Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.

Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters, leaderboards, membership sets, or session stores, or when reviewing/cleaning up Redis key naming.

Iris is Redis's umbrella for AI-focused products. Use this skill when integrating with the Iris Redis Agent Memory (RAM) data plane on Redis Cloud — recording session events for an AI agent, creating or searching long-term memories, configuring a memory store, or tuning background memory promotion. Code examples use the official `redis-agent-memory` (Python) and `@redis-iris/agent-memory` (TypeScript) SDKs.

Authors and structures professional-grade agent skills following the agentskills.io spec. Use when creating new skill directories, drafting procedural instructions, or optimizing metadata for discoverability. Don't use for general documentation, non-agentic library code, or README files.

Load when modernizing, migrating, or upgrading C++ code from older standards (C++98/03/11/14) to modern C++ (17/20/23), or when applying systematic code improvements that preserve behavior.

OpenTelemetry SDK initialization and configuration. Use when setting up or reviewing TracerProvider, MeterProvider, or LoggerProvider; choosing exporters, processors, or propagators; configuring OTLP transport; or extending an existing SDK setup for new signals. Use this skill whenever the task involves wiring up the OpenTelemetry SDK, even if the user only mentions "add tracing" or "set up metrics" without saying "SDK."

OpenTelemetry best practices for manual instrumentation. Use when adding, changing, or reviewing OpenTelemetry instrumentation in code. Guidance to choose runtime boundaries, choose signals, apply semantic conventions, handle propagation, control cardinality, and verify the result.

Ollygarden's recommended pattern for setting up OpenTelemetry in Java services. Covers the Javaagent vs Spring Boot Starter vs manual autoconfigure decision-making, the Maven BOM dependency pattern, and the setup checklist (no query strings in telemetry, startup DB span hygiene, declarative YAML config, standard OTEL_* env vars honored). Use when adding OTel to a Java project, choosing a setup path, or reviewing dependency declarations. Triggers on "java otel setup", "javaagent vs starter", "opentelemetry-bom", "url.query", "query parameter PII".

Ollygarden's recommended pattern for setting up the OpenTelemetry SDK in Go services using otelconf. Covers project structure, the Providers struct, no-op fallback, runtime attribute injection, and the zap log bridge. Use when adding OTel to a Go project, structuring telemetry code, or reviewing an existing setup — including when DB spans show up as trace roots or GORM/database spans are disconnected from HTTP spans. Triggers on "go otel setup", "go telemetry pattern", "Providers struct go otel", "gorm WithContext", "root client span go".

Set up agent context for a project. Analyzes tech stack, installs relevant skills from skills.sh, generates custom skills, and configures MCP servers. Use when starting new project, setting up AI context, or asking "set up project", "configure AI", "what skills do I need".

Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.

Create academic presentation slide decks and optionally demo videos from research papers. Use when the user asks to "make slides", "create a deck", "make a presentation", "demo video", "paper slides", "conference talk slides", or wants to turn a paper into a visual presentation. Covers slide generation, narration scripts, TTS audio, and video assembly.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Search published venue papers (IEEE, ACM, Springer, etc.) via Semantic Scholar API. Complements /aris-arxiv (preprints) with citation counts, venue metadata, and TLDR. Use when user says "search semantic scholar", "find IEEE papers", "find journal papers", "venue papers", "citation search", or wants published literature beyond arXiv preprints.

Draft LaTeX paper section by section from an outline. Use when user says "写论文", "write paper", "draft LaTeX", "开始写", or wants to generate LaTeX content from a paper plan.

Mandatory pre-flight compute resource check before running experiments. Detects whether local/remote GPU or compute resources are actually available. If resources are unavailable, STOPS the experiment pipeline immediately and reports to the user — preventing the model from hallucinating fake experiment results. Use when: about to run experiments, deploy training, or any GPU-intensive task.

Search, download, and summarize academic papers from arXiv. Use when user says "search arxiv", "download paper", "fetch arxiv", "arxiv search", "get paper pdf", or wants to find and save papers from arXiv to the local paper library.

Discover top LinkedIn influencers and voices by topic, industry, follower count, and country. Use when you need to find the top 100 voices in a space, build influencer lists for outreach, or identify thought leaders on LinkedIn.

Scrapes LinkedIn job postings using the JobSpy library (python-jobspy). Use this skill whenever the user wants to find jobs on LinkedIn, search for open roles, pull job listings, build a job pipeline, source job targets for GTM research, or monitor hiring signals. Even if the user just says "find me some jobs" or "what roles is [company] hiring for", use this skill. It runs a local Python script that outputs a CSV of job postings with title, company, location, salary, job type, description, and direct URLs.

Extract competitor and customer intelligence from any company's landing page HTML. Discovers tech stack, analytics tools, ad pixels, customer logos, SEO metadata, CTAs, hidden elements, and more. No API keys required.