「iter」の検索結果

Find leads matching criteria and bulk-add them to an Apollo outreach sequence. Handles enrichment, contact creation, deduplication, and enrollment in one flow.

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TRIGGER THIS when crafting job descriptions, posting new roles, defining position requirements, creating hiring briefs, updating JDs, or structuring role expectations. Creates compelling, inclusive, DEI-focused job descriptions with clear requirements, responsibilities, qualifications, salary transparency, and modern hiring language.

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。

Execute autonomous multi-step research using Google Gemini Deep Research Agent. Use for: market analysis, competitive landscaping, literature reviews, technical research, due diligence. Takes 2-10 minutes but produces detailed, cited reports. Costs $2-5 per task.

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Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.

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。

Create, edit, and evaluate agent skills iteratively.

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。

X/Twitter Content Specialist. Writes X-native posts — hooks, threads, and standalone tweets optimised for the platform. Strictly enforces the 280 character limit with a count on every post. Supports standalone tweets and X threads (1/ format). Reads brand-style.md and content-calendar.md. Optional X trend research via Tasty Content MCP. Output to outputs/x/.

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。

Builds a month of social media post ideas for SMBs. Takes brand context, platforms, and goals and produces a structured content calendar with post topics, formats, angles, and visual direction for each slot. Output saves to context/content-calendar.md for use by /caption-writer and /social-creative-designer. Supports Instagram, LinkedIn, Facebook, TikTok, and X.

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。

Threads Content Specialist. Writes Threads-native posts — short, direct, opinion-led, made for conversation. Strictly enforces the 500 character limit with a count on every post. Supports standalone posts and threads (connected posts). Reads brand-style.md and content-calendar.md. Flags infographic opportunities for /publisher. Output to outputs/threads/.

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。

Writes on-brand social media captions for SMBs. Takes post concepts or a content calendar and produces ready-to-post captions with hooks, body copy, CTAs, and hashtags. Reads brand-style.md for voice and tone. Supports Instagram, LinkedIn, Facebook, TikTok, and X. Batch and single-post modes. Optional trend and competitor research via Firecrawl and SerpApi.

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。

Brand onboarding setup skill. Captures a client's visual identity, content patterns, audience, and goals through evidence capture + pre-filled client doc + structured intake. Writes context/brand-style.md as the foundation for all social skills. Run once per client before using /social-creative-designer, /content-calendar, or /caption-writer.

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。

LinkedIn Content Specialist. Writes LinkedIn-native posts for SMB clients — first-person, professional-but-human, built for LinkedIn's format and algorithm. Not adapted captions: posts written from first principles for the platform. Reads brand-style.md and content-calendar.md. Flags posts that would benefit from a Blotato infographic. Output to outputs/linkedin/.

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。

UE (user experience engineering) skill for defining end-to-end user experience behavior and interaction logic. Use for tasks like flow design, interaction models, state machines, error recovery, input validation, usability heuristics, accessibility, and turning UX intent into testable acceptance criteria for implementation.

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 wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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横纵分析法（Horizontal-Vertical Analysis）深度研究Skill。由数字生命卡兹克提出，融合了索绪尔的历时-共时分析、社会科学的纵向-横截面研究设计、商学院案例研究法与竞争战略分析的核心思想。 当用户想要系统性研究一个产品、公司、概念、技术或人物时使用。核心是双轴分析：纵轴追踪从诞生到当下的完整生命历程（以叙事故事呈现），横轴在当下时间截面上与竞品/同类进行系统性横向对比，最后交叉两条轴产出独到洞察。最终产出一份排版精美的PDF研究报告。 触发词包括但不限于：横纵分析、研究一下、帮我分析、深度研究、做个研究、调研一下、竞品分析、帮我看看这个东西怎么样、这个产品/公司/概念是怎么回事、帮我摸清楚、帮我搞懂、帮我做个deep research。 即使用户只是说"帮我了解一下XX"或"XX是什么来头"，只要上下文暗示需要系统性的深度研究（而非简单的概念解释），都应该触发。也适用于用户丢来一个产品名、公司名、技术名词说"帮我研究一下这个"的场景。 不要用于简单的名词解释（用户只是问"XX是什么"）、不要用于公众号写作（那个用khazix-writer）、不要用于纯标题摘要生成（用wechat-title）。

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 wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

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。

Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.

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。

Transforms a rough idea into a detailed design document with implementation plan. Follows Prompt-Driven Development — iterative requirements clarification, research, design, and planning.

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Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

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Trigger this skill when the user wants to train, fine-tune, or adapt Gemma models (e.g. SFT, DPO, RLHF, Reward Modeling) on local hardware. Covers TRL, Unsloth, dataset preparation, validation, and GGUF/LiteRT conversion.

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。

An expert at creating and refining automated tests using TestDriver.ai

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Build TestDriver tests iteratively using MCP tools with visual feedback

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。

Find scientific papers, follow citations, and verify claims against paper passages. Use for literature questions, related work, citing papers, references, or a question about a particular scientific paper.

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。

Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

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Generate engaging, localized App Store release notes (What's New) from git log, bullet points, or free text using canonical metadata under `./Store`. Optionally pairs with promotional text updates.

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。