Diogenesoftoronto

This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or recursive optimization guidance.

This skill helps an LLM generate correct AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, shared fields, llmQuery(...), RLM code execution, recursionOptions, or agent runtime behavior. For tuning and eval with agent.optimize(...), use ax-agent-optimize.

This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, presets, embeddings, extended thinking, context caching, or mentions OpenAI/Anthropic/Google/Azure/Groq/DeepSeek/Mistral/Cohere/Together/Ollama/HuggingFace/Reka/OpenRouter with @ax-llm/ax.

This skill helps an LLM generate correct AxFlow workflow code using @ax-llm/ax. Use when the user asks about flow(), AxFlow, workflow orchestration, parallel execution, DAG workflows, conditional routing, map/reduce patterns, or multi-node AI pipelines.

This skill helps an LLM generate correct AxGen code using @ax-llm/ax. Use when the user asks about ax(), AxGen, generators, forward(), streamingForward(), assertions, field processors, step hooks, self-tuning, or structured outputs.

This skill helps an LLM generate correct AxGEPA optimization code using @ax-llm/ax. Use when the user asks about AxGEPA, GEPA, Pareto optimization, multi-objective prompt tuning, reflective prompt evolution, validationExamples, maxMetricCalls, or optimizing a generator, flow, or agent tree.

This skill helps an LLM generate correct AxLearn code using @ax-llm/ax. Use when the user asks about self-improving agents, trace-backed learning, feedback-aware updates, or AxLearn modes.

This skill helps an LLM generate correct DSPy signature code using @ax-llm/ax. Use when the user asks about signatures, s(), f(), field types, string syntax, fluent builder API, validation constraints, or type-safe inputs/outputs.

This skill helps an LLM generate correct AxAgent tuning and evaluation code using @ax-llm/ax. Use when the user asks about agent.optimize(...), judgeOptions, eval datasets, optimization targets, saved optimizedProgram artifacts, or recursive optimization guidance.

This skill helps an LLM generate correct AxAgent code using @ax-llm/ax. Use when the user asks about agent(), child agents, namespaced functions, discovery mode, shared fields, llmQuery(...), RLM code execution, recursionOptions, or agent runtime behavior. For tuning and eval with agent.optimize(...), use ax-agent-optimize.

This skill helps an LLM generate correct AI provider setup and configuration code using @ax-llm/ax. Use when the user asks about ai(), providers, models, presets, embeddings, extended thinking, context caching, or mentions OpenAI/Anthropic/Google/Azure/Groq/DeepSeek/Mistral/Cohere/Together/Ollama/HuggingFace/Reka/OpenRouter with @ax-llm/ax.

This skill helps an LLM generate correct AxFlow workflow code using @ax-llm/ax. Use when the user asks about flow(), AxFlow, workflow orchestration, parallel execution, DAG workflows, conditional routing, map/reduce patterns, or multi-node AI pipelines.

This skill helps an LLM generate correct AxGen code using @ax-llm/ax. Use when the user asks about ax(), AxGen, generators, forward(), streamingForward(), assertions, field processors, step hooks, self-tuning, or structured outputs.

This skill helps an LLM generate correct AxGEPA optimization code using @ax-llm/ax. Use when the user asks about AxGEPA, GEPA, Pareto optimization, multi-objective prompt tuning, reflective prompt evolution, validationExamples, maxMetricCalls, or optimizing a generator, flow, or agent tree.

This skill helps an LLM generate correct AxLearn code using @ax-llm/ax. Use when the user asks about self-improving agents, trace-backed learning, feedback-aware updates, or AxLearn modes.

This skill helps an LLM generate correct DSPy signature code using @ax-llm/ax. Use when the user asks about signatures, s(), f(), field types, string syntax, fluent builder API, validation constraints, or type-safe inputs/outputs.

Work on the Rust-canonical den CLI and MCP server for provisioning, deployment, lifecycle, and custom-domain flows across Sprite, Railway, Cloudflare, and sesame/Porkbun. Use when changing `den` commands, `den-mcp` workflows, runtime or domain provider dispatch, the self-hosted runtime design, or tests and docs that describe the Rust CLI and MCP behavior.

Maintain den when provisioning and lifecycle flows use the Sprite CLI on Fly and custom domains are managed through sesame on Porkbun. Use when changing Sprite-backed `den` workflows, custom-domain forwarding, provider-specific docs, or tests that depend on Sprite URLs and Porkbun forwarding behavior.

Diagnose runtime errors using the autopsy library. Invoke when the user hits an error, wants to understand a crash, asks "why did this fail", or wants to install global error hooks. Covers explain(), install(), and visualize().

Write, update, and review Mintlify MDX documentation for Bifrost features. Explores the full codebase (UI, Go backend, config schema), validates config.json examples, places screenshot placeholders, and presents outlines for approval before writing. Invoked with /docs-writer <feature-name>, /docs-writer update <doc-path>, or /docs-writer review <doc-path>.