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Found 78 Skills
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.
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.
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".
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".
Expert in observing, benchmarking, and optimizing AI agents. Specializes in token usage tracking, latency analysis, and quality evaluation metrics. Use when optimizing agent costs, measuring performance, or implementing evals. Triggers include "agent performance", "token usage", "latency optimization", "eval", "agent metrics", "cost optimization", "agent benchmarking".
Generate comprehensive philosophy and standards documents for any domain (UX design, landing pages, email outbound, API design, etc.). Load when user says "create philosophy doc", "generate standards for [domain]", "build best practices guide", or "create benchmarking document". Conducts deep research, synthesizes findings, and produces structured philosophy documents with principles, frameworks, anti-patterns, checklists, case studies, and metrics.
Performance optimization expert covering profiling, benchmarking, memory allocation, SIMD, cache optimization, false sharing, lock contention, and NUMA-aware programming.
Automates benchmark test creation for C++ projects using Google Benchmark with consistent software testing patterns. Use when creating performance benchmarks, profiling tests, or when the user mentions benchmarking, Google Benchmark, or performance testing.
Spatial indexing and world streaming for Three.js building games with thousands of pieces. Use when optimizing building games, implementing spatial queries, chunk loading, or profiling performance. Includes spatial hash grids, octrees, chunk managers, and benchmarking tools.
Perform a deep competitive analysis for a solopreneur business. Use when mapping competitors in detail, finding exploitable gaps, understanding competitor strategy, benchmarking your own offering, or deciding how to position against the field. Goes deeper than the broad landscape mapping in market-research — this is focused dissection of specific competitors. Trigger on "analyze my competitors", "competitive analysis", "who are my competitors", "competitor deep-dive", "how do I beat the competition", "competitive landscape", "benchmark against competitors".
Senior SaaS CFO / Financial Analyst (15+ years) specialized in financial modeling, projections, and exit strategy for bootstrapped and VC-backed SaaS companies. Activate when user needs: (1) Revenue projections (1-5 years), (2) Exit valuation and multiples, (3) Unit economics analysis (CAC, LTV, payback), (4) Scenario modeling (conservative/base/optimistic), (5) Fundraising narratives with financial backing, (6) M&A due diligence financials, (7) SaaS metrics benchmarking, (8) Cohort analysis and churn modeling. Triggers: "proyecciones", "projections", "exit", "valuation", "ARR", "MRR", "multiples", "revenue forecast", "financial model", "exit strategy", "CAC", "LTV", "unit economics", "churn", "fundraising", "M&A", "acquisition", "5 year plan".
Optimizes Python library performance through profiling (cProfile, PyInstrument), memory analysis (memray, tracemalloc), benchmarking (pytest-benchmark), and optimization strategies. Use when analyzing performance bottlenecks, finding memory leaks, or setting up performance regression testing.