Results for “writing”

Test local web applications by writing native Python Playwright scripts, with helpers for server lifecycle management and a reconnaissance-then-action pattern for dynamic UIs.

Provides best practices for writing JavaScript/TypeScript tests using Jest, including test structure, mocking strategies, async testing, snapshot testing, and React component testing.

Plan and govern product content: audit existing material, define content models, establish voice and tone, and set ownership workflows.

Runs a multi-agent academic research pipeline covering systematic review, LaTeX paper writing, peer review, and end-to-end orchestration with integrity gates.

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

Use this first, before any writing, to stress-test whether a clinical study clears NEJM's bar — practice-changing clinical impact, methodological rigor, and generalizability. Decides NEJM vs Lancet/JAMA vs a specialty journal.

Use to structure a Cell Article and hold its length — Summary, Introduction, Results, Discussion, STAR Methods, figure legends, references — with main-text length and display-item budgets, and Results subheadings driving the single arc.

Conversion-focused product copy for Nick's apps. Use when writing landing page copy, CTA text, onboarding copy, trust-building messaging, feature explanations, pricing language, or microcopy that needs to drive clarity and action.

Extends pandas for geospatial vector data operations including reading/writing shapefiles, GeoJSON, and GeoPackage, performing spatial joins, coordinate transformations, and creating static or interactive maps.

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

REST API design — resource naming, HTTP methods, status codes, pagination, versioning, OpenAPI. Use when designing a new REST API, reviewing API contracts, writing OpenAPI/Swagger documentation, or choosing pagination, versioning, or error-format strategies.

Redux middleware — the `store => next => action => {}` pipeline for side effects, logging, analytics, and async (thunk/saga). Use when writing custom middleware, wiring async thunks, ordering middleware in configureStore, or keeping side effects out of reducers.

Plan new YouTube videos with optimized titles, thumbnails, and hooks. Use when planning a video, writing or reviewing opening hooks, generating title and thumbnail concepts, or creating a production-ready video plan backed by research.

Rust documentation practices for HASH codebase. Use when writing doc comments, documenting functions/types/traits/modules, creating error sections, using intra-doc links, following rustdoc conventions, or exploring crate APIs with cargo doc.

Guides Claude in writing idiomatic, efficient, well-structured Rust code using proper data modeling, traits, impl organization, macros, and build-speed best practices. TRIGGER when working on .rs files or Rust projects (Cargo.toml present).

Generate user-facing release notes from tickets, PRDs, or changelogs. Creates clear, engaging summaries organized by category (new features, improvements, fixes). Use when writing release notes, creating changelogs, announcing product updates, or summarizing what shipped.

Remove signs of AI-generated writing from academic medical papers. Use when editing or reviewing manuscripts to make them sound more natural and professionally written. Based on Wikipedia's "Signs of AI writing" guide, adapted for medical literature. Detects and fixes patterns including: inflated significance claims, superficial -ing analyses, vague attributions, AI vocabulary words, copula avoidance, excessive hedging, generic conclusions, informal word choices (linked/beyond/via/where/yield), overly assertive causal claims, and artificially condensed expressions. Preserves legitimate academic transitions (Notably, Prior studies have shown, etc.).

Canonical patterns for writing custom MSBuild targets, covering the three-level target chain, chain extension, DependsOnTargets vs BeforeTargets vs AfterTargets, Returns vs Outputs, and common pitfalls.

Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC with agency-specific formatting, review criteria, budget preparation, and compliance guidance.

Builds a reusable writing style profile from real source material and applies it across content, outreach, and social workflows for consistent brand voice.

Writes mechanism-first technical implementation deep-dive articles that explain design intent, data flow, trade-offs, and operations, with code locations as supporting evidence.

Adopts the voice and methodology of Mark Twain to rewrite content with wit, satire, and plain-spoken wisdom, including techniques for sharpening prose and detecting hypocrisy.

Test Parallelizer - Auto-activating skill for Test Automation. Triggers on: test parallelizer, test parallelizer Part of the Test Automation skill category. Use when writing or running tests. Trigger with phrases like "test parallelizer", "test parallelizer", "test".

Use when a draft in Turkish, English, or both reads as AI-generated and needs revision that removes machine writing patterns and translation calques while preserving meaning, citations, statistics, and the author's voice.

Provides the single source of truth for brand guidelines, design tokens, technology choices, and voice/tone. Use this skill whenever generating UI components, styling applications, writing copy, or creating user-facing assets to ensure brand consistency.

Write effective unit tests with best practices for any project. Use when writing tests, creating test cases, improving test coverage, increasing confidence before release, or when the user mentions testing. Mutation score / assertion theater → test-mutation.

Alert design and on-call practices — severity, runbooks, SLO burn-rate alerting, escalation. Use when designing alerts, writing runbooks, reducing alert fatigue, setting up escalation policies, implementing SLO-based alerting, or improving on-call processes.

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX writing.

Create job stories using the 'When [situation], I want to [motivation], so I can [outcome]' format with detailed acceptance criteria. Use when writing job stories, creating JTBD-style backlog items, or expressing user situations and motivations.

Create user stories following the 3 C's (Card, Conversation, Confirmation) and INVEST criteria with descriptions, design links, and acceptance criteria. Use when writing user stories, breaking down features into backlog items, or defining acceptance criteria.

Enforce writing specifications before any code, with structured formats, acceptance criteria, and validation to reduce rework and clarify requirements.

Write long-form content like blog posts, guides, tutorials, and newsletters with a distinctive, non-generic voice derived from supplied examples or brand guidance.

Write, create, modernize, or fix comprehensive MSTest unit tests with MSTest 3.x/4.x APIs, covering assertions, data-driven tests, lifecycle, and async patterns.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture for rapid experimentation and synthesis, producing papers and presentations.