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Core coding rules and workflow for developing VTEX FastStore storefronts. Use when starting any FastStore development task, writing TypeScript/React…

Use when establishing branching strategies, implementing Conventional Commits, creating or reviewing PRs, resolving PR review comments, merging PRs (including…

A software security skill that integrates with Project CodeGuard to help AI coding agents write secure code and prevent common vulnerabilities. Use this skill…

CRITICAL: Use for ALL Rust questions including errors, design, and coding.

Use when working with CUE kind definitions, schemas, or versioning in grafana-app-sdk projects (app platform apps). This skill should be used when the user…

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6×…

Execute the implementation planning workflow using the plan template to generate design artifacts.

25+ proven headline formulas that stop the scroll, capture attention, and drive clicks. Templates and examples for every situation. Use when: Writing headlines…

AI-powered deep research using Parallel AI APIs for chat, research reports, entity discovery, and data enrichment. Use this skill when doing web research,…

Analyzes events through environmental lens using ecological principles, systems thinking, sustainability frameworks, and conservation biology to assess ecosystem health, biodiversity impacts, and long-term environmental sustainability. Provides insights on climate change, resource management, pollution, habitat conservation, and human-nature relationships. Use when: Environmental policy, climate decisions, conservation planning, resource extraction, pollution assessment. Evaluates: Ecosystem health, biodiversity, sustainability, climate impacts, carrying capacity, environmental justice.

Use when writing or reviewing k6 documentation across TypeScript types, user docs, and release notes.

Run a full Flows app platform review against a React/TypeScript CDF codebase, following the cognitedata/dune-app-reviews scoring criteria. Produces three artifacts: review-files.md (per-file inventory), review-packages.md (dependency audit), and review-report.md (scored report with must/should/nice-fix items). Use when the user asks for a Flows app review, pre-submit review, approval review, app certification review, code quality audit, CDF platform review, or "run dune-review" on a codebase before submission.

Measure what matters with proper event tracking, funnels, cohorts, and metrics. Use when setting up analytics, tracking features, or understanding behavior.

Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast…

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics),…

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's…

Use when writing UI tests, recording interactions, tests have race conditions, timing dependencies, inconsistent pass/fail behavior, or XCTest UI tests are…

NHL data via ESPN public endpoints — scores, standings, rosters, schedules, game summaries, play-by-play, injuries, transactions, futures, team/player stats, leaders, and news. Zero config, no API keys. Use when: user asks about NHL scores, standings, team rosters, schedules, game stats, box scores, play-by-play, injuries, transactions, betting futures, team/player statistics, or NHL news. Don't use when: user asks about other hockey leagues (AHL, KHL, college hockey). For other sports use: nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), mlb-data (MLB), football-data (soccer), tennis-data (tennis), golf-data (golf), cfb-data (college football), cbb-data (college basketball), fastf1 (F1). For betting odds use polymarket or kalshi. For news use sports-news.

Persistent cross-session memory using Memento MCP knowledge graph (mcp__memento__* tools). Recall-before-acting: search memory before starting tasks, on errors, and when receiving corrections. Multi-dimensional search: two queries per recall event (technical topic + process/workflow learnings). Store-after-discovery: persist solutions, conventions, and corrections immediately. Three-step recall: search, open_nodes, traverse relations. WORKING_STATE.md for crash recovery. Self-reminder protocol every 5-10 messages. Activate on task start, errors, corrections, session boundaries, or explicit memory requests. See references/agents-md-setup.md for AGENTS.md integration.

High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2×…