「voc」の検索結果

Draw from architecture, fashion, music, food, and industrial design to expand your design taste range. Use when seeking inspiration beyond digital, building diverse reference sets, or expanding creative vocabulary.

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Run taste-informed design reviews using Observation > Principle > Question. Use when facilitating design critiques, improving feedback quality on a team, or building a shared vocabulary for quality.

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。

Pull design principles from architecture, fashion, film, industrial design, and physical spaces into screen-based design. Use when seeking inspiration beyond digital, expanding creative range, or building cross-disciplinary design vocabulary.

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 a task should repeat on a schedule or until a condition holds (CI until green, follow a PR to merge, review rounds until no blockers, daily session summary). Ships ready prompt templates with run and duration limits, stop conditions and go-gate safety rules, and per-harness loop invocation.

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。

Analyze yesterday's prompts to surface one technical concept worth understanding, delivered as a concise morning lesson with everyday analogy + engineer vocabulary. Use when "morning lesson", "prompt mirror", "today's learning", "what should I learn", or via scheduled automation each morning.

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。

Invocable iOS design pipeline. When called with a design prompt, runs an autonomous loop: designs iOS screens in Pencil MCP following Apple HIG, reviews for taste using Design Philosophy, validates with XcodeBuildMCP, and iterates until done. Use as: /ios-design-stack "Design a [screen] for [app] with [requirements]"

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Implement iOS authentication patterns including Sign in with Apple (ASAuthorizationAppleIDProvider, ASAuthorizationController, ASAuthorizationAppleIDCredential), credential state checking, identity token validation, ASWebAuthenticationSession for OAuth and third-party auth flows, ASAuthorizationPasswordProvider for AutoFill credential suggestions, and biometric authentication with LAContext. Use when implementing Sign in with Apple, handling Apple ID credentials, building OAuth login flows, integrating Password AutoFill, checking credential revocation state, or validating identity tokens server-side.

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Apply critical rationalist epistemology (Popper, Deutsch) to evaluate reasoning, identify errors, and refine understanding. Use when the user explicitly requests help with reasoning - phrases like "help me think this through", "does this make sense", "any flaws in this", "what am I missing", "critique this", "is this reasoning sound", "stress test this idea", "devil's advocate", or any request to evaluate arguments, identify logical problems, or improve thinking. Also use when errors in reasoning are significant enough to materially affect the user's goals, even if not explicitly requested.

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Structured 3-phase peer review of a diff or PR — intent, issues by severity, then pointed merge-readiness questions and a verdict. Use for general code review. Not for AI workflow self-fix-and-edit (ai-systems-review) or no-edit devil's-advocate threat models (adversarial-challenge). Phase 3 asks questions only; it is not the adversarial-challenge skill.

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Hostile self-review of recently written AI workflow code that then applies fixes (agents, tools, chat context, summarization, tokens, attachments). Use after generating AI systems code, or when asked to self-review and harden it. Not for PR peer review (code-review) or no-edit devil's-advocate attacks (adversarial-challenge). Prefer: adversarial-challenge → decide → this skill.

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Strips AI fingerprints from written text by enforcing banned vocabulary, forbidden sentence structures, and authentic prose patterns. Use when writing articles, documentation, social content, emails, or any text that should read as human-authored rather than AI-generated.

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Shared foundation for all human-voice writing skills. Load before any profession-specific skill. Contains: burstiness principle, perplexity principle, soul injection techniques, community-discovered AI patterns with sources, additional core patterns, banned AI vocabulary, and scoring rubric.

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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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Fast BPE/WordPiece tokenization and custom vocab training.

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Implements secure API key generation, storage, rotation, and revocation controls to protect API authentication credentials from leakage, brute force, and abuse. The engineer designs API key formats with sufficient entropy, implements secure hashing for storage, enforces per-key scoping and rate limiting, monitors for leaked keys in public repositories, and builds key rotation workflows. Activates for requests involving API key management, API key security, key rotation policy, or API credenti...

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。

Plain language directive. Cuts verbosity, corporate jargon, and overused AI vocabulary. Use when you want clear, direct, short writing. No fluff, no hedging, no unnecessary complexity.

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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

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Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

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Reference for writing and editing skills well — the vocabulary and principles that make a skill predictable.

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Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.

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Explore and diagnose a PostHog endpoint's execution logs — error messages, failed runs, cache misses, slow runs, or unexpected row counts during endpoint invocations. Use when the user says "my endpoint is failing", "show me the logs for endpoint X", "what error did endpoint Y produce", "why did endpoint Z return no rows", "is this endpoint hitting cache", or "check the last N runs". Focused on a single named endpoint's runtime log entries, not project-wide auditing or query performance profiling.

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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

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。

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

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。

Stellar dApp / frontend development. Covers the JavaScript stellar-sdk (browser + Node.js), Freighter wallet, Stellar Wallets Kit (multi-wallet), Wallet Standard, smart accounts with passkeys, transaction building / signing / submission, smart contract invocation from the client, simulation, and error handling. Use when building a React/Next.js/Node.js app that talks to Stellar — classic operations or smart contracts.

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。