vasilyu1983/AI-Agents-public - Page 2

qa-agent-testing

Builds QA harnesses for LLM agents. Use when evaluating tool, trace, red-team, regression, multi-agent, or carried-workspace trajectory behavior.

qa-debugging

Systematic debugging for crashes, regressions, flakes, and production bugs. Use when diagnosing stack traces, logs, traces, or profiling data.

qa-docs-coverage

Audits and enforces documentation quality. Use when checking coverage, freshness, runbook validity, AI-instruction coverage, or cleaning stale/duplicate markdown after LLM edits.

qa-persona-testing

Tests apps as an ICP persona through browser automation and reports friction, fixes, and risks. Use when running persona-based, ICP, or synthetic-user testing.

qa-refactoring

Safe refactoring with behavior preservation. Use when reducing technical debt, planning codemods, applying strangler migrations, or tightening CI guardrails around risky changes.

research-arxiv-scout

Discovers and triages recent arXiv papers for AI/ML, agents, and software/QA. Use when scouting categories, arXiv IDs, or source lists.

research-git

Scans public GitHub repos for agent skills, dev practices, and code patterns. Use when enriching skills, setting team policy, or researching a build domain.

research-scout

Mines academic papers, research blogs, and curator newsletters for stealable methods and frameworks. Use when scanning research for applicable techniques across AI/ML/SWE.

software-architecture-design

Designs runtime and platform architecture inside a chosen solution. Use when deciding modular monolith vs services, consistency, resilience, or estate topology.

software-backend

Builds backend services and APIs with durable defaults. Use when implementing REST, GraphQL, tRPC, or gRPC services with auth, queues, data, or observability.

software-clean-code-standard

Defines clean-code standards and CC-* rules. Use when reviewing code, setting team standards, citing lint findings, or measuring erosion, complexity mass, and verbosity.

software-code-review

Applies systematic code review patterns and checklists. Use when reviewing PRs or diffs for correctness, security, readability, maintainability, and AI-generated changes.

software-frontend

Builds frontend applications across major web stacks. Use when implementing UI, fixing hydration or SSR issues, or setting up modern frontend architecture.

software-ui-ux-design

Designs and audits UI/UX systems with usability and accessibility requirements. Use when shaping flows, design systems, interaction patterns, or WCAG-aware product behavior.

data-lake-platform

Designs lakehouse platforms across Iceberg, Delta, Hudi, and Paimon. Use when choosing catalogs, CDC paths, query engines, governance, or cost controls.

ops-devops-platform

Designs DevOps and platform engineering systems. Use when planning Kubernetes, Terraform, GitOps, CI/CD, observability, incident response, or cloud-native operations.

software-android-design

Designs and audits native Android interfaces. Use when reviewing Compose layout, typography, color, motion, or adaptive patterns on a verified emulator build.

software-ios-ai-engine

Design local AI engines for iOS. Use when wiring Apple Foundation Models, local classifiers, extraction, summarization, grounded answers, and cloud fallbacks.

software-ios-design

Designs and audits native iOS interfaces. Use when reviewing or refining SwiftUI layout, typography, Liquid Glass, navigation, or dashboards on a freshly verified build.

software-ios-native

Guides native iOS with Swift, SwiftUI, UIKit interop, concurrency, and persistence. Use when building or reviewing iPhone/iPad apps after establishing runtime truth.

software-mobile

Guides mobile platform selection and delivery across native and cross-platform stacks. Use when planning auth, push, deep links, releases, or app architecture for iOS/Android.

software-ux-research

Guides user research methods and research ops. Use when running interviews, usability tests, surveys, or A/B tests to de-risk product decisions.

ai-architecture-advisor

Chooses among AI/ML approaches: classical ML, LLM, RAG, fine-tuning, agents, multimodal, embeddings/recsys, dense/MoE/SSM/diffusion. Use when picking or scaling an architecture.

ai-coding-agents-command-runtime

Designs slash-command runtimes for coding-agent CLIs. Use when modeling command registries, lazy loading, aliases, forked commands, or remote-safe dispatch.

ai-coding-agents-plugins

Designs plugin systems for coding-agent runtimes and CLIs. Use when adding plugin manifests, extension points, built-in plugins, or reloadable agent integrations.

ai-coding-agents-provider-runtime

Designs provider runtimes for coding agents. Use when modeling model abstraction, streaming semantics, tool-call normalization, retries, or fallback routing.

ai-coding-agents-release-distribution

Designs release and distribution systems for coding-agent CLIs. Use when modeling packaging, auto-update channels, plugin compatibility, cache migrations, or install footprints.

ai-coding-agents-remote-runtime

Designs remote execution and bridge runtimes for coding agents. Use when implementing remote sessions, local-UI remote-execution, reconnect logic, or permission bridging.

ai-coding-agents-sessions

Designs session lifecycle for coding-agent runtimes. Use when implementing resume, transcript restoration, checkpoint rewind, cross-worktree recovery, or session-state persistence.

ai-coding-agents-settings-policy

Designs settings and policy layers for coding-agent runtimes. Use when modeling source precedence, managed policy, env controls, or runtime settings validation.

ai-coding-agents-terminal-ui

Designs terminal-first coding-agent UX: REPL, prompt input, status lines, keybindings, display modes. Use when shaping TUI rendering, history, or background-task navigation.

ai-coding-agents-tools

Designs tool runtimes for coding agents. Use when modeling tool registries, deferred loading, permission-aware execution, tool search, or remote tool rendering.

ai-data-curation-pretraining

Builds and audits LLM pretraining corpora: extraction, filtering, dedup, decontamination, data mixing, synthetic data. Use when curating or ablating a pretraining data pipeline.

ai-deep-research

Builds repeatable deep-research workflows for verified synthesis. Use when producing evidence-backed briefs, comparisons, dossiers, or research pipelines.

ai-distributed-training

Guides multi-GPU pre-training: DDP, FSDP2, ZeRO, tensor/pipeline/expert parallelism, fp8/Muon. Use when scaling a run, training MoE, or reproducing GPT-2 on rented GPUs.

ai-llm-inference

LLM inference patterns for latency, batching, caching, quantization, routing, and serving stacks. Use when optimizing throughput, tail latency, or serving cost.

ai-local-model-ops

Runs local and self-hosted LLM workflows with Ollama, LM Studio, MLX, Open WebUI, llamafile, and adapters. Use when operating private model stacks.

ai-ml-data-science

ML and data science workflows - EDA, feature engineering, modelling, evaluation, and production handoff. Use when exploring data or building models.

ai-ml-timeseries

Time-series forecasting with temporal validation, panel models, probabilistic forecasts, and TS foundation models. Use when modeling ordered observations.

ai-mlops

Operates production MLOps for ML, LLM, and agent systems. Use when designing deployment, monitoring, retraining, incident response, or GenAI security workflows.