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Found 149 Skills
Track and analyze content performance across Instagram, YouTube, LinkedIn, Twitter/X, and Reddit using anysite MCP server. Measure engagement metrics, analyze post effectiveness, benchmark content strategy, identify top-performing content, and optimize posting strategies. Supports post performance tracking, engagement analysis, content type comparison, and competitive benchmarking. Use when users need to measure content ROI, optimize social strategy, identify viral content patterns, or analyze content engagement across platforms.
Use this skill when profiling application performance, debugging memory leaks, optimizing latency, benchmarking code, or reducing resource consumption. Triggers on CPU profiling, memory profiling, flame graphs, garbage collection tuning, load testing, P99 latency, throughput optimization, bundle size reduction, and any task requiring performance analysis or optimization.
This is a skill for benchmarking the efficiency of automatic prefix caching in vLLM using fixed prompts, real-world datasets, or synthetic prefix/suffix patterns. Use when the user asks to benchmark prefix caching hit rate, caching efficiency, or repeated-prompt performance in vLLM.
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
Run cross-framework agent comparisons using evaluatorq from orqkit — compares any combination of agents (orq.ai, LangGraph, CrewAI, OpenAI Agents SDK, Vercel AI SDK) head-to-head on the same dataset with LLM-as-a-judge scoring. Use when comparing agents, benchmarking, or wanting side-by-side evaluation. Do NOT use when comparing only orq.ai configurations with no external agents (use run-experiment instead).
High-performance Rust optimization. Profiling, benchmarking, SIMD, memory optimization, and zero-copy techniques. Focuses on measurable improvements with evidence-based optimization.
Rust profiling skill for performance analysis. Use when generating flamegraphs from Rust binaries, measuring monomorphization bloat with cargo-llvm-lines, analysing binary size with cargo-bloat, microbenchmarking with Criterion, or interpreting inlined frames in profiles. Activates on queries about cargo flamegraph, cargo-bloat, cargo-llvm-lines, Criterion benchmarks, Rust performance profiling, or binary size analysis.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Decompose Return on Equity into component ratios to identify performance drivers. Use for financial analysis, performance benchmarking, and identifying improvement opportunities.
PlutoBa platform help — AI influencer vetting and creator due diligence across TikTok, Instagram, and YouTube. Covers PlutoBa Score (7-dimension assessment), Deep Assessments (100+ posts, 300+ comments), fake follower detection, audience authenticity, brand safety risk scoring, rate benchmarking, AI-powered creator outreach, creator CRM, and campaign briefs. Use when worried an influencer's followers are fake, need to check if a creator is brand-safe before signing a deal, want to know what to pay an influencer, PlutoBa Score seems too low or too high, creator outreach templates aren't getting responses, unsure which PlutoBa plan fits your needs, or setting up PlutoBa for an agency with multiple brands. Do NOT use for influencer strategy across platforms (use /sales-influencer-marketing) or influencer discovery and search (use /sales-hypeauditor or /sales-modash).
Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models