Apple Ads benchmarks for subscription apps: TTR, CR, CPT and CPA by country, App Store genre and niche, plus subscriber economics and relative effect studies.
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin apple-ads-benchmarks in adaptyteam/apple-ads-benchmarks. Commands read from the repo's manifest — 1 month ago.
apple-ads-benchmarksUse when someone asks whether an Apple Ads or subscription metric is unusual, what CPA/CPT/TTR/CR was published for a country, App Store genre, or niche, how Apple Ads subscriber economics compare with other paid channels, or what uplift the 2026 report measured for Custom Product Pages and keyword-
Carta Investors plugin — skills for querying investor data, performance benchmarks, regulatory reporting, AGM deck generation, brand extraction, and more via the Carta MCP Server.
29 skills · hooks
Carta Investors plugin — skills for querying investors data, performance benchmarks, regulatory reporting, AGM deck generation, brand extraction, and more via the Carta MCP server
29 skills · hooks
Autonomous experiment loop — optimize any file by a measurable metric. 5 slash commands (/autoresearch-agent:setup, /ar-run, /ar-loop, /ar-status, /ar-resume), 8 built-in evaluators, configurable loop intervals (10min to monthly).
6 skills · 1 agent · hooks
Comprehensive context engineering and harness engineering skills for production-grade AI agent systems: fundamentals, degradation patterns, compression, optimization, self-managed context (model-controlled live context editing), multi-agent coordination, long-horizon prompting (pseudo-formal task briefs for long-running and parallel agents), memory systems, tool design, filesystem context, hosted agents, evaluation, autonomous harnesses, self-improvement loops (RSI, meta-harnesses, evolutionary scaffold search), latent briefing (KV cache sharing between agents), project development, and cognitive architecture. Ships with a researcher operating system (rubrics, mechanism registry, claim provenance, run state machine, adversarial benchmarks, continuous loop).
19 skills
LLM benchmarking and evaluation including lm-evaluation-harness, BigCode Evaluation Harness, and NeMo Evaluator. Use when benchmarking models or measuring performance.
98 skills
用五重过滤法寻找值得模仿的对标，并排除主体差异造成的噪音。用户要求找对标、选择模仿对象或分析竞争参照时使用。
1 skill
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