Results for “pr”

Idiomatic Kotlin patterns, best practices, and conventions for building robust, efficient, and maintainable Kotlin applications with coroutines, null safety, and DSL builders.

Cognitive enhancement toolkit combining non-linear reasoning, mental models, and literate programming. Use for complex analysis, system design, debugging, deep learning, and research workflows.

Next.js version migrations using official guides and codemods. Use when migrating a Next.js project to a new major version using codemods.

Implement data access patterns, Scaling, Migrations, and ORM selection in NestJS. Use when implementing TypeORM/Prisma repositories, migrations, or database patterns in NestJS.

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Git workflow patterns including branching strategies, commit conventions, merge vs rebase, conflict resolution, and collaborative development best practices for teams of all sizes.

Signal-based outbound specialist who designs multi-channel prospecting sequences, defines ICPs, and builds pipeline through research-driven personalization — not volume.

Idiomatic Kotlin patterns, best practices, and conventions for building robust, efficient, and maintainable Kotlin applications with coroutines, null safety, and DSL builders.

Comprehensive Perl security covering taint mode, input validation, safe process execution, DBI parameterized queries, web security (XSS/SQLi/CSRF), and perlcritic security policies.

Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific patterns.

Design product-led growth strategies with self-serve funnels and expansion revenue. TRIGGERS - Use when user needs help with plg-strategy related tasks.

Resize a product photo to Shopify's recommended 2048×2048px square format, sharpen, and convert to WebP for fast storefront load times.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.

Read, create, and convert Microsoft PowerPoint (.pptx) presentations — extract slide text to JSON/markdown, build decks from JSON/markdown outlines, and export to PDF.

Scrum master for sprint planning and story preparation. Use when the user asks to talk to Bob or requests the scrum master.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

Design product-led growth strategies with self-serve funnels and expansion revenue. TRIGGERS - Use when user needs help with plg-strategy related tasks.

Use when you need to characterize score distributions of a profile HMM on random sequences, such as calibration checks, benchmarking, or filter-behavior experiments.

Use when working with GenBank format files and need to create reference indexers for sequence data retrieval or processing within the Entrez Direct toolkit.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.

Responding to incidents: identifies impact scope, formulates recovery procedures, creates postmortems. Use when incident response or disaster recovery is needed. Delegates fixes to Builder.

Review test quality using best practices validation. Use when user says "lets review tests" or "I want to evaluate test quality"

Researches federal opportunities, competitors, incumbents, recompetes, teaming partners, agencies, markets, and pricing context to support evidence-based GovCon capture and growth decisions.

Use when user asks to "build a Solana dapp", "write an Anchor program", "create a token", "debug Solana errors", "set up wallet connection", "test my Solana program", "deploy to devnet", or "explain Solana concepts" (rent, accounts, PDAs, CPIs, etc.). Also use for quick on-chain lookups via public RPC + curl — "what's the balance of <wallet>", "look up transaction <sig>", "token balance for <account>", "check this address on mainnet/devnet". End-to-end Solana development playbook covering wallet connection, Anchor/Pinocchio programs, Codama client generation, LiteSVM/Mollusk/Surfpool testing, security checklists, and JSON-RPC curl lookups against public clusters. Integrates with the Solana MCP server for live documentation search. Prefers framework-kit (@solana/client + @solana/react-hooks) for UI, wallet-standard-first connection (incl. ConnectorKit), @solana/kit for client/RPC code, and @solana/web3-compat for legacy boundaries.

Full architectural preferences and skill routing map for Sage/Acorn projects — complete workflow ecosystem reference, Lando command tables, design tool URL routing (Paper/Stitch/Figma/Pencil/Playwright), MCP query patterns, discover-abilities, execute-ability, WordPress MCP Adapter, plan system structure, integration with base superpowers skills. Invoke for: "which skill should I use", "architectural preferences", "design tool routing", "mcp query patterns", "full skill list", "sage ecosystem overview", "when to use Livewire vs Blade", "Acorn vs WordPress pattern decision". Skip when: the session-start routing table already covers your decision — invoke the target skill directly instead of loading this overview first.

Coordinate agents via the AMQ CLI for file-based inter-agent messaging. Use this skill whenever you need to send messages to another agent (codex, claude, or any named handle), check your inbox, drain queued messages, set up co-op mode between agents, join a swarm team, route messages across projects, or diagnose delivery issues. Also use it when you receive a message and need to know how to reply, inspect receipts, or handle priority. Covers any multi-agent coordination task where agents need to talk to each other — review requests, questions, status updates, decision threads, wake notifications, and orchestrator integration (Symphony, Kanban). For collaborative spec/design workflows specifically, prefer the /amq-spec skill which provides structured phase-by-phase guidance. Not intended for distributed systems design (RabbitMQ, Kafka), CI/CD pipelines, or single-agent tasks with no partner.

Research markets with sizing, segmentation, competitor mapping, pricing checks, and demand validation that turn fuzzy ideas into decision-ready evidence. Use when (1) you need TAM, SAM, SOM, whitespace, or category sizing; (2) you must compare competitors, pricing, positioning, or customer segments before acting; (3) the user asks whether a niche, launch, expansion, or go-to-market bet is actually worth pursuing.

Coordinate multi-agent development pipelines using shared JSON task files. Use when dispatching work across dev team agents (code-agent, test-agent, docs-agent, monitor-bot), tracking pipeline progress, or running sequential/parallel workflows. Covers project init, task assignment, status tracking, agent dispatch via sessions_send, and result collection. Supports two modes: linear (sequential pipeline) and dag (dependency graph with parallel execution).

Use when planning an ACM PODS project timeline across its multiple submission cycles per year, from venue fit through the EasyChair abstract-then-paper deadlines, the 48-hour rebuttal, the accept/reject/revision decision and shepherded revision round, PACMMOD-track publication, the arXiv full version, and presentation — with backward-planning offsets for a theory paper and honest handling of cycle choice.

Docker and container development agent skill and plugin for Dockerfile optimization, docker-compose orchestration, multi-stage builds, and container security hardening. Use when: user wants to optimize a Dockerfile, create or improve docker-compose configurations, implement multi-stage builds, audit container security, reduce image size, or follow container best practices. Covers build performance, layer caching, secret management, and production-ready container patterns.

Terraform infrastructure-as-code agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw. Covers module design patterns, state management strategies, provider configuration, security hardening, policy-as-code with Sentinel/OPA, and CI/CD plan/apply workflows. Use when: user wants to design Terraform modules, manage state backends, review Terraform security, implement multi-region deployments, or follow IaC best practices.

Design and implement SLIs, SLOs, and error budgets for production services. Covers SLI selection, SLO target-setting, error budget calculation, burn-rate alerting, and error budget policy. Use when asked about "SLO", "SLI", "error budget", "reliability targets", "burn rate", "uptime SLA", or when designing alerting for a production service. Do NOT use for: application-level performance optimization — use `web-performance` for frontend.

Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.