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Found 106 Skills
Arrfounder platform help — founder revenue directory by @Folyd (2024) that auto-extracts MRR/ARR + products from Twitter/X bios via AI, lists 1000+ founders on sortable leaderboards (ARR / followers / products / recently added), free Airtable submission with 24-48h manual approval, auto-syncs within hours of bio changes. Social-proof verification only (no Stripe / Lemon Squeezy / Polar API integration) — built for peer discovery and community browsing, not acquisition-grade proof. Use when getting listed on Arrfounder, writing a Twitter/X bio that passes the MRR/ARR extractor, fixing a profile that didn't get approved or stopped updating after a bio edit, deciding Arrfounder vs TrustMRR or StartuPage for verified-revenue display, benchmarking against peers in the $1K-$10M+ ARR tiers, or using Arrfounder as a comp-check tool before pricing a sale or fundraise. Do NOT use for selling/buying a project or cross-marketplace valuation (use /sales-side-project-valuation).
Social media campaign analysis and performance tracking. Calculates engagement rates, ROI, and benchmarks across platforms. Use for analyzing social media performance, calculating engagement rate, measuring campaign ROI, comparing platform metrics, or benchmarking against industry standards.
Score each creator on a completed campaign across consistency, content quality, engagement rate, and brand alignment, then produce a ranked retention list for future campaigns. This skill should be used when grading creators after a campaign ends, evaluating influencer performance post-campaign, ranking creators by campaign performance, building a retention list of top creators, deciding which creators to rebook for the next campaign, scoring influencer deliverables after a launch, comparing creator performance across a campaign roster, auditing which creators delivered the most value, or tiering creators into re-engage versus one-and-done lists. For calculating engagement rates and benchmarking them by tier, see engagement-rate-calculator-benchmarker. For scoring niche fit before a campaign, see niche-fit-scorer. For building the full campaign report with ROI narrative, see campaign-roi-calculator-narrative-builder.
Multi-path parallel product analysis with cross-model test-time compute scaling. Spawns parallel agents (Claude Code agent teams + Codex CLI) to explore product from multiple perspectives, then synthesizes findings into actionable optimization plans. Can invoke competitors-analysis for competitive benchmarking. Use when "product audit", "self-review", "发布前审查", "产品分析", "analyze our product", "UX audit", or "信息架构审计".
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.
Challenge an outbound campaign copy by benchmarking it against the user's existing campaigns — what worked, what didn't, what the winners do differently — and return a concrete verdict plus prioritized fixes. Use whenever the user wants to know if a campaign or sequence is good, compare a draft to past campaigns, audit campaign copy against real performance, pressure-test a sequence before launch, validate a sequence before going live, or asks 'is this campaign as good as my best ones'. Triggers on: 'challenge this campaign', 'benchmark this sequence', 'is this campaign good', 'audit my copy', 'pressure-test before launch', 'compare to my best campaigns', 'should I launch this'. Pulls existing campaign performance from the La Growth Machine MCP when connected; otherwise works from stats and copy the user pastes; falls back to a best-practice baseline when there is no campaign history. For SDR, RevOps, Growth, Head of Sales/Marketing, founders launching outbound. Maintained by La Growth Machine.
Estimate fair market rates for creator partnerships based on platform, follower count, engagement rate, niche, and deliverable type. This skill should be used when estimating influencer rates, calculating creator pricing, building a rate card for a campaign, checking if a creator's rate is fair, comparing influencer costs across platforms, budgeting for a creator campaign, evaluating a creator's rate card, figuring out how much to pay an influencer, benchmarking creator rates against market data, or assessing whether a creator is overcharging. For negotiating rates after estimation, see rate-negotiation-playbook. For full creator vetting beyond pricing, see creator-vetting-scorecard.
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.
Terminal-Bench integration for Mux agent benchmarking and failure analysis
Live Google Search Console analytics — fetches real SEO data (clicks, impressions, CTR, rankings) and delivers actionable insights with CTR benchmarking and opportunity detection. Zero dependencies. Use when the user asks about GSC, Google Search Console, SEO performance, search performance, keywords, rankings, organic traffic, top pages, top queries, "how is my site performing in Google", "check rankings", or "search console report".
Decompose Return on Equity into component ratios to identify performance drivers. Use for financial analysis, performance benchmarking, and identifying improvement opportunities.