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Skills in this repository
SkillsMP has collected 1,540 skills from MadewellRD/skills-lab. Open a skill to review its source and details.
MadewellRD/skills-labShowing 40 of 1,540 collected skills.
design web information architecture including sitemap, route hierarchy, navigation, url taxonomy, content relationships, wayfinding, content hierarchy, and findability. use when Claude needs to structure websites, portals, dashboards, docs sites, ecommerce…
create web-specific product requirements, page and route scope, user journeys, acceptance criteria, success metrics, analytics intent, source facts, risks, open questions, and downstream handoff notes for websites, web apps, landing pages, portals,…
define ux/ui design system guidance for web surfaces including component inventory, responsive behavior, design tokens, interaction states, accessibility-aware patterns, brand consistency, and design governance. use before frontend implementation or…
orchestrate complete web development workflows across product, information architecture, ux/ui design systems, frontend engineering, backend integration, cms/content operations, web security/secops, performance, accessibility, seo, testing, observability,…
coordinate post-launch web maintenance and growth including analytics-informed backlog, experiments, conversion optimization, content refresh, seo iteration, accessibility remediation, performance regression follow-up, dependency upgrades, refactors,…
design production web observability including rum, synthetic checks, availability, frontend errors, api errors, latency, core web vitals, analytics events, dashboards, alerts, ownership, incident hooks, launch monitoring, and post-launch review. use before…
plan and gate web performance using core web vitals, performance budgets, rendering cost, hydration, bundle size, images, fonts, scripts, cdn, caching, data fetching, latency, field and lab measurement, launch thresholds, and regression controls. use before…
plan web release and deployment across ci/cd, preview environments, build commands, environment promotion, hosting, cdn, edge config, cache invalidation, feature flags, launch checklist, rollback, post-release validation, and release-to-observability handoff.…
review and plan web security and secops controls across auth, sessions, browser storage, cookies, csp, security headers, cors, csrf, dependency risk, secrets, third-party scripts, cdn/edge hardening, abuse prevention, monitoring hooks, and incident readiness.…
define web testing and qa strategy across browsers, devices, responsive layouts, visual regression, forms, auth flows, integrations, accessibility checks, seo checks, performance checks, smoke tests, regression tests, release signoff, and defect triage. use…
design AI agent architecture including planning boundaries, execution loops, memory and state strategy, tool routing, approval gates, retries, delegation, and halt behavior.
design observability for AI agents and workflows including traces, prompts, model calls, tool calls, retrieval events, approvals, errors, eval probes, cost, latency, and safety signals.
orchestrate AI engineering workflows from capability intent through model, prompt, tool, agent, retrieval, eval, safety, inference, observability, release, and incident stages using connector-grounded evidence, workflow packets, stage advancement, and halt…
triage AI production incidents involving hallucination spikes, safety failures, prompt injection, tool misuse, data leakage, model regressions, cost spikes, latency degradation, eval regressions, or user harm reports.
assess readiness to release AI capabilities across requirements, evals, safety review, red-team status, inference ops, observability, rollback, docs, support handoff, and owner approval.
review AI capability risks including misuse, policy compliance, privacy, security, hallucination harm, data leakage, autonomy, tool-use risk, user impact, and mitigations.
optimize AI system cost and latency using model routing, caching, prompt compression, context pruning, batching, streaming, parallelism, retrieval tuning, and fallback tiers while preserving quality and safety gates.
plan and review AI datasets for source selection, labeling, balancing, privacy, deduplication, train and eval splits, drift, provenance, consent, and retention.
design AI evaluation plans with goals, datasets, rubrics, grading methods, thresholds, regression slices, safety checks, human review, and reporting requirements.
analyze completed AI eval runs, regression deltas, failure clusters, grading reliability, threshold status, release blockers, and rerun recommendations.
assess and plan fine tuning only when prompt, retrieval, tool, model routing, and eval evidence justify training a specialized model.
plan production inference operations including deployment topology, rate limits, quotas, retries, caching, streaming, fallbacks, batching, timeouts, secrets, logging, and SLOs.
select model candidates, routing constraints, fallback behavior, and model tradeoffs for AI capabilities using task fit, quality, latency, cost, safety, modality, context, and deployment evidence.
design prompt systems, instruction hierarchy, context assembly, prompt contracts, refusal and defer behavior, prompt evaluation fixtures, prompt injection defenses, and prompt observability hooks for AI capabilities.
plan and analyze adversarial AI testing for jailbreaks, prompt injection, data exfiltration, harmful instructions, over-permissioned tools, and policy evasion.
design retrieval augmented generation systems with indexing, chunking, embeddings, ranking, filters, citations, freshness policy, permission filtering, and grounding behavior.
design synthetic data generation workflows with seed examples, constraints, diversity targets, contamination controls, review loops, and validation gates.
design AI tool schemas, resource contracts, permission boundaries, argument validation, idempotency rules, error semantics, and result contracts for agentic workflows.
prepare Android native app implementation plans for Kotlin, Java, Jetpack Compose, View systems, modularization, storage, networking, background work, sensors, permissions, and platform APIs.
design Android app and game architecture, module boundaries, data flow, offline behavior, engine integration, services, APIs, migrations, and ADR-ready decisions.
define Android service and API integration, auth, sync, payments, push notifications, analytics, remote config, multiplayer, leaderboards, cloud saves, retries, offline behavior, and failure modes.
orchestrate complete Android app and game development workflows across discovery, product, architecture, implementation, testing, release, Play Store operations, live ops, and maintenance. use when the user wants to plan, build, validate, launch, operate,…
prepare Android game implementation plans for AGDK, NDK, C/C++, Unity, Unreal, Godot, custom engines, rendering, input, assets, frame pacing, and gameplay/runtime constraints.
plan Android maintenance, dependency upgrades, SDK target updates, deprecations, Play policy changes, experiments, monetization iteration, store optimization, retention, and technical debt.
define Android observability and live ops for crash reporting, logs, metrics, analytics events, alerts, feature flags, remote config, game economy/events, rollout monitoring, and incident response.
plan Android performance for startup, memory, battery, ANR and crash risk, rendering, frame pacing, asset loading, Macrobenchmark, Baseline Profiles, profiling, and device-tier budgets.
define Android app and game product requirements, audience, platform targets, acceptance criteria, non-goals, risks, Play constraints, monetization assumptions, and open questions.
plan Android builds, signing, versioning, CI/CD, AAB/APK packaging, internal testing, Play tracks, release notes, staged rollout, rollback, Play Asset Delivery, and store listing readiness.
review Android security, privacy, permissions, secrets, Play policy risk, data safety, secure storage, anti-tamper, networking, dependency risk, and abuse controls.
inspect Android repo, Gradle, SDK, NDK, dependency, manifest, device, emulator, engine, CI, feasibility, constraint, and unknown facts before implementation.