Results for “prompt-design”

Routes requests to design, create, debug, update, or upgrade GitHub Agentic Workflows by loading the appropriate workflow prompt or skill file.

Routes requests to design, create, update, debug, or upgrade GitHub Agentic Workflows by loading the appropriate workflow prompt or skill file.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Use the @steipete/oracle CLI to bundle a prompt plus the right files and get a second-model review (API or browser) for debugging, refactors, design checks, or cross-validation.

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

Generates images from text prompts using Google Gemini's image generation model, saving them as PNG files for use in UI, documentation, and design projects.

Generates images from text prompts using Google Gemini's image generation model, saving them as PNG files for use in UI, documentation, and design assets.

Design and author reusable subagents for specialized AI tasks. Use when the task needs an isolated agent persona, scoped system prompt, or reusable domain-specific assistant rather than a general skill.

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Routes engineering design, delivery, reliability, security, operations, and maintenance prompts to a router skill backed by specialist guidance for multiple AI coding agents.

Generates images from text prompts using Google Gemini's image generation model, saving them as PNG files for use in UI, documentation, and design assets.

Generates images from text prompts using Google Gemini's image generation model, saving them as PNG files for use in UI, documentation, and design assets.

Generates images from text prompts using Google Gemini's image generation model, saving them as PNG files for use in UI, documentation, and design projects.

Clarifies ambiguous or conflicting requests by researching first, then asking only judgment calls. Use when prompts say "grill me", ask hard questions, request relentless interrogation, pressure-test assumptions, clarify scope/requirements, define success criteria, or request system-design/optimization decisions.

Diagnose whether an AI workflow suffers from context stuffing or benefits from context engineering, and apply structured techniques to improve reliability.

Audits any movemental.com page across UI, content, architecture, UX, and conversion, then writes a markdown report and a standalone fix prompt.

Use when designing AI product first-run or blank-slate UX — chat empty state, example prompts, capability discovery, templates, nudges, or follow-ups. Trigger on "users don't know what to ask", onboarding, suggestion chips, or discoverability.

Design a diary study plan with prompts, duration, participant criteria, and analysis framework to understand user behavior over time in natural contexts.

Designs command-line interface parameters and UX, including arguments, flags, subcommands, help text, output formats, error messages, exit codes, prompts, config/env precedence, and safe/dry-run behavior.

Use when driving a feature prompt to a production-ready PR through discovery, definition, design, spec, issues, and dev phases, or when the user runs /feature or /feature resume — orchestrates the A-Team agentic pipeline over a target repo.

Route LangSmith work into one workflow packet before touching SDK code. Use when the user needs LangSmith tracing, offline evals, annotation/review queues, prompt-registry decisions, audit/gap review, or cross-service trace propagation for an LLM app or agent workflow. Choose one packet: trace-debug, eval, review, prompt-registry, propagation, or audit. Triggers on: LangSmith, LangChain tracing, `@traceable` / `traceable`, `wrap_openai` / `wrapOpenAI`, datasets, experiments, annotation queues, feedback criteria, Prompt Hub, run trees, trace IDs, or production confidence for an AI feature. Not for generic SLO/alert design, non-LangSmith deployment orchestration, or runtime guardrails outside LangSmith.

Uses a multi-agent generator-evaluator feedback loop to build high-quality applications from a single prompt, inspired by GANs and Anthropic's harness design.

Use when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.

Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and prediction monitoring. Use when asked to "deploy a model", "model registry", "MLflow", "feature store", "drift detection", "retrain trigger", "shadow mode", "model versioning", "serving infrastructure", or "ML pipeline". Do NOT use for: prompt engineering or RAG pipelines — see prompt-engineering and rag-architect skills. Do NOT use for: general API deployment without an ML component.

Equipment manager for Claude Code projects — inventory local skills/agents/scripts, audit external upstream repos (red-flag + prompt-injection screening), and sync selectively after user approval. Absorbs container-layout skill repos AND single-skill-at-root repos (e.g. a company design-system repo). Project-agnostic.

Builds reusable specialized skills for creating research-paper figures from lawful source material, then uses those skills to design and render figures for target papers.

Use for designing, auditing, or refactoring a GitHub Copilot customization system in Visual Studio Code across instructions, prompt files, Agent Skills, custom agents, hooks, MCP servers, and plugins. Do not use merely to author one already-selected artifact or configure unrelated VS Code settings.

Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

Caching strategies across the RAG stack. Semantic caching with GPTCache and LangChain, Redis-based embedding-similarity cache, cache key design, TTL/invalidation, partial caching (cache retrieval only), provider-native prompt caching (Anthropic, OpenAI), and hierarchical L1/L2 caches. USE WHEN: user mentions "semantic cache", "GPTCache", "LLM cache", "prompt caching", "Redis vector cache", "cache invalidation for RAG", "reduce LLM cost", "latency reduction LLM" DO NOT USE FOR: retrieval accuracy - use `rag-patterns`; groundedness checks - use `rag-guardrails`; incremental indexing - use `rag-production`

Designs and governs recursive self-improvement loops where an agent mines its own failures and proposes edits to its own harness, prompts, or workflow, covering acceptance gates, diversity preservation, and the optimization ladder.

Quantifies how sensitive a language model benchmark's reliability and ranking stability are to specific design choices, such as the selection of scenarios, subscenarios, examples, and few-shot prompts. Use when the user has predictions and gold and needs to compute DIoR.

Diagnose and fix retention problems using behavior design (B=MAP). Use when the user mentions "users drop off", "activation rate", "onboarding friction", "retention metrics", "why users dont complete", "churn analysis", "user activation", or "aha moment". Also trigger when analyzing cohort retention curves, designing activation milestones, reducing time-to-value for new users, or investigating why users stop after their first session. Covers the Ability Chain, prompt design, and tiny behaviors that compound. For habit loops and variable rewards, see hooked-ux. For intrinsic motivation, see drive-motivation.

Generate and validate music with Google Lyria 3 through the Gemini Interactions API. Use before calling OpenMontage `google_music`, designing Lyria 3 Clip or Pro prompts, using image-to-music or custom lyrics, choosing between Lyria 3 and Lyria RealTime, diagnosing Google music-generation failures, or preparing exact-duration music for a video.

Reverse-lookup glossary that turns a vague description of a web animation or motion effect into its exact term ("the bouncy thing when a popover opens" → Pop in; "the iOS rubber-band scroll" → Rubber-banding). Use when the user asks "what's it called when…", or describes a motion effect without knowing its name and wants the right word to prompt an AI or designer with. For naming an effect, not designing or building one. Source: github.com/emilkowalski/skills.

Skilled agent architecture replacing multi-agent system for RL training. Trigger when: (1) planning agent-guided training, (2) implementing tool-augmented LLM consultations, (3) comparing skilled vs multi-agent approaches, (4) designing simulate-verify loops for training, (5) implementing prompt evolution / learnable parameters, (6) understanding Claude Agent SDK integration in training, (7) debugging SkilledTrainer consultations or tool calls, (8) configuring agent safety bounds for training actions.