that exact campaign/creator is 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.
Example
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."
# 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.
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.
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.
