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Agent skill

Performance Analyzer

by aiskillstore in aiskillstore/marketplace

A skill your agent uses when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark…

Apache-2.0Auto-check passedMarketing & SEO

Install Performance Analyzer

skills CLI
$ npx skills add aiskillstore/marketplace --skill performance-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aiskillstore/marketplace performance-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aiskillstore/marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/aaron-he-zhu/performance-analyzer .claude/skills/performance-analyzer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
performance-analyzer
GitHub stars
430
Token cost
~2.6k tokens
SKILL.md length
852 words
Files
3 (incl. references)
Skills in repo
1,085
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark…

  • Works in 8 steps: Gather performance data — log… → Analyze core metrics — score reach,… → Analyze by platform — compare platforms… → …
  • The user asks to analyze influencer campaign performance
  • SKILL.md covers Quick Start, Skill Contract, Data Sources and Instructions, plus 3 more sections
  • Calls python3; needs YOUTUBE_API_KEY

What it does

Performance Analyzer is an agent skill from aiskillstore/marketplace. 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.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/analysis-templates.md` and `skill-report.json`). Compatibility notes: Claude Code and compatible agent-skill hosts

It sits in Marketing & SEO, covering Influencer and creator marketing. The repository describes itself as: Security-audited skills for Claude, Codex & Claude Code. One-click install, quality verified. The licence is Apache-2.0.

When your agent uses it

  • The user asks to analyze influencer campaign performance
  • Compare influencers
  • Find what content worked
  • Produces metric scorecards vs target and benchmark

Example prompts

  • “analyze influencer campaign performance”
  • “compare influencers”
  • “find what content worked”
  • “/performance-analyzer”

Requirements

  • Python 3
  • A credential in YOUTUBE_API_KEY
  • Compatibility (from SKILL.md): Claude Code and compatible agent-skill hosts

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Gather performance data — log campaign/period/influencers/platforms and the available sources (native analytics, influencer reports, web…
  2. Analyze core metrics — score reach, impressions, engagements, ER, video views, clicks, promo uses, conversions, and revenue against target…
  3. Analyze by platform — compare platforms on reach/ER/clicks/conversions/CPA, name the best and worst with reasons, and break out…
  4. Analyze by influencer — rank creators on reach/ER/conversions/ROI, deep-dive top performers (why they won, content anatomy, renew call)…
  5. Content performance analysis — rank top content, compare formats and themes, and name the winning hook/messaging/visual patterns…
  6. Engagement quality analysis — break engagement by type and intent, run comment sentiment, surface purchase-intent signals, and score…
  7. Conversion & attribution analysis — draw the funnel, score conversion metrics vs benchmark, attribute by method (promo / UTM / direct /…
  8. Generate insights & recommendations — write the top-5 learnings, what worked / what didn't, optimization opportunities, roster renew/drop…

What it can do on your machine

Read from SKILL.md and the folder at commit 4ac52da. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python3

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • YOUTUBE_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Claude Code and compatible agent-skill hosts

    From compatibility in the SKILL.md frontmatter.

Context cost

Performance Analyzer loads about 2.6k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 852 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~96
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aiskillstore/marketplace at commit 4ac52da, republished under its Apache-2.0 licence (© aiskillstore). 852 words, ~2,581 tokens.

Download SKILL.mdSave it as .claude/skills/performance-analyzer/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
performance-analyzer
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.
compatibility
Claude Code and compatible agent-skill hosts
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
version
17.0.0
license
Apache-2.0
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…
argument-hint
<campaign name> [platform or influencer handles]
metadata.author
aaron-he-zhu
metadata.version
17.0.0

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.

  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 — 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.
  3. 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.
  4. 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.
  5. Content performance analysis — rank top content, compare formats and themes, and name the winning hook/messaging/visual patterns. Template: step 5.
  6. Engagement quality analysis — break engagement by type and intent, run comment sentiment, surface purchase-intent signals, and score quality /10. Template: step 6.
  7. 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.
  8. 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.
Show full SKILL.md (254 more words)Show less

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 C3 analysis (ACE/ART scope scores) from c3/scoring-architecture.md, and hand the measured inputs to roi-calculator for the ROI score and CVI rollup — this skill contributes the inputs but does not compute the rollup.

Example

User: "Analyze performance of our summer skincare campaign with 10 influencers"

Output (abridged — full version in references/analysis-templates.md):

markdown
# 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

Next Best Skill

Primary: roi-calculator — convert measured performance into dollar-level ROI, cost-per-result, and payback math.

Alternates (same Measure 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.

© aiskillstore, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/aaron-he-zhu/performance-analyzer of aiskillstore/marketplace.

  • SKILL.md
  • references/analysis-templates.md
  • skill-report.json

Open the folder on GitHubat commit 4ac52da

Compare with similar skills

Performance Analyzer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Performance Analyzer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Performance Analyzer this skillaiskillstore/marketplace430—~2.6kAutomated safety check: PassApache-2.0
Influencer Discoverytigerless-labs/influencer-discovery211—~2.5kAutomated safety check: NotesNone
Opencloneteam-attention/openclone130—~2.6kAutomated safety check: NotesMIT
Reelclaw Adsdansugc/reelclaw145—~3.9kAutomated safety check: NotesMIT
Audience ResearchScrapeCreators/social-media-research-skills3.3k—~635Automated safety check: NotesMIT
Affiliate CheckAffitor/affiliate-skills698—~808Automated safety check: NotesMIT

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Categories

Questions about Performance Analyzer

What does Performance Analyzer do?

A skill your agent uses when the user asks to "analyze influencer campaign performance", "compare influencers", or "find what content worked"; produces metric scorecards vs target and benchmark…. Performance Analyzer is an agent skill from aiskillstore/marketplace. 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.

When should I use Performance Analyzer?

Performance Analyzer fits situations like: the user asks to analyze influencer campaign performance; compare influencers; find what content worked; produces metric scorecards vs target and benchmark.

How do I install Performance Analyzer in Claude Code?

Run `npx skills add aiskillstore/marketplace --skill performance-analyzer -a claude-code`. Or copy the skill folder (skills/aaron-he-zhu/performance-analyzer in aiskillstore/marketplace) into .claude/skills/performance-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Performance Analyzer in Codex?

Run `npx skills add aiskillstore/marketplace --skill performance-analyzer -a codex`. Or copy the skill folder (skills/aaron-he-zhu/performance-analyzer in aiskillstore/marketplace) into .agents/skills/performance-analyzer in your project. Codex loads it when a task matches its description.

Can I use Performance Analyzer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aiskillstore/marketplace --skill performance-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/performance-analyzer, .gemini/skills/performance-analyzer, .github/skills/performance-analyzer and .opencode/skills/performance-analyzer in your project.

What does Performance Analyzer need to run?

Going by SKILL.md and its folder, Performance Analyzer needs the command-line tools its instructions call (python3) and credentials named YOUTUBE_API_KEY. Our summary lists: Python 3; A credential in YOUTUBE_API_KEY. Compatibility (from SKILL.md): Claude Code and compatible agent-skill hosts.

Does Performance Analyzer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Performance Analyzer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Performance Analyzer use?

Performance Analyzer is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Performance Analyzer use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to Performance Analyzer?

Skills that share tags, products or a category with Performance Analyzer: Influencer Discovery (tigerless-labs/influencer-discovery, 211 stars), Openclone (team-attention/openclone, 130 stars), Reelclaw Ads (dansugc/reelclaw, 145 stars) and Audience Research (ScrapeCreators/social-media-research-skills, 3.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Performance Analyzer?

aiskillstore (a GitHub organization) maintains it in aiskillstore/marketplace, which has 430 GitHub stars. The repository holds 1,085 skills in this directory. The repository was last updated on October 7, 2026.

Source: aiskillstore/marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.