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Found 1,893 Skills
Use when working with Roblox animation systems including playing, stopping, or blending animations on Humanoid characters or non-Humanoid models, handling AnimationTrack events, replacing default character animations, or debugging animation priority and blending issues.
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of managing token budgets and session longevity.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch for Claude Code or Cursor, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Roblox platform UX and monetization specialist - Masters engagement loop design, DataStore-driven progression, Roblox monetization systems (Passes, Developer Products, UGC), and player retention for Roblox experiences
PostHog feature flags for Go applications
PostHog feature flags for Python applications
PostHog feature flags for API applications
Graham cigar-butt (NCAV / net-net) single-stock diagnostic. Combines a 100-point static cheapness score (NCAV, PE, PB, dividend yield, debt coverage, earnings stability) with a dynamic adjustment layer (industry cycle, earnings trend, insider activity, NCAV trajectory) to separate real bargains from value traps. Pulls data from Longbridge CLI/MCP first, falls back to WebSearch only for gaps, runs cross-statement reconciliation (勾稽校验) before scoring, and footnotes every figure to its source. Triggers: "格雷厄姆", "捡烟蒂", "烟蒂股", "烟蒂投资", "NCAV", "净流动资产", "清算价值", "安全边际", "价值陷阱", "深度价值", "撿煙蒂", "煙蒂股", "煙蒂投資", "淨流動資產", "清算價值", "安全邊際", "價值陷阱", "深度價值", "Graham", "cigar butt", "net-net", "liquidation value", "value trap", "margin of safety", "deep value", "Benjamin Graham".
The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Cross-modal eval is the recommended Phase 3 quality gate: 3 frontier models from different providers critique the output, you iterate to quality, THEN write tests that lock in the proven-good behavior.
Use when the user asks "what predefined metrics are available", "which built-in metrics should I use", "what does CSAT measure", "how does hallucination detection work", "what's the difference between Interruption Score and AI Interrupting User", "which metrics are free", "which metrics need audio", "configure silence threshold", "set up sentiment metric", or any question about Cekura's out-of-the-box metrics. Covers the full catalog of predefined metrics — what each does, costs, constraints, configuration options, and when to use each one.
Multi-AI Agent P2P Debate. Suitable for technical solution stress testing, multi-perspective collision, and design decision convergence. Use it when you want a solution to be challenged or to understand the pros and cons of different technical routes. Triggered when mentioning "debate", "agent discussion", "multi-angle analysis", or "start a team".
Develops resources for FiveM using the Qbox Project (qbx_core). Covers the exports-based API, bridge compatibility, Ox integration (ox_lib, ox_inventory), and best practices. Use when the user works with FiveM, Qbox, qbx_core, or mentions `exports.qbx_core`, `QBX.PlayerData`, or `ox_lib`.