Help developers integrate Chainlink Data Feeds into smart contracts and applications. Use for price feed integration, feed address lookup, consumer contract generation, multi-chain data feeds (EVM, Solana, Aptos, StarkNet, Tron), MVR bundle feeds, SVR/OEV feeds, feed monitoring, historical data, L2 sequencer checks, rates/volatility feeds, SmartData/RWA feeds, or debugging feed integrations. Trigger on any mention of Chainlink price feeds, oracle data, AggregatorV3Interface, latestRoundData, or feed addresses.

Configures and runs LLM evaluation using Promptfoo framework. Use when setting up prompt testing, creating evaluation configs (promptfooconfig.yaml), writing Python custom assertions, implementing llm-rubric for LLM-as-judge, or managing few-shot examples in prompts. Triggers on keywords like "promptfoo", "eval", "LLM evaluation", "prompt testing", or "model comparison".

Run Semgrep static analysis scans and create custom detection rules. Use when asked to scan code with Semgrep, find security vulnerabilities, write custom YAML rules, or detect specific bug patterns. IMPORTANT: Also use this skill when users ask to 'scan for bugs', 'check code quality', 'find vulnerabilities', 'static analysis', 'lint for security', 'audit this code', or want to enforce coding standards — even if they don't mention Semgrep by name. Semgrep is the right tool for pattern-based code scanning across 30+ languages.

Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.

Perform a refactor pass focused on simplicity after recent changes. Use when the user asks for a refactor/cleanup pass, simplification, or dead-code removal and expects build/tests to verify behavior.

Create a well-formed git commit from current changes using session history for rationale and summary; use when asked to commit, prepare a commit message, or finalize staged work.

How to manage imas-codex services (Neo4j graph, embedding server, LLM proxy, SSH tunnels). Use when starting, stopping, checking status, or troubleshooting services.

Troubleshoot and respond to Langfuse-related incidents and outages. Use when experiencing Langfuse outages, debugging production issues, or responding to LLM observability incidents. Trigger with phrases like "langfuse incident", "langfuse outage", "langfuse down", "langfuse production issue", "langfuse troubleshoot".

The Idea Generator is the brainstorming specialist for Headout's PM OS. Engage after a Problem Frame is complete — or directly when a PM has a clear problem statement and wants to explore the solution space rigorously before committing to a direction. Acts as a high-fidelity thought partner: builds deep context through a multi-round interview (current state, constraints, prior attempts, platform nuances), maps the solution space into 2-4 strategic themes before generating any ideas, then produces specific ideas within each theme. Trigger for: "what are the ways we could solve this", "brainstorm approaches to X", "what should we build for this problem", "explore the solution space", "I have a problem frame and want ideas", "ideate on this", "what directions could we go", or any request to generate solution directions after a problem has been framed. Output: a structured Idea Brief.

Create webhook endpoints with signature verification, retry logic, and payload validation. Use when receiving and processing webhook events. Trigger with phrases like "create webhook", "handle webhook events", or "setup webhook handler".

Build secure API authentication systems with OAuth2, JWT, API keys, and session management. Use when implementing secure authentication flows. Trigger with phrases like "build authentication", "add API auth", or "secure the API".

Access external LLM providers through Domino AI Gateway - a secure proxy with centralized API key management, usage monitoring, and compliance. Supports OpenAI, AWS Bedrock, Azure OpenAI, Anthropic, and more. Use when calling LLMs from Domino, configuring AI Gateway endpoints, or monitoring LLM usage and costs.

Use when reviewing PRs, simplifying over-engineered code, judging architecture, or renaming unclear concepts — inline fake helpers, delete meatless ceremony, rename lying names, merge over-split files, reject premature abstraction, say no to speculative config/modes/layers, then end with the smallest safe next change. Triggers when code is too fancy, too abstract, too clever, too many files/helpers/layers, or too well-factored but painful to change. Embody Grug brain: complexity very bad, small words, no consultant speak, no hard pivot to opposite dogma.

Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.

Implements standardized API error responses with proper status codes, logging, and user-friendly messages. Use when building production APIs, implementing error recovery patterns, or integrating error monitoring services.

Use when working with *.excalidraw or *.excalidraw.json files, user mentions diagrams/flowcharts, or requests architecture visualization - delegates all Excalidraw operations to subagents to prevent context exhaustion from verbose JSON (single files: 4k-22k tokens, can exceed read limits)

Trains and fine-tunes ML models, builds data preprocessing and feature engineering pipelines, deploys models as REST APIs, integrates inference into production applications, and designs RAG and LLM-powered systems. Covers MLOps workflows including experiment tracking, drift detection, retraining triggers, and A/B testing. Use when the user asks about training or fine-tuning a model, building ML pipelines, model serving or inference optimization, evaluating model performance, working with frameworks like PyTorch, TensorFlow, scikit-learn, or Hugging Face, setting up vector databases, prompt engineering, or taking an ML prototype to production.

Strategic guide for becoming an effective GitHub contributor. Covers opportunity discovery, project selection, high-quality PR creation, and reputation building. Use when looking to contribute to open-source projects, building GitHub presence, or learning contribution best practices.