product-management
Founder-PM toolkit for discovery, roadmaps, prioritization, and PMF measurement. Use when planning product strategy, metrics, or roadmaps.
qa-refactoring
Safe refactoring with behavior preservation. Use when reducing technical debt, planning codemods, applying strangler migrations, or tightening CI guardrails around risky changes.
qa-resilience
Designs and tests distributed-system resilience. Use when adding retries, deadlines, hedging, circuit breakers, overload protection, chaos experiments, or SLO reliability gates.
qa-testing-mobile
Mobile QA for iOS and Android. Use when planning automation frameworks, device matrix, flake control, or CI/CD release gates.
qa-testing-nunit
Designs NUnit-based C# test suites for API, component, and integration coverage. Use when creating fixtures, wiring Testcontainers, or reducing flaky CI behavior.
qa-testing-playwright
Builds and debugs Playwright E2E suites. Use when authoring browser tests, fixing flakes, or hardening Playwright CI and locator strategy.
qa-testing-strategy
Risk-based test strategy for software delivery. Use when defining coverage, setting CI gates, managing flaky tests, choosing test layers, or establishing release criteria.
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-csharp-backend
Applies C# and .NET backend standards. Use when shaping API boundaries, data access, resilience, observability, or security defaults.
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-security-appsec
Provides application security guidance for design and implementation. Use when reviewing auth, data handling, supply-chain controls, or AppSec 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.
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.
agents-memory
Manages AGENTS.md, CLAUDE.md, and scoped repo rules for Claude Code and Codex. Use when fixing stale memory, ignored instructions, memory audits, or model-upgrade migration.
agents-skills-feedback-loop
Adds per-skill learnings loops for dated patterns, mistakes, and domain facts. Use when wiring skill memory, consolidation, or drift audits.
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-execution-sandbox
Designs execution sandboxes for coding agents. Use when modeling process isolation, filesystem policy, network controls, workspace mounts, or destructive-command boundaries.
ai-coding-agents-observability-evals
Designs coding-agent observability and evals. Use when measuring traces, replay, checkpoint lineage, quality trajectories, tool grading, regression, or cost.
ai-coding-agents-permissions
Designs approval and permission systems for coding-agent runtimes. Use when modeling tool approvals, plan-mode transitions, sandbox prompts, or worker permission handoffs.
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-tasks
Designs task runtimes for Loop Engineering, Graph Engineering, and background work. Use when work needs task lists, cyclic/workflow graphs, cancellation, or teammate coordination.
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-coding-agents
Creates coding agents on Claude Code, Codex, and Agent SDK. Use when defining review, test, refactor, or team agents — not building a runtime.
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-evals
Designs trustworthy LLM, agent, responsible-AI, and multimodal evaluations. Use when measuring quality, fairness, privacy, grounding, safety, or judge reliability.
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-post-training
Post-training and alignment: reward modeling, RLHF/PPO, DPO/DAAs, GRPO, RLVR, RLAIF, over-optimization. Use when adapting an SFT model with preference or verifiable-reward signals.
ai-pretraining
Builds a transformer/GPT and BPE tokenizer from scratch. Use when implementing autograd, self-attention, a nanoGPT-style pretraining loop, or a byte-level tokenizer.
ai-product-operating-model
Designs operating models for AI in product teams. Use when planning platform ownership, provider strategy, data boundaries, evals, or sensitive-data controls.
ai-scaling-laws
Sizes models and token budgets using Kaplan/Chinchilla scaling laws. Use when reasoning about compute-optimal N and D, tokens-per-parameter ratios, or over-training tradeoffs.
ai-vector-brain
Builds vector-brain implementations for repos, docs hubs, and compliance corpora. Use when creating pgvector retrieval brains with scripts, SQL, manifests, and evals.
ai-voice-bots
Builds production voice bots and IVR with Python STT/TTS pipelines. Use when designing telephony, streaming audio, latency budgets, or voice quality monitoring.
data-streaming
Designs streaming platforms for Kafka, Flink, CDC, and lakehouse ingestion. Use when planning event backbones, CDC pipelines, schema governance, or real-time lakehouse delivery.
dev-context-code-graph
Builds per-repo code graphs in JSON and markdown-ready derived artifacts. Use when you need blast radius, symbol-level maps, import graphs, inheritance, or test links.
dev-context-multi-repo
Builds multi-repo context hubs and compiled markdown knowledge maps. Use when profiling repo portfolios or assembling LLM-ready cross-repo knowledge bases.
dev-contribution-quality-analysis
Analyzes commit and PR history to score contribution quality objectively. Use when building engineering scorecards, calibrating promotions, or measuring AI-assist impact.
docs-notes-retrieval
Builds local-first note vault retrieval for Obsidian, markdown notebooks, and NotebookLM-adjacent exports. Use when packaging notes into LLM-ready context.
foundations-ai-planning-search
Applies planning and search theory (A*, CSP, MCTS, STRIPS/PDDL, HTN) to agent design. Use when an LLM agent hallucinates action sequences or needs precondition/effect validity.
foundations-behavioral-economics
16 behavioral-economics primitives for ethical pricing, choice design, and retention. Use when framing, defaults, habits, dark patterns, AI-agent nudging, or nudge ethics apply.
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