Loading...
Loading...
Found 1,066 Skills
Corrective cleanup of AI-generated code — removes LLM-specific patterns while preserving behavior. Use when the user says "clean up", "deslop", "slop", "clean AI code", or when you spot LLM-generated code smells after any generation session.
Discover scientific equations from data using LLM-guided evolutionary search (LLM-SR). Multi-island algorithm with softmax-based cluster sampling, island reset, and LLM-proposed equation mutations. Use for symbolic regression and equation discovery.
Network protocol attack playbook. Use when exploiting layer 2/3 protocols including ARP spoofing, LLMNR/NBT-NS/mDNS poisoning, WPAD abuse, DHCPv6 attacks, VLAN hopping, STP manipulation, DNS spoofing, IPv6 attacks, and IDS/IPS evasion.
Senior UI/UX Engineer. Architect digital interfaces overriding default LLM biases. Enforces metric-based rules, strict component architecture, CSS hardware acceleration, and balanced design engineering.
Skill for writing and updating scalar.config.json — Scalar Docs configuration reference for users and LLMs.
Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.
Intercept and debug HTTP traffic from any CLI, service, or script using HTTP Toolkit. Use when you need to inspect LLM API calls, backend requests, auth flows, or debug network-level issues across any language or runtime.
Launch multiple sub-agents in parallel to execute tasks across files or targets with intelligent model selection, quality-focused prompting, and meta-judge → LLM-as-a-judge verification
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.
Automated sitemap generation for all locale URLs, robots.txt configuration, and llms.txt for AI crawler optimization. Use when setting up sitemap.xml, configuring crawling rules, or improving discoverability for search engines and AI systems.
Build AI-native products with agency-control tradeoffs, calibration loops, and eval strategies. Use when building AI agents, LLM features, or products where AI handles user tasks autonomously. Part of the Modern Product Operating Model collection.