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Found 4,086 Skills
Define navigation tabs for Steedos applications. Tabs appear in the application sidebar and support three types: object (shows list view), page (renders a micro page), and url (opens internal/external URL). Covers .tab.yml structure, permissions per permission_set, icon/label configuration, desktop/mobile visibility, iframe/new-window options, license restrictions, and URL template variables.
Curated text animation catalog with exact JSON specs for headings, labels, counters, and text swaps. Use when an agent needs to pick or translate named effects like soft blur in, typewriter, shared axis, line reveal, stagger, crossfade, or kinetic builds into WAAPI, Motion, Framer Motion, GSAP, CSS, Lottie, Rive, or similar stacks.
This skill MUST be used for semantic Rust navigation and analysis: resolving definitions across crate boundaries, finding all references to a symbol, inspecting inferred types or trait implementations, searching symbols by name, and renaming symbols safely. SHOULD be preferred over grep or file reads whenever the task requires Rust-aware understanding.
Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, or another serving framework. Use when a user wants to find the best deployment command for one model across multiple serving frameworks under the same workload, GPU budget, and latency SLA.
Framework for collective skill evolution in multi-user LLM agent ecosystems — automatically distills session experience into reusable SKILL.md files and shares them across agent clusters.
Chrome extension for automating OpenAI OAuth registration flows with captcha retrieval, CPA callback verification, and auto-recovery across multiple rounds
Automated, project-wide code coverage and CRAP (Change Risk Anti-Patterns) score analysis for .NET projects with existing unit tests. Auto-detects solution structure, runs coverage collection via `dotnet test` (supports both Microsoft.Testing.Extensions.CodeCoverage and Coverlet), generates reports via ReportGenerator, calculates CRAP scores per method, and surfaces risk hotspots — complex code with low test coverage that is dangerous to modify. Use when the user wants project-wide coverage analysis with risk prioritization, coverage gap identification, CRAP score computation across an entire solution, or to diagnose why coverage is stuck or plateaued and identify what methods are blocking improvement. DO NOT USE FOR: targeted single-method CRAP analysis (use crap-score skill), writing tests, running tests without coverage collection, applying test filters, producing TRX reports, or troubleshooting test execution (use run-tests for all of these).
Generative ideation engine. Takes a domain, trend, question, or constraint and produces 15-30 novel possibilities — things that might be true, businesses that could exist, futures that could unfold. Spawns a team of 6 specialist agents — Signal Scout, Analogist, Inverter, Combinator, Contrarian, Futurist — who each generate ideas from a distinct creative angle. The lead cross-pollinates across agents, finds unexpected combinations, and ranks the output by novelty × plausibility. Use when the user says "brainstorm", "what could exist", "what's possible", "generate ideas", "what might be true", "possibilities", or presents a domain and wants divergent exploration rather than evaluation of a specific idea.
Guides systematic PyTorch recommender-system model development across compact data facts, existing source code, configs, focused tests, and training loops without overloading context from broad research archives. Use when building, debugging, or refactoring torch/nn.Module RecSys models with Transformer/HSTU/attention blocks, sparse/dense/list feature fusion, pCVR/CTR heads, ablation axes, or competition codebases where many model ideas exist but bugs and interface drift must be controlled. 用来指导推荐系统 PyTorch 模型开发、Transformer/HSTU 建模、关键数据事实、特征交互、shape/debug、训练闭环和已有模型结构的系统化推进。
Use when writing or reviewing k6 documentation across TypeScript types, user docs, and release notes.
Cluely platform help — real-time AI meeting assistant with live coaching overlay, pre-call briefs, meeting notes, conversation analytics, and knowledge base RAG. Use when setting up Cluely for live AI prompts during sales calls, configuring the knowledge base with company docs for real-time RAG retrieval, connecting Cluely to HubSpot or Salesforce via Merge.dev, troubleshooting transcription accuracy or speaker attribution errors, comparing Cluely Pro vs Pro + Undetectability plans, or setting up team coaching scorecards and missed opportunity tracking. Do NOT use for choosing between AI note-takers across vendors (use /sales-note-taker) or reviewing a call for coaching (use /sales-call-review).
Specializes in analyzing Lynx trace data to diagnose performance issues and provide actionable optimization strategies. Key Scenarios: - Loading Performance: Diagnosing slow startup metrics (FCP, FMP, TTI) and white screen issues. - Smoothness Analysis: Investigating root causes for scroll jank, frame drops, and interaction lag. - Regression Detection: Comparing traces to identify performance degradation or verify optimization gains between versions. - Pipeline Deep Dive: Pinpointing bottlenecks in specific rendering stages like Layout, Paint, JS execution, and background threads. - Native Module Analysis: Investigating performance issues related to native module calls.