performance-analyzer Checked
Installation
Copy the prompt below and send it to your AI assistant (e.g. Claude Code) — it will follow the instructions and install automatically.
Install the "performance-analyzer" skill by following the instructions at https://skill123.me/install/performance-analyzer.
Run in your terminal — downloads and installs to ~/.claude/skills/.
curl -fsSL https://skill123.me/install/performance-analyzer.sh | bash
Download the zip and extract it into your skills directory (e.g. ~/.claude/skills/), then restart your session.
⬇ Download v1.0.0 · 12 KBNot live-tested yet — this skill is rated on static analysis only.
About this skill
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
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 (../../../ad/orchestrate/ad-test-designer/SKILL.md) (what to test) and paid-measurement-loop (../../../ad/scale/paid-measurement-loop/SKILL.md) (what to read back). Save paid runs under memory/ad/performance-analyzer/.
Documentation
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
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 (../../../ad/orchestrate/ad-test-designer/SKILL.md) (what to test) and paid-measurement-loop (../../../ad/scale/paid-measurement-loop/SKILL.md) (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 (../roi-calculator/SKILL.md). Reuse each explicit upstream opaquecreator_refor 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 frommemory/creators/<aggregate-id>.mdonly 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
measuredorclosed, 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 tomemory/influencer/performance-analyzer/YYYY-MM-DD-<campaign>.mdonly with exact WARM-save authorization; saved tables, headings, evidence, and handoffs usecreator_refplus opaque source refs, never raw handles, names, profile URLs, email addresses, or provider IDs. - **Promotes**: only with separate exact authorization, promote durable evidence-backed campaign facts (verified metric results and descriptive format/platform associations) to
memory/hot-cache.md; any ROI/ROAS value remains tied to its exact roi-calculator artifact. The Retro Card's qualitative `rene
View full documentation
w | 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 (../../../protocol/creator-registry/SKILL.md) alone decides whether it becomes canonical.
- **Done when**:
- Core metrics are compared against compatible source-dated targets/benchmarks. Missing or incompatible context is
Unknown/NOT_SCORED, never an invented/10score 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
measuredorclosedstate, 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 tounknown, while missing/forked state blocks the card. - **Primary next skill**: roi-calculator (../roi-calculator/SKILL.md) — turn measured performance into dollar-level return.
### Handoff Summary
> Emit the standard shape from skill-contract.md §Handoff Summary Format (../../../references/skill-contract.md).
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 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 (../../../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 (../../../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 (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.
1. **Gather performance data** — log campaign/period/influencers/platforms and the available sources (native analytics, influencer reports, web analytics, sales, promo codes). Template: step 1.
2. **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.
3. **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.
4. **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.
5. **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.
6. **Engagement quality analysis** — break engagement by type/intent, run evidenced comment sentiment, and surface purchase-intent sig
Score breakdown
Assessment methodology
Version history
Imported from aaron-he-zhu/aaron-marketing-skills@main