「arr」の検索結果

Performs numerical and symbolic mathematics using NumPy, SciPy, SymPy, and mpmath. Use when the user needs linear algebra, arrays, optimization, integration, ODEs, symbolic expressions, equation solving, or arbitrary-precision arithmetic.

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Structured exposure habits for building taste through relentless, intentional consumption. Not passive scrolling. Active intake across design, architecture, film, music, food, and culture. Use when building a taste practice, advising on professional development, or diagnosing why someone's design range feels narrow.

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Comprehensive investigation of complex multi-domain narratives (technology, theology, policy, finance). Systematically decouples factual claims from speculation through iterative verification, multi-source comparison, and contradiction resolution. Use for investigations requiring institutional knowledge, historical context, and handling of conflicting incentives.

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Implement Swift Codable models for JSON and property-list encoding and decoding with JSONDecoder, JSONEncoder, CodingKeys, and custom init(from:) or encode(to:). Use when parsing API responses, remapping keys, flattening nested JSON, handling date or data decoding strategies, decoding heterogeneous arrays, or integrating Codable with URLSession, SwiftData, or UserDefaults.

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Expert guidance on Swift Testing best practices, patterns, and implementation. Use when developers mention: (1) Swift Testing, @Test, #expect, #require, or @Suite, (2) "use Swift Testing" or "modern testing patterns", (3) test doubles, mocks, stubs, spies, or fixtures, (4) unit tests, integration tests, or snapshot tests, (5) migrating from XCTest to Swift Testing, (6) TDD, Arrange-Act-Assert, or F.I.R.S.T. principles, (7) parameterized tests or test organization.

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Craft a single, high-quality GitHub issue from a developer's description of a bug, unexpected behavior, or desired enhancement. Walks through reproduction, codebase exploration, and structured drafting so the issue is actionable on arrival. Use when user wants to file an issue, report a bug, request a feature, or document a problem they've encountered.

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Applies Chesterton's Fence, Occam's Razor, the Peter Principle, and Dunning-Kruger to technical decisions. Use before refactoring, architectural changes, or any task where changing existing behavior carries risk or requires judgment about what the user actually needs.

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Improve layout, spacing, and visual rhythm. Fixes monotonous grids, inconsistent spacing, and weak visual hierarchy. Use when the user mentions layout feeling off, spacing issues, visual hierarchy, crowded UI, alignment problems, or wanting better composition.

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Plans Convex schema and data migrations with widen-migrate-narrow and @convex-dev/migrations. Use for breaking schema changes, backfills, table reshaping, or zero-downtime rollouts.

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Workflow-state classification vocabulary for inter-team relay. Maps inbound signals from the remote agent to a routing decision. Intentionally narrow — states and destinations only.

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Drives development with tests. Use when implementing any logic, fixing any bug, or changing any behavior. Use when you need to prove that code works, when a bug report arrives, or when you're about to modify existing functionality.

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Expert in building immersive scroll-driven experiences - parallax storytelling, scroll animations, interactive narratives, and cinematic web experiences. Like NY Times interactives, Apple product pages, and award-winning web experiences. Makes websites feel like experiences, not just pages. Use when: scroll animation, parallax, scroll storytelling, interactive story, cinematic website.

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You are an accessibility expert specializing in WCAG compliance, inclusive design, and assistive technology compatibility. Conduct audits, identify barriers, and provide remediation guidance.

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Busabase-backed App-in-Skill idea desk that works like a business consultant — it interrogates a product or business idea until it is sharp, then carries it through professional, visual BRD, MRD, and PRD documents. Use when the user invokes $kelly-ideas or /kelly-ideas, wants to capture or shape a product/business idea, asks for a BRD/MRD/PRD, says they have a product idea but cannot describe it clearly, or wants an idea shaped into a spec that $kelly-app-skill-creator can build into a real app.

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Validates compliance, security, and GRC terminology in marketing copy. Enforces accurate claims, prevents common mistakes (e.g., calling SOC 2 a "certification"), and applies risk-first narrative framing for B2B SaaS audiences. Use when writing or reviewing any marketing content that references compliance frameworks, security standards, regulatory requirements, or audit processes. Also use when creating ads, landing pages, emails, case studies, or sales collateral for GRC/cybersecurity B2B SaaS products.

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Performs statistical analysis of Zeek conn.log connection intervals to detect C2 beaconing patterns. Uses the ZAT library to load Zeek logs into Pandas DataFrames, calculates inter-arrival time standard deviation, and flags periodic connections with low jitter. Use when hunting for command-and-control callbacks in network data.

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Prepares meeting materials by gathering context from Notion, enriching with Claude research, and creating both an internal pre-read and external agenda saved to Notion. Helps you arrive prepared with comprehensive background and structured meeting docs.

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Visual storytelling skill chain for DSH; use for narrative motion, scroll-driven UX, and stack-aware implementation rails.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

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Shape an article as a journey of beats, choose-your-own-adventure style. The user picks a starting beat from the raw material, you write only that beat, then offer options for where to pivot next, beat by beat, until the article reaches a natural end. Use when the user has raw material and wants to assemble it as a narrative rather than an argument.

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How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event, a product surface no canonical scout covers). Covers the scout SKILL.md anatomy, the emit contract, the dedupe + scratchpad-memory conventions, the per-team skills-store path vs the canonical in-repo path, and the emit-and-inspect test loop (with dry-run as an optional safety net). Trigger on "write/edit/customize a signals scout", "new scout for X", "tune my scout schedule", "make a scout that watches <event>".

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Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.

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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.

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Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.

日本語の概要は準備中です。原文の説明を表示しています。