Use when writing Java code with `dev.axllm:ax` for agent memory, recall callbacks, dynamic skill discovery, loaded-skill state, and used-skill tracking.
Use when writing Java code with `dev.axllm:ax` for agent tracing, centralized and multi-tenant usage accounting, action logs, runtime diagnostics, replay, and production debugging.
Use when writing Java code with `dev.axllm:ax` for agent optimization, evaluators, judges, optimizer artifacts, BootstrapFewShot, and GEPA.
Use when writing Java code with `dev.axllm:ax` for RLM executor loops, AxCodeRuntime sessions, runtime envelopes, process runtimes, and optional runtime profiles.
Use when writing Java code with `dev.axllm:ax` for named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic,…
Use when writing Java code with `dev.axllm:ax` for audio input/output, OpenAI Responses audio mapping, realtime event folding, and generated package audio examples.
Use when writing Java code with `dev.axllm:ax` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
Use when writing Java code with `dev.axllm:ax` for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output…
Use when writing Java code with `dev.axllm:ax` for GEPA, Pareto tradeoffs, reflection clients, metric budgets, optimizer state, and artifacts.
Use when writing Java code with `dev.axllm:ax` for using the generated Ax package, factory functions, package docs, examples, and API reference.
Use when writing Java code with `dev.axllm:ax` for the playbook() context-engineering surface, agent-bound verified evolution, run-end learning, online updates, and rendering a…
Use when writing Java code with `dev.axllm:ax` for reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement patterns.
Use when writing Java code with `dev.axllm:ax` for string signatures, field descriptors, JSON schema output, validation, and typed tool argument shapes.
Use when writing Python code with `axllm` for agents, child delegation, tools, MCP, clarification, runtime state, final typed responses, and direct-respond executor skipping.
Use when writing Python code with `axllm` for deciding between context maps, trajectory context policy, offline optimization (ACE/GEPA), and memory recall for long-context agents.
Use when writing Python code with `axllm` for agent memory, recall callbacks, dynamic skill discovery, loaded-skill state, and used-skill tracking.
Use when writing Python code with `axllm` for agent tracing, centralized and multi-tenant usage accounting, action logs, runtime diagnostics, replay, and production debugging.
Use when writing Python code with `axllm` for agent optimization, verified agent-playbook evolution, evaluators, judges, optimizer artifacts, BootstrapFewShot, and GEPA.
Use when writing Python code with `axllm` for RLM executor loops, AxCodeRuntime sessions, runtime envelopes, process runtimes, and optional runtime profiles.
Use when writing Python code with `axllm` for named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers,…
Use when writing Python code with `axllm` for audio input/output, OpenAI Responses audio mapping, realtime event folding, and generated package audio examples.
Use when writing Python code with `axllm` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
Use when writing Python code with `axllm` for AxGen programs, forward calls, indexed multi-sampling, result pickers, streaming, tools, assertions, traces, usage, and output…
Use when writing Python code with `axllm` for GEPA, Pareto tradeoffs, reflection clients, metric budgets, optimizer state, and artifacts.
Use when writing Python code with `axllm` for using the generated Ax package, factory functions, package docs, examples, and API reference.
Use when writing Python code with `axllm` for the playbook() context-engineering surface, evolving task knowledge, online updates, and rendering a playbook into a program.
Use when writing Python code with `axllm` for reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement patterns.
Use when writing Python code with `axllm` for string signatures, field descriptors, JSON schema output, validation, and typed tool argument shapes.
Use when writing Rust code with `axllm` for agents, child delegation, tools, MCP, citations, persistent playbook learning, stage instructions, runtime state, final typed…
Use when writing Rust code with `axllm` for deciding between context maps, trajectory context policy, offline optimization (ACE/GEPA), and memory recall for long-context agents.
Use when writing Rust code with `axllm` for agent memory, recall callbacks, dynamic skill discovery, loaded-skill state, and used-skill tracking.
Use when writing Rust code with `axllm` for agent tracing, centralized and multi-tenant usage accounting, action logs, runtime diagnostics, replay, and production debugging.
Use when writing Rust code with `axllm` for agent optimization, verified agent-playbook evolution, evaluators, judges, optimizer artifacts, BootstrapFewShot, and GEPA.
Use when writing Rust code with `axllm` for RLM executor loops, AxCodeRuntime sessions, runtime envelopes, process runtimes, and optional runtime profiles.
Use when writing Rust code with `axllm` for named deployment profiles, generic provider clients, model selection, OpenAI-compatible calls, Responses, Gemini, Anthropic, routers,…
Use when writing Rust code with `axllm` for audio input/output, OpenAI Responses audio mapping, realtime event folding, and generated package audio examples.
Use when writing Rust code with `axllm` for flows, nodes, program graphs, nested programs, dynamic options, caching, and optimizer components.
Use when writing Rust code with `axllm` for AxGen programs, forward calls, streaming, tools, assertions, traces, usage, and output parsing.
Use when writing Rust code with `axllm` for GEPA, Pareto tradeoffs, reflection clients, metric budgets, optimizer state, and artifacts.
Use when writing Rust code with `axllm` for using the generated Ax package, factory functions, package docs, examples, and API reference.
Use when writing Rust code with `axllm` for the playbook() context-engineering surface, agent-bound verified evolution, run-end learning, online updates, and rendering a playbook…
Use when writing Rust code with `axllm` for reward-scored generation, iterative candidate improvement, evaluator feedback, and optimizer-backed refinement patterns.
Use when writing Rust code with `axllm` for string signatures, field descriptors, JSON schema output, validation, and typed tool argument shapes.
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
Build image analysis applications with Azure AI Vision SDK for Java. Use when implementing image captioning, OCR text extraction, object detection, tagging, or smart crop — from…
Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket. — from majiayu000/claude-skill-registry
Azure Communication Services common utilities for Java. Use when working with CommunicationTokenCredential, user identifiers, token refresh, or shared authentication acro — from…
Azure Monitor OpenTelemetry Exporter for Java. Export OpenTelemetry traces, metrics, and logs to Azure Monitor/Application Insights. Note: This package is DEPRECATED.
Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources. — from majiayu000/claude-skill-registry
Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources. — from majiayu000/claude-skill-registry
Build blob storage applications with Azure Storage Blob SDK for Java. Use when uploading, downloading, or managing files in Azure Blob Storage, working with containers, o — from…
MicroPython REPL usage, package management, module inspection, and interactive debugging for Universe 2025 (Tufty) Badge.
Minimal BAML skill. BAML is a statically-typed, expression-oriented language with first-class LLM functions — TypeScript-like, snake_case methods, etc.
Spezialfall Contractual Trust Arrangement CTA: doppelseitige Treuhand, Insolvenzschutz durch Verpfaendung an Arbeitnehmer, IFRS-Status Trust Assets, deutsche HGB-Bilanzierung.
Better Auth is an open source authentication framework for TypeScript apps. It gives agents a concrete way to wire sign-in, sessions, passkeys, OAuth providers, and plugins into…
Better Auth JWT verification for Python/FastAPI backends. Use when integrating Python APIs with a Better Auth TypeScript server via JWT tokens.
Build reproducible scientific documents, presentations, and websites with Quarto supporting R, Python, Julia, and Observable JS.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez) — from…
Advanced Biopython modules for motifs, population genetics, sequence utilities, restriction analysis, clustering, and GenomeDiagram visualization; use when you need extended…
Sequence alignment and alignment file processing with Biopython (Bio.Align/Bio.AlignIO), triggered when you need global/local pairwise alignment, MSA read/write/format conversion,…