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Found 14 Skills
Agent skill for performance-benchmarker - invoke with $agent-performance-benchmarker
Generate realistic KPI benchmarks for an influencer campaign before launch based on industry, platform, creator tier, and budget. This skill should be used when setting performance expectations for a creator campaign, estimating reach engagement and conversion benchmarks before launch, building KPI targets for an influencer program, forecasting campaign performance by creator tier and platform, setting EMV and ROAS targets for a campaign brief, defining what good looks like for an upcoming creator activation, calibrating expectations for a gifting or paid campaign across Instagram TikTok or YouTube, or creating a benchmark framework to measure campaign success against. For calculating ROI after a campaign ends, see campaign-roi-calculator. For calculating engagement rates from actual post data, see engagement-rate-calculator-benchmarker. For building a full KPI framework tied to business objectives, see campaign-goal-to-kpi-framework-builder.
Performance benchmarking expertise for shell tools, covering benchmark design, statistical analysis (min/max/mean/median/stddev), performance targets (<100ms, >90% hit rate), workspace generation, and comprehensive reporting
Build a complete KPI framework for a creator marketing campaign from a business objective. This skill should be used when setting KPIs for an influencer campaign, building a measurement plan before campaign launch, mapping business objectives to creator marketing metrics, defining primary and secondary KPIs for a creator program, creating a metrics framework for an awareness or conversion campaign, setting measurement benchmarks by creator tier, building a KPI dashboard structure for influencer reporting, or defining success criteria before activating creators. For calculating ROI after a campaign ends, see campaign-roi-calculator. For setting numeric benchmark targets, see performance-benchmark-setter. For tracking creator posting compliance, see creator-posting-compliance-tracker.
Calculate influencer campaign ROI and build a leadership-ready narrative summary from raw performance data. This skill should be used when calculating ROI for a creator campaign, building a campaign performance report for leadership, turning raw influencer metrics into an executive summary, computing CPM CPE ROAS and EMV for a creator program, summarizing campaign spend versus revenue for a stakeholder meeting, proving influencer marketing ROI to a CMO or VP, creating a campaign wrap report with financial metrics, or comparing influencer channel efficiency against paid social. For setting KPI targets before a campaign launches, see performance-benchmark-setter. For tracking creator posting compliance, see creator-posting-compliance-tracker. For full end-of-campaign reporting with qualitative analysis, see post-campaign-creator-scorecard. For building UTM links to enable attribution, see utm-parameter-builder.
Set up performance benchmarks and CodSpeed harness for a project. Use this skill whenever the user wants to create benchmarks, add performance tests, set up CodSpeed, configure codspeed.yml, integrate a benchmarking framework (criterion, divan, pytest-benchmark, vitest bench, go test -bench, google benchmark), or when the user says 'add benchmarks', 'set up perf tests', 'create a benchmark', 'benchmark this', or wants to measure performance of their code for the first time. Also trigger when the optimize skill needs benchmarks that don't exist yet.
Advanced test optimization with cargo-nextest, property testing, and performance benchmarking. Use when optimizing test execution speed, implementing property-based tests, or analyzing test performance.
Use when migrating from SwiftData to SQLiteData — decision guide, pattern equivalents, code examples, CloudKit sharing (SwiftData can't), performance benchmarks, gradual migration strategy
Analyzes and optimizes SQL queries using EXPLAIN plans, index recommendations, query rewrites, and performance benchmarking. Use for "query optimization", "slow queries", "database performance", or "EXPLAIN analysis".
Optimize code performance through iterative improvements (max 2 rounds). Benchmark execution time and memory usage, compare against baseline implementations, and generate detailed optimization reports. Supports C++, Python, Java, Rust, and other languages.
Decompose Return on Equity into component ratios to identify performance drivers. Use for financial analysis, performance benchmarking, and identifying improvement opportunities.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.