This skill helps an LLM generate correct AxGen code using @ax-llm/ax. Use when the user asks about ax(), AxGen, generators, forward(), streamingForward(), validation, assertions, streaming assertions, field processors, step hooks, self-tuning, or structured outputs. For MCP clients, transports, prompts, resources, tasks, subscriptions, or authentication use ax-mcp alongside this skill.

This skill helps an LLM generate correct audio code with @ax-llm/ax. Use when the user asks about ai.transcribe(), ai.speak(), signature audio inputs or outputs, agent audio behavior, .chat() conversational audio, OpenAI audio or realtime models, Gemini Live native audio, Grok Voice Agent models, voices, formats, transcripts, or how audio fits with structured outputs.

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, routing, adaptive balancing, presets, embeddings, batch audio with ai.transcribe() or ai.speak(), extended thinking, context caching, or mentions OpenAI/Anthropic/Google/Azure/DeepSeek/Meta/Mistral/Cohere/Reka/Grok with @ax-llm/ax.

Covers publication-ready matplotlib figures for academic papers, slides, and reports—bars, trends, scatter, heatmaps, and multi-panel layouts—with this repository’s house style, print/vector export conventions, and parity with figures4papers demos. Use when the user is finalizing or creating such figures in matplotlib. Do not use for interactive dashboards or web viz (Plotly, Altair, Bokeh), exploratory-only plots without a publication target, dominant 3D or geographic mapping, or Illustrator/Figma-first infographic workflows.

Publication-quality DNA/RNA visualizations. Plasmid maps (circular/linear), sequence logos, restriction enzyme maps, GC content plots, and gene feature annotation tracks from GenBank/FASTA.

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

Install or remove third-party openscience skills from a public git repository. Use when the user says "add this skill <url>", "install skill <url>", or "remove skill <namespace>". The skill runs locally via `openscience skill add|list|remove`, fetches the repo, runs a 6-layer safety gate (regex + server-side Haiku classifier), prompts the user to confirm, then writes the skills to ~/.openscience/installed-skills/ and uploads to the dashboard for cross-machine sync.

Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.

Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

Use when building a Django app with the Viewflow library — workflows (BPMN), CRUD viewsets, FSM state transitions, jsonstore process data, or Viewflow forms. Loads Viewflow conventions and the full docs.

Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI.

Compatibility alias for Loopy. Use only when an existing installation or older instruction explicitly invokes loop-library; use Loopy for new installations and requests. Provides the same discovery, recommendation, audit, repair, adaptation, guided crafting, bounded execution, run debrief, project loop saving, and publication-preparation workflows.

Investigate and fix flaky/random CI test failures in dotnet/macios. Trigger on GitHub issues describing intermittent test failures, CI postmortem issues, or when asked to fix a flaky test. Analyzes test code, identifies root causes (shared state, environment dependencies, race conditions), and applies fixes.

Reviews financial documents (prospectuses, ADVs, marketing materials) for FINRA 2210 compliance, required disclosures, and balanced presentation. Use when reviewing financial statements, audit documents, regulatory filings, or when the user mentions compliance checks, financial audits, or document verification.

Schedules reminders and recurring tasks via the letta cron CLI. Use when the user asks to be reminded of something, wants periodic work or check-ins, or needs to list, inspect, replace, or cancel scheduled tasks.

Generate images from text prompts (and optionally edit/remix input images). Use when the user asks to create, generate, draw, render, or edit an image, illustration, logo, icon, diagram, or photo.

Creates, edits, and migrates Letta Code statusline mods. Use when handling the /statusline command or continuing work started by /statusline.

Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Use when spawning sub-agents to complete scoped work.

Switch between multiple Obsidian wiki vault profiles. Use this skill when the user says "/wiki-switch NAME", "switch to my work wiki", "switch vault", "change wiki", "which wiki am I on", "list my wikis", "show my vaults", "create a new vault config", or "add a new wiki profile". The skill manages named config files at <global config dir>/config.NAME and activates one by symlinking it to <global config dir>/config.

Answer questions by searching the compiled Obsidian wiki. Use this skill when the user asks a question about their knowledge base, wants to find information across their wiki, asks "what do I know about X", "find everything related to Y", or wants synthesized answers with citations from their wiki pages. Also use when the user wants to explore connections between topics in their wiki, or asks a multi-hop "how is X connected to Y", "what links X to Y", "trace the chain from X to Z", or "what does X depend on transitively" question — answered by walking typed edges across multiple hops. Works from any project. Includes an index-only fast mode triggered by "quick answer", "just scan", "don't read the pages", "fast lookup" — returns answers from page summaries and frontmatter without reading page bodies. Accepts inline named-vault routing like "wiki-query @work what do I know about X" via the shared Config Resolution Protocol.