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Found 2,256 Skills
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.
Parse raw text from an Instagram or TikTok Story insights screenshot and format it into a clean, spreadsheet-ready row with labeled fields. This skill should be used when parsing Story metrics from a screenshot, formatting Story insights for a spreadsheet, extracting metrics from a pasted Story screenshot, cleaning up Story analytics data, converting Story insights text into structured data, turning a Story performance screenshot into a row for the tracker, logging Story metrics into a spreadsheet, normalizing Story screenshot data, pulling numbers from a Story insights paste, organizing Story metrics from creator screenshots, processing a batch of Story screenshots into rows, building a Story metrics tracker from screenshots, or entering Story data from a screenshot into a sheet. For normalizing metrics from multiple sources into a unified table, see metrics-normalization-formatter. For calculating engagement rates and comparing to benchmarks, see engagement-rate-calculator-benchmarker.
Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, implementing real-time data processing, handling stateful operations, or optimizing streaming performance.
Comprehensive vitest testing patterns covering test structure, AAA pattern, parameterized tests, assertions, mocking, test doubles, error handling, async testing, and performance optimization. Use when writing, reviewing, or refactoring vitest tests, or when user mentions vitest, testing, TDD, test coverage, mocking, assertions, or test files (*.test.ts, *.spec.ts).
Review React/TypeScript code for bugs, security vulnerabilities, performance issues, accessibility gaps, and CLAUDE.md workflow compliance. Enforces TypeScript strict mode, GPU-accelerated animations, WCAG AA accessibility, bundle size limits, and surgical simplicity. Use when completing features, before commits, or reviewing pull requests.
Pay people in SOL or USDC, buy and sell tokens, check prices, manage Solana wallets, stake SOL, earn yield through lending, and track portfolio performance — all from the command line. No API keys, no private key env vars. Use when the user wants to send crypto, trade, check balances, earn yield, or see how their holdings are doing.
Comprehensive Kubernetes and OpenShift cluster management skill covering operations, troubleshooting, manifest generation, security, and GitOps. Use this skill when: (1) Cluster operations: upgrades, backups, node management, scaling, monitoring setup (2) Troubleshooting: pod failures, networking issues, storage problems, performance analysis (3) Creating manifests: Deployments, StatefulSets, Services, Ingress, NetworkPolicies, RBAC (4) Security: audits, Pod Security Standards, RBAC, secrets management, vulnerability scanning (5) GitOps: ArgoCD, Flux, Kustomize, Helm, CI/CD pipelines, progressive delivery (6) OpenShift-specific: SCCs, Routes, Operators, Builds, ImageStreams (7) Multi-cloud: AKS, EKS, GKE, ARO, ROSA operations
When the user wants to discover, evaluate, or prioritize App Store keywords. Also use when the user mentions "keyword research", "find keywords", "search volume", "keyword difficulty", "keyword ideas", or "what keywords should I target". For implementing keywords into metadata, see metadata-optimization. For auditing current keyword performance, see aso-audit.
Worker that checks DRY/KISS/YAGNI/architecture compliance with quantitative Code Quality Score. Validates architectural decisions via MCP Ref: (1) Optimality (2) Compliance (3) Performance. Reports issues with SEC-, PERF-, MNT-, ARCH-, BP-, OPT- prefixes.
Run after making Docyrus API changes to catch bugs, performance issues, and code quality problems. Use when implementing or modifying code that uses Docyrus collection hooks (.list, .get, .create, .update, .delete), direct RestApiClient calls, query payloads with filters/calculations/formulas/childQueries/pivots, or TanStack Query integration with Docyrus data sources. Triggers on tasks involving Docyrus API logic, data fetching, mutations, or query payload construction.
Use when managing Oracle Autonomous Database on OCI, troubleshooting performance issues, optimizing costs, or implementing HA/DR. Covers ADB-specific gotchas, cost traps, SQL_ID debugging workflows, auto-scaling behavior, and version differences (19c/21c/23ai/26ai).
Comprehensive system health scanner that checks security risks, performance metrics, and optimization opportunities. Works on Windows, macOS, and Linux.