Find the perfect capability for your agent.
Review a GitHub pull request using multiple expert personas. Takes a PR URL as input, analyzes the changes, and generates comprehensive review feedback from different perspectives (Merge Specialist, Frontend, Backend, Security, DevOps, AI/Agent, SRE, Chief Architect).
Create release notes for a new version tag. Gathers all commits, PRs, issues fixed, and breaking changes since a previous release. Creates the release notes markdown file, tags the repo, and pushes. Asks the user to confirm the base version to diff against.
Multi-person projects - shared state, todo claiming, handoffs
Microsoft Teams bots and AI agents - Claude/OpenAI, Adaptive Cards, Graph API
Reddit Ads API - campaigns, targeting, conversions, agentic optimization
AI Engine Optimization - semantic triples, page templates, content clusters for AI citations
SEO and AI discovery (GEO) - schema, ChatGPT/Perplexity optimization
OpenAI Codex CLI code review with GPT-5.2-Codex, CI/CD integration
Analyze existing repositories, maintain structure, setup guardrails and best practices
TDD iteration loops using Claude Code Stop hooks - runs tests after each response, feeds failures back automatically
Update documentation pages to match source code changes on the current branch
Analyzes environmental and climate data including temperature trends, pollution monitoring, ecological modeling, carbon footprint assessment, and biodiversity metrics; trigger when users discuss climate change, ecosystems, pollutants, or sustainability assessments.
Analyze political data, fact-check claims, and study policy impacts using evidence-based methods
Machine learning pipeline for scientific research including data preprocessing, feature engineering, model selection, training, evaluation, and interpretation. Covers supervised/unsupervised learning, deep learning, cross-validation, hyperparameter tuning, and model explainability. Use when user asks to build a predictive model, classify data, cluster samples, do feature selection, or apply ML to research data. Triggers on "machine learning", "classification", "clustering", "random forest", "neural network", "deep learning", "predict", "feature selection", "cross-validation", "train model".
HuggingFace Transformers for model inference. Use when: text classification, NER, question answering, summarization, embeddings, zero-shot classification. NOT for: training large models (use cloud), simple regex/rule-based tasks, production serving at scale (use vLLM).
