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Найдите подходящую возможность для вашего агента.
dispatching-parallel-agents
Use when facing 3+ independent tasks that can be completed without shared state or dependencies - dispatches multiple agents to work concurrently on summarization, investigation, implementation, or analysis
lang-cypher-dev
Foundational Cypher (Neo4j) patterns covering graph pattern matching, MATCH/CREATE/MERGE/DELETE operations, relationships, path patterns, aggregation, filtering, and common graph query patterns. Use when writing Cypher queries, modeling graph data, or needing guidance on graph database operations. This is the entry point for Cypher development.
ml-deployment
Deploy ML models to production - APIs, containerization, monitoring, and MLOps
devexpert-testimonials
Import DevExpert testimonials from Google Sheets (gog) or pasted TSV lists, format text with line breaks, crop profile images to 400x400, copy them to src/assets/testimonials, update src/data/testimonials.json, and optionally update AI Expert IDs in src/pages/cursos/expert/ai.astro. Use when adding new testimonials or processing images.
robotics-code-generator
Generates clean, runnable ROS 2, Gazebo, Isaac Sim, and VLA code for humanoid robotics
aegis-escalation-cascade
Tier escalation and human override cascade protocol for AI-to-Human governance bridge.
model
Algorithm/model development and fine-tuning skill. Use for tasks like dataset design/cleaning, supervised fine-tuning (SFT), preference optimization (DPO/RLHF concepts), LoRA/QLoRA, training configs, evaluation (offline/online), safety checks, deployment packaging, and cost/performance trade-offs.
agent-browser
Browser automation CLI for AI agents. Use when automating web interactions, testing user flows, scraping web content, or performing browser-based tasks. Fast Rust CLI with Node.js fallback, optimized for AI agent workflows with snapshot-based element selection using refs.
using-serena
Enables token-efficient structured development via /serena command. Use for component development, API implementation, system design, test creation, bug fixes, and optimization. Available for Claude Code and all Agents.
try-first-tell-later
Structure educational content using try-first-tell-later pedagogy where students predict, attempt, or reflect before receiving explanations. Creates active learning through cognitive engagement and variation theory's contrast patterns. Use when writing educational materials, designing exercises, creating lecture notes, structuring tutorials, writing teaching examples with LaTeX/Beamer, developing problem sets, or when user mentions try-first, predict-first, productive failure, Socratic method, question-before-answer, exercise-driven learning, or inquiry-based teaching.
context-degradation
Recognize and mitigate context failures including lost-in-middle, context poisoning, distraction, confusion, and clash.
coding-prompt-optimizer
コーディング用プロンプトを最適化・添削するスキル。 以下の状況で使用: (1) ユーザーが「このプロンプトを改善して」「プロンプトを最適化して」と依頼した時 (2) ユーザーが明示的に「/coding-prompt-optimizer」を実行した時 (3) AI向けプロンプトの品質改善を求められた時
template-assistant
Generate intelligent content for PARA Obsidian vault templates. Use when asked to create new notes (project, area, resource, task, capture, daily, weekly-review, booking, checklist, itinerary, trip-research), populate template sections with AI-generated content, or understand what fields a template requires before creation.
context-engineering
Elite context engineering specialist for AI agents - token optimization, degradation patterns, compression, memory systems, multi-agent coordination, vector databases, knowledge graphs, RAG systems, and enterprise context management. Use PROACTIVELY for complex AI orchestration, agent design, debugging context failures, or building LLM pipelines.
respond-to-pr-review
Fetch, analyze, and respond to PR review comments. Use when asked to check PR comments, address review feedback, respond to reviewers, or fix issues raised in code reviews.
ai-engineering
Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI systems.
