geohash-spatial-code-maps
Geohash encoding/decoding in TypeScript or Rust — bit interleaving, the base32 alphabet, precision and cell geometry, neighbour/adjacency computation, proximity and bounding-box queries, and geohash-backed spatial indexing. Also covers the "codebase as a navigable 2D map" pattern — projecting a codebase into a coordinate plane, geohashing it so prefixes become business-domain regions, and navigating it like Google Maps (zoom, tiles, level-of-detail, clustering, deep links). Trigger when implementing, reviewing, or debugging geohash work — even when the user does not say "geohash" but the work involves spatial hashing, Morton/Z-order codes, proximity search on lat/lon, or mapping and visualising code structure spatially. Contains 42 impact-ordered rules with TypeScript and Rust examples.
go-process-cli
Write Go CLIs that manage processes and workloads — daemons, supervisors, job runners, deploy tools, anything that spawns or controls other processes. Use when writing, reviewing, or refactoring Go that handles OS signals, runs children via os/exec, fans out concurrent work, threads context for cancellation, returns exit codes, or builds a flag/cobra command surface. Covers the footguns the standard library makes easy to hit — SIGTERM vs Ctrl-C, SIGKILL-by-default children, orphaned process groups, pipe deadlocks, goroutine leaks, zombie reaping, and os.FindProcess lying about liveness. Triggers whenever the task touches process lifecycle, graceful shutdown, subprocess control, or concurrent workload supervision in Go — even if not named.
human-copywrite
User-facing copy — UI strings, documentation, marketing, release notes, error messages, onboarding flows, blog posts, or anything that a person will read. Distilled from the Apple Style Guide (June 2025): voice and tone (active voice, present tense, second person, no jargon), punctuation, capitalization, hyphenation rules for compound modifiers, when to spell out numbers, units of measure, UI vocabulary (button vs control vs sheet vs popover), Apple product and feature naming, technical notation for developer docs, and international style for localization. Trigger when writing, editing, or reviewing copy — even if the user doesn't explicitly mention \"style guide\" or \"copywriting,\" any time the deliverable is human-readable text the rules here apply. Especially valuable when the user is writing instructions, naming UI elements, or deciding how to format a number, date, or unit.
implementation-design-patterns-python
Implementation guide for the 22 Gang of Four design patterns in idiomatic modern Python (3.10+), distilled from refactoring.guru. Use when writing, refactoring, or reviewing Python with a pattern-shaped problem — class-explosion from inheritance, conditionals switching on type, tight coupling to concrete classes, tree-shaped models, runtime algorithm selection, undo/redo, state-dependent behavior, or hiding subsystem complexity. Each entry leads with the Pythonic form (functions, dataclasses, Protocol, singledispatch, match, generators, copy/replace) and falls back to the class-based GoF structure only when identity, state, or dispatch require it. Includes intent, applicability (when to use AND when NOT to), a runnable example, steps, pros/cons, and relations. Trigger even when no pattern is named — cues like "too many constructor params," "giant if/elif," "swap behavior at runtime," "need undo," or "walk a tree" are pattern-shaped. Covers all 5 Creational, 7 Structural, and 10 Behavioral GoF patterns.
inngest-nextjs-patterns
Inngest event-driven functions in a Next.js (App Router) project — creating the client, the /api/inngest route handler, typed events with Zod schemas, durable event-triggered functions, scheduled cron functions, or fan-out orchestrators. Trigger when scaffolding Inngest patterns — and even if the user just says "add a background job", "process this async", "schedule X every hour", or "send an event when Y happens" in a Next.js codebase. Also trigger when reviewing existing Inngest code to enforce event-naming, function-id stability, and step-idempotency conventions.
io-bound-data-processing
Processing, transforming, or moving datasets that may exceed RAM on a single low-compute box — covers memory discipline (streaming, generators, dtype shrinkage), I/O access patterns (sequential vs random, mmap, async), data formats (Parquet vs CSV vs JSON, predicate pushdown), chunking & batching, spill-to-disk (external merge sort, DuckDB/Polars), pipelining (bounded queues, backpressure, checkpointing), codec selection (zstd/lz4/gzip), concurrency for I/O-bound workloads (asyncio, threads, prefetch), and observability (iowait vs CPU%, rows/sec, py-spy/strace). Trigger on "process a large file", "stream this", "out-of-core", "OOM kill", "this is slow", or code with `pd.read_csv` of multi-GB files, `requests.get(...).content` on big bodies, `BytesIO` on unbounded inputs, per-row INSERTs, sequential `requests.get` loops, falling `tqdm` rates — even if I/O or memory isn't mentioned. Complement to computer-science-algorithms.
language-spec-author
Turn a rough idea for a language into a complete, implementable specification — a DSL, query, config/data, template, or protocol language — by interviewing the author dimension by dimension until another developer could build a conforming implementation from the document alone. It grills for the decisions authors skip: lexical rules (whitespace, case, comments, literals), grammar with precedence and ambiguity resolution, a semantic/type model, validation rules with counter-examples, execution algorithms and the error model, the output/serialization format, and RFC 2119 conformance. The completeness bar and formal notation (lexical vs syntactic grammar, function-style algorithms) are distilled from the GraphQL specification. Trigger on \"spec out my language\", \"design a DSL / query language\", \"write a language or grammar spec\", \"formalize this syntax\", or when someone has a language idea that needs to become an implementable spec — even if they only say \"spec\" or \"grammar\".
library-reference-distillation
Methodology for starting a new library-reference distillation skill — one that turns an external library (nuqs, zod, framer-motion, msw, react-hook-form, emilkowal-animations) into an idiomatic-usage rule pack — or evolving one against a new upstream release. Distills the conventions empirically shared across shipped library-ref skills in this repo — the source-priority ladder (docs → blog/changelog → issues → types → examples), version pinning that inverts with API velocity, the universal 4-tier category ladder (CRITICAL setup → HIGH isolation → MEDIUM composition → LOW edge cases), the 4-slot When-to-Apply template, the failure-gap exemplar heuristic (privilege production lessons over API restatement), and metadata.references[] as cite-set checksum. Triggers on "I want to write a skill for library X", "refresh against new upstream", "where should I source rules from", "what categories should this skill have", and on /dev-skill:new for a library-reference distillation.
linguistic-semantic-algorithms
Mapping out an unfamiliar codebase via NLP and graph algorithms — 40 algorithms across topic modelling, semantic embeddings, code graphs, repository mining, clone detection, IR-based bug localization, identifier linguistics, and complexity metrics. Trigger when hunting bugs across many files, scoping a new feature, identifying domain entities, or analyzing commit history — even if the user doesn't explicitly mention algorithms — apply when they ask "where does X live in this codebase?", "what is this codebase about?", "find duplicated logic", "what changed recently?", "who owns this code?", or "is this function risky?".
marketplace-personalisation
Personalisation and recommendation systems for a two-sided trust marketplace built on AWS Personalize — event tracking, dataset and schema design, two-sided matching, cold start, feedback loops, bias control, recipe selection, serving-time re-ranking, observability, and a diagnostic playbook for existing systems. Trigger when designing, building, debugging, reviewing, or improving such a system — and even when the user does not explicitly mention "AWS Personalize" but is working on recommendations, ranking, search, homepage personalisation, or anything that matches seekers and providers across a trust-based catalog.
marketplace-pre-member-personalisation
Pre-member journey of a two-sided trust marketplace — from anonymous landing through onboarding, registration, and the paid-membership paywall. Covers anonymous signal inference, what pet owners specifically need to validate before paying (safety, availability, competence, effort, local cost comparison), what pet sitters specifically need to validate (opportunity, first-stay path, daily commitment, hidden costs), information-asymmetry closure, progressive profile building, social proof, conversion psychology, onboarding intent capture, identity stitching, and pre-member measurement. Triggers on tasks involving visitor-to-member conversion, anonymous personalisation, onboarding flow design, paywall timing, pre-member ranking, or any question about what a pet owner or pet sitter needs to see before paying. Use this skill BEFORE marketplace-personalisation and marketplace-search-recsys-planning.
marketplace-recsys-feature-engineering
Feature engineering for marketplace recommenders — what to extract from raw marketplace assets (listing photos, owner-entered listing metadata, sitter wizard responses) to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders. Covers asset auditing, first-principles feature decomposition, vision-feature extraction (CLIP, room-type, amenities, aesthetics), listing text and metadata encoding, sitter wizard design, derived-composition patterns for i2i / u2i / u2u (ANN shelves, two-tower, mutual-fit), feature quality governance (training-serving parity, drift, PII), and incremental value proof (ablation A/B, kill reviews, feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
marketplace-search-recsys-planning
Search and recommendation system planning for a two-sided trust marketplace built on OpenSearch — user-intent framing, product-surface architecture, index design, query understanding, retrieval strategy, ranking, search-plus-recs blending, measurement, and a dashboard-and-alerting layer for ongoing decision making. Triggers on tasks involving marketplace search, homefeeds, ranking, relevance tuning, OpenSearch query DSL, analyzers, synonyms, golden sets, NDCG, A/B testing, or diagnosing an existing retrieval system. Use this skill BEFORE marketplace-personalisation when planning new work; hand off when the diagnosed bottleneck is personalisation-specific.
metric-validation-harness
Empirically validates a software metric before trusting or optimizing it — point it at any candidate metric (a command that takes a path and prints one number) plus a corpus, and it runs experiments that try to falsify each property a good metric must have. Checks determinism (same input, same number across runs and hash seeds), invariance to cosmetic edits (also an anti-gaming probe), monotonicity under construct-increasing edits, discrimination, robustness on edge inputs, near-linear tractability, and construct validity (convergent, discriminant vs LOC, predictive AUC, lift over a baseline). Trigger whenever someone proposes, reviews, tunes, or ships a metric, score, or index, asks "is this metric any good", suspects a score tracks LOC or jumps between runs, or builds a deterministic optimization target. It is the empirical companion to the deterministic-metric-design skill and is read-only.
migrate-js-to-modern-typescript
Migrating a JavaScript codebase to TypeScript — converting .js files to .ts, adding types to existing JS, or tightening a loosely-typed TS project toward strict mode. Covers tsconfig and allowJs strategy, incremental strict-flag ratcheting (noImplicitAny, strictNullChecks, noUncheckedIndexedAccess), typing public surfaces, replacing `any` and unsafe casts with `unknown` and narrowing, validating runtime boundaries (JSON, env, API responses), converting CommonJS to ESM and prototypes to classes, and the build/CI changes a migration needs. Trigger even when the user only says "add types", "turn on strict mode", or "convert this file to TypeScript", and especially on a mixed JS/TS repo. Distinct from general TypeScript refactoring — this is the migration act itself, performed file by file while keeping the build green.
mlflow-3
MLflow 3 (open-source, pinned to 3.15) for classic-ML MLOps — logging and registering models, promoting versions across dev/staging/prod, standing up a tracking server, evaluating with gates, and serving. Corrects the MLflow 2-era defaults a model reaches for (artifact_path, registry stages and get_latest_versions, top-level mlflow.evaluate with baseline_model, runs-URI registration, pickle serialization, mlruns file stores, MLServer serving) with the MLflow 3 idioms that replaced them (named LoggedModels, aliases and copy_model_version, models.evaluate plus validate_evaluation_results, skops/torch.export defaults, database backends, the FastAPI scoring server). Use when writing, reviewing, or migrating Python code that touches MLflow tracking, the model registry, evaluation, or serving.
mlflow-mlops-migration
Guided workflow for taking any ML codebase — including one with no experiment tracking at all, or one full of MLflow 2-era idioms — to a production-grade open-source MLflow 3 setup with dev/staging/prod environments, registry-based promotion, and served models. Walks seven phases with a developer who may have zero MLflow 3 experience — assess the codebase (scripted read-only audit), model the registry domain (per-environment model names, aliases, gates), stand up tracking per environment, restructure training code to MLflow 3 idioms, wire evaluation-gated promotion, serve and smoke-test, then run the ongoing MLOps loop. Use when asked to set up MLflow, migrate to MLflow 3, productionize model training and serving, or design a dev/staging/prod MLOps cycle. Pairs with the sibling mlflow-3 rule pack for every API decision.
nextjs-bundle-optimizer
Next.js 16 bundle-size and build-time optimization — runs a data-driven iteration loop: measure baseline → analyze top offenders → apply ONE recipe → re-measure → verify nothing broke (build + types + tests + no regression) → commit or revert. Built for Next.js 16 with Turbopack default, and falls back to webpack-mode tooling when the project hasn't migrated yet. Triggers on phrases like \"First Load JS is huge\", \"bundle size\", \"build takes too long\", \"page is slow to TTI\", \"reduce bundle\", \"tree-shake\", or when the user shares output from `next experimental-analyze` / `@next/bundle-analyzer` — even if the user doesn't say \"optimize\" explicitly.
nextjs-ppr-patterns
Next.js 16 App Router pages mixing static and dynamic content — Partial Prerendering (PPR) under the Cache Components model. Covers enabling it with cacheComponents (the removed experimental.ppr / experimental_ppr flags), the dynamic-by-default rendering inversion, the Suspense static-shell/dynamic-hole boundary, the 'use cache' directive (automatic keys, cacheLife/cacheTag, children/action pass-through, runtime values as props, serverless durability), async runtime APIs and connection() for non-determinism, page composition from a single hole to parallel dashboards to streaming a Promise into a Client Component with use(), and forms/wizards with updateTag read-your-writes and Activity state preservation. Triggers on PPR, cacheComponents, 'use cache', Suspense streaming, partial prerendering, or static-shell work even when not named explicitly.
openai-codex-rust-patterns
OpenAI Codex Rust coding patterns distilled from the codex-rs workspace. Use this skill whenever writing, reviewing, or refactoring Rust code — especially for async agents, CLI tools, sandboxing, secret handling, Ratatui TUIs, JSON-RPC protocols, tokio-based services, or any codebase that needs defensive panic discipline. Trigger even when the user does not explicitly mention Codex, because the patterns generalize to any production Rust workspace. Covers async cancellation, error enum design, process sandboxing, DNS-rebinding defense, credential hardening (zeroize/mlock/ctor), Cargo workspace architecture, wiremock-based fakes, insta snapshot testing, OpenTelemetry tracing, and Ratatui rendering.
opencode-ts
Write and refactor TypeScript code in repos that use Effect-TS services, Effect Schema, event-sourced persistence, and barrel-module architecture. Use this skill when implementing features, fixing bugs, writing tests, or refactoring in opencode or any TypeScript codebase built on the same stack (Effect DI via Context.Service, Drizzle ORM, Hono routes, Bun runtime). Triggers on tasks involving Effect services, Context.Service / Layer modules, Effect Schema definitions, SyncEvent patterns, tool implementations, test writing, or code review in Effect-based TypeScript projects.
opensearch-function-scoring-algorithms
Search relevance and ranking on OpenSearch/Elasticsearch for a two-sided marketplace — candidate retrieval (hybrid BM25 + kNN, RRF, two-tower EBR), base relevance (BM25F, multi_match, LambdaMART), quality signals (Wilson lower bound, Bayesian average, rank_feature saturation/sigmoid), personalization (listing/user/session embeddings), spatial/temporal decay (gauss/exp), marketplace balance (conversion-weighted ranking, supply fairness, Pareto multi-objective), bias correction (IPS, click models, Thompson sampling), empirical evaluation (judgment sets, NDCG, ablation, A/B sizing, CUPED, regression suites), and diversity (MMR, DPP, max-per-host). Triggers on function_score, rank_feature, script_score, kNN, hybrid query, learning-to-rank, two-sided ranking, exposure fairness, NDCG, A/B testing, judgment set construction, ranking ablation, or "why is my OpenSearch ranking bad". Applies to Elasticsearch too — same APIs.
opensearch-personalize-caching-strategies
Caching strategies in front of AWS OpenSearch (Elasticsearch) or AWS Personalize — search, recommenders, multi-recommender pages, anon vs logged-in traffic. Covers ROI decision (TPS/minProvisionedTPS, Zipf, amplification), key design (canonicalisation, cohort vs user, solution-version pinning, bucketing), personalisation boundary (anon/logged split, fan-out coalescing), strategies (cache-aside, refresh-ahead, write-through, batch precompute, L1+L2), TTL (volatility, soft/hard, jitter, event-driven invalidation), stampede protection (single-flight, XFetch, stale-while-revalidate, circuit breaker), observability (hit-rate, cost-per-1k, cardinality drift, log-replay), defensive caching (negative, Bloom filter), and tier composition (LRU, ElastiCache Redis, CloudFront, OpenSearch request/filter cache). Triggers on cache hit rate, Personalize throttling, stampede, single-flight, L1/L2, ElastiCache sizing. Complements opensearch-function-scoring-algorithms.
opinionated-nextjs-patterns
Opinionated, backend-agnostic Next.js 16 (App Router) architecture — authorization at the data layer, server-side loading with cache()+Promise.all, mutations through next-safe-action + typed route handlers, client/server boundaries ('use client' at leaves + TanStack Query), forms with RHF + Zod, UI via shadcn/ui + Tailwind + Base UI + next-intl, request handling in proxy.ts (Next.js 16's renamed middleware), and a Turbo monorepo of @app/* packages confining the backend behind one data-access package. Examples use Supabase but every rule states the transferable principle. Use when writing, reviewing, or refactoring Next.js 16 code. Trigger on server actions, route handlers, RSC vs 'use client' placement, TanStack Query, RHF/Zod, proxy.ts, or monorepo package layout — even when the user doesn't say 'patterns' or 'best practices'.
pt-irs-runbook
Portuguese personal income tax (IRS / Código do IRS) and Modelo 3 filing — determining tax residency (Art. 16), classifying income by category (rental/Anexo F, capital gains/Anexo G, employment/Anexo A, self-employment/Anexo B, pensions, foreign income/Anexo J), choosing the right annexes, applying current Art. 72 autonomous rates, and meeting Portal das Finanças deadlines. Covers residents and non-residents, including landlords with Portuguese-source rental income and double-taxation relief. Portugal-specific; not a substitute for a contabilista certificado.
python-pep-author
Drafting Python Enhancement Proposals (PEPs) — proposing a Python language feature, a standard library change, an interoperability standard, or an informational/process document for the Python community. Covers all three PEP types (Standards Track, Informational, Process): choosing the right type, scaffolding a valid reStructuredText pep-NNNN file, filling each required section to the acceptance bar, linting the headers, and navigating the sponsor / submission / Steering Council review process. Trigger on \"write a PEP\", \"draft a PEP\", \"propose a Python feature\", \"create a Python Enhancement Proposal\", \"PEP template\", or when someone is preparing a proposal for the Python Discourse or python-dev. Use it even if the user only says \"PEP\" or doesn't mention reStructuredText — the headers, sections, and process are exactly what trips PEP authors up.
ray-llm
LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch inference with ray.data.llm (build_processor). Corrects the stale defaults a model produces — the archived ray-llm repo and its YAML configs, hand-rolled vLLM engines inside plain Serve deployments, the removed build_llm_processor name, deprecated boolean stage flags, top-level LLMServer/LLMRouter imports, free-form accelerator strings, one-deployment-per-LoRA-adapter designs — with the 2.57 idioms (stage configs, placement_group_config over hand-rolled PGs, deployment_config autoscaling, dynamic LoRA multiplexing, prefix-cache-affinity routing, the full OpenAI endpoint surface). Use when writing, reviewing, or productionizing LLM serving or batch inference on Ray. Classic-ML Ray (Train/Tune/Data/Serve/clusters) lives in the sibling ray skill.
ray
Production Ray (open-source, pinned to 2.57) for classic-ML workloads from training to serving — Ray Train, Tune, Data, Serve, Core, and cluster deployment on KubeRay. Corrects the older-corpus defaults a model reaches for (ray.air session reporting, Trainer-inside-Tuner, tune.run, map_batches concurrency=, DatasetPipeline/to_torch, max_concurrent_queries, RayServeHandle + ray.get, Deployment.deploy, ray.state, ray.get-in-a-loop) with the 2.57 idioms that replaced them (Train V2 defaults, driver-function tuning, compute strategies, streaming datasets, DeploymentHandle/DeploymentResponse, serve build/deploy, ray.util.state, KubeRay CRDs and Jobs API). Use when writing, reviewing, or productionizing Python code that touches Ray distributed training, data pipelines, hyperparameter tuning, model serving, or Ray cluster operations. LLM serving/batch-inference on Ray lives in the sibling ray-llm skill.
react-fetch-cache-patterns
React data-fetching patterns at scale — recommender carousels, infinite feeds, pages with many parallel fetches, dashboards. Covers request orchestration (parallelism, batching, deduplication), cache strategy (keys, normalization, staleTime, SWR), backend protection (concurrency caps, debounce/throttle, jittered retries, circuit breakers), prefetching (route loaders, hover/intent, idle, server hydration), failure resilience (AbortController, timeouts, error boundaries, stale fallback, idempotent mutations), and feed/carousel patterns (virtualization, cursor pagination, summary/detail split). Includes 5 ready-to-use scaffolding templates (resource query hook, carousel data loader, infinite feed, hover-prefetch link, request collapser). Trigger when building, reviewing, or refactoring React components that fetch data — even if the user doesn't explicitly mention "performance" or "scale".
relational-data-modeling
Corrects the wrong defaults a model has when designing a relational schema — the DDL decisions an experienced engineer makes differently. Use when creating or reviewing tables, migrations, ER models, or ORM schema definitions. Covers identity and keys (surrogate vs natural, identity vs serial, uuidv7, composite keys that make cross-tenant references impossible), relationships (polymorphic foreign keys the database cannot enforce, referential actions, unindexed FK columns, disjoint subtypes), invariants the engine can prove instead of application code (EXCLUDE, partial unique indexes, CHECK limits, deferrable cycles, NOT VALID), types (timestamptz, exact money, range types, enum vs lookup table), derived and encoded data (generated columns, JSONB as an escape hatch), and time (events vs in-place updates, soft-delete flags that silently disable constraints, temporal keys). NOT for query tuning, index selection for read paths, or connection pooling.
relational-database-design
Distills a logical relational-database design methodology into rules an agent applies while designing or reviewing a schema. Covers the design sequence (mission → tables → fields → keys → relationships → business rules → views → integrity review), one-subject-per-table decomposition, atomic single-valued fields, candidate/primary/foreign keys, relationship types with deletion rules and participation, the four levels of data integrity, database-vs-application business rules, validation tables, views for derived data, and the flat-file / spreadsheet / RDBMS-driven antipatterns to avoid. The structure is logical and RDBMS-agnostic, and normalized by construction. Use when designing a new relational schema, reviewing or refactoring an existing one, resolving redundant or repeating data, choosing keys, modeling relationships, or deciding where a constraint belongs.
same-results-less-code
Same behaviour in fewer, clearer lines — covers the judgment gaps that linters cannot catch (reinvention, wrong frame, hidden duplication, derived state, procedural rebuilds, speculative generality, defensive excess, type-system underuse). Trigger when reviewing, refactoring, or simplifying code — and even when the user doesn't explicitly ask for "simplification" but is reviewing code, refactoring, or asking "is there a shorter way to write this?". Complements knip/eslint/ruff/tsc by focusing on the conceptual modelling layer those tools cannot see.
playwriter
Control the user own Chrome browser via Playwriter extension with Playwright code snippets in a stateful local js sandbox. Use this over other Playwright MCPs to automate the browser — it connects to the user's existing Chrome instead of launching a new one. Use this cli for navigating JS-heavy websites (Instagram, Twitter, cookie/login walls, lazy-loaded UIs) instead of webfetch/curl. ALWAYS load this skill before using any playwriter commands
sensitive-browser
Execute sensitive browser actions (login, payments, form filling) outside the core agent loop using a dedicated CLI tool. Use when Claude needs to handle credentials, payment information, or other sensitive data in browser automation workflows. Triggers when users ask to log into websites, fill payment forms, or perform authenticated browser actions where sensitive data must be kept secure and separate from the main agent context.
winmd-api-search
Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI/ML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).
webapp-testing
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
doublecheck
Three-layer verification pipeline for AI output. Extracts verifiable claims, finds supporting or contradicting sources via web search, runs adversarial review for hallucination patterns, and produces a structured verification report with source links for human review.
breakdown-plan
Issue Planning and Automation prompt that generates comprehensive project plans with Epic > Feature > Story/Enabler > Test hierarchy, dependencies, priorities, and automated tracking.
csharp-async
Get best practices for C# async programming
copilot-sdk
Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.
cli-mastery
Interactive training for the GitHub Copilot CLI. Guided lessons, quizzes, scenario challenges, and a full reference covering slash commands, shortcuts, modes, agents, skills, MCP, and configuration. Say "cliexpert" to start.
breakdown-epic-arch
Prompt for creating the high-level technical architecture for an Epic, based on a Product Requirements Document.
java-docs
Ensure that Java types are documented with Javadoc comments and follow best practices for documentation.
java-junit
Get best practices for JUnit 5 unit testing, including data-driven tests
java-springboot
Get best practices for developing applications with Spring Boot.
java-mcp-server-generator
Generate a complete Model Context Protocol server project in Java using the official MCP Java SDK with reactive streams and optional Spring Boot integration.
php-mcp-server-generator
Generate a complete PHP Model Context Protocol server project with tools, resources, prompts, and tests using the official PHP SDK
first-ask
Interactive, input-tool powered, task refinement workflow: interrogates scope, deliverables, constraints before carrying out the task; Requires the Joyride extension.
fluentui-blazor
>
write-coding-standards-from-file
Write a coding standards document for a project using the coding styles from the file(s) and/or folder(s) passed as arguments in the prompt.
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