「line art」の検索結果

Converts a PDF into one self-contained, readable HTML file that preserves images, tables, charts and reading order — optionally translating it into another language while keeping every figure. Uses structured extraction (PyMuPDF), font-size-driven layout, compressed base64-inlined images (a single portable file), and mandatory headless-Chrome visual verification. Use whenever someone wants to READ a PDF as a web page or clean document, turn a PDF into HTML, or translate a PDF into another language while keeping its images/tables/charts intact — e.g. "PDF 转 HTML", "把这个 PDF 转成中文网页版", "make this report readable", "translate this PDF but don't lose the charts", "I just want to read this PDF on my phone". Distinct from doc-to-markdown (plain Markdown text) and pdf-creator (Markdown→PDF) — this one produces a styled, image-faithful HTML reading experience.

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。

Run a company event — the launch party, the customer day, the team celebration — as the operation it is: the goal that shapes every choice, the budget with its forgotten lines, the vendor and venue coordination, the run-of-show with owners, and the day-of roles that keep hosts hosting. Use when asked plan the company event, organize our customer day/holiday party/launch event, what am I forgetting for this event, or be the run-of-show for Thursday. Produces the goal-shaped plan, the budget with the forgotten lines, the run-of-show, and the day-of role card.

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。

Plan a Chinese campus recruitment season (校招: 秋招 and 春招), including internships that convert to offers (实习转正): the timeline, a target company list, a one-page campus CV, online application answers, written tests and interview rounds, and choosing between offers and signing the tripartite agreement (三方协议). Use when asked 秋招怎么准备, 帮我做校招计划, 校招简历, 网申问题怎么写, 群面, 三方协议, 怎么选 offer, or plan my campus recruitment in China. Produces a season timeline to confirm with each company, a tiered company list, a campus CV, application answer drafts, a test and interview preparation plan, and an offer comparison.

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。

Decode an auto repair estimate — what each line actually is, which items are urgent vs upsell, and the questions that separate a fair shop from a fishing expedition. Use when someone asks 'is this repair quote fair', 'decode my mechanic's estimate', 'do I really need all this', or 'is the shop ripping me off'. Produces a line-by-line decode with urgency triage, parts/labor sanity checks, ranked red flags, and the exact questions to ask the shop before authorizing.

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。

Agent 2 of the video editing pipeline. Finds and removes spoken mistakes — stutters, repeated words, false starts, and retakes (re-recorded lines) — from a talking-head recording. Use after cut-silences, when asked to cut mistakes, remove stutters / repeats / filler restarts, clean up flubs, or keep the best take. Works review-gated: it proposes every cut with context and a reason for approval, then renders only the approved cuts via ffmpeg. Requires a word-level transcript.

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。

Search Mouser Electronics for electronic components — secondary source for prototype orders. Find parts, check pricing/stock, download datasheets, analyze specifications. Use with KiCad for BOM creation and part selection. Also supports batch MPN-list seeding (`--mpn-list`) for bulk datasheet workflows without a KiCad project. Use this skill when the user specifically mentions Mouser, when DigiKey is out of stock or has worse pricing, when comparing prices across distributors, or when searching for parts that DigiKey doesn't carry. For package cross-reference tables and BOM workflow, see the `bom` skill.

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。

Search Newark, Farnell, and element14 for electronic components — find parts by MPN or distributor part number, check pricing/stock, download datasheets, analyze specifications. One unified API covers all three storefronts (Newark for US, Farnell for UK/EU, element14 for APAC). Free API key, simple query-parameter auth, no OAuth. Datasheets download directly from farnell.com CDN with no bot protection. Sync and maintain a local datasheets directory for a KiCad project, or use batch MPN-list seeding (`--mpn-list`) for bulk workflows without a project. Use this skill when the user mentions Newark, Farnell, element14, needs parts from a non-US distributor, wants to compare pricing across regions, or needs datasheets from a source that doesn't require complex API auth. For package cross-reference tables and BOM workflow, see the `bom` skill.

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。

BOM (Bill of Materials) management for electronics projects — the workflow skill that coordinates DigiKey, Mouser, LCSC, element14, JLCPCB, PCBWay, and KiCad skills around a unified BOM lifecycle. Create, update, and maintain BOMs with part numbers, costs, quantities stored as KiCad symbol properties. ALWAYS trigger this skill for any task involving component sourcing, pricing, ordering, distributor searches, BOM export, or fabrication preparation — even if the user names a specific distributor or fab house (e.g. "search DigiKey for...", "generate JLCPCB BOM", "order from Mouser"). This skill analyzes the schematic for sourcing gaps, recommends which distributor/fab skills to call for each gap, and writes results back as symbol properties — the agent (or user) performs the actual searches via the called skills. Also trigger on phrases like "what parts do I need", "order components", "how much will this cost", "export for JLCPCB", "find parts for this board", "compare pricing", or "check stock".

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。

PCBWay PCB fabrication and assembly — turnkey/consigned assembly, design rules, ordering workflow. Alternative to JLCPCB for manufacturing. Use with KiCad. Use this skill when the user mentions PCBWay, needs turnkey assembly (PCBWay sources parts by MPN), has parts not available on LCSC, needs assembled boards with non-LCSC components, wants to compare PCBWay vs JLCPCB, or needs assembly with parts sourced globally rather than from LCSC only. For gerber/CPL export, stencil ordering, and BOM management, see the `bom` skill.

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。

Pick the right way to represent a dataset so a reader gets the finding in three seconds — a catalog of 20+ chart and diagram forms with when-to-use and failure modes, plus the encoding decisions that make any of them readable (takeaway headline, direct labels, kill the axis, highlight-and-mute, show the caveat). Style-agnostic — use with whatever palette or design system the destination already has. Use when charting survey results, benchmark data, usage metrics, or research findings for a post, brief, deck, or report, and whenever the default bar chart feels like it's burying the point.

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 an Advanced Materials manuscript must be fit to its article-type format — Communication versus Research Article — where word count and display items are budgeted together against a typeset page limit. Manages the length budget; does not restructure the science or polish prose line by line.

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。

Pipeline analysis composite. Pulls deal/meeting data from any CRM or tracking system, analyzes the pipeline over a user-defined period (weekly, fortnightly, monthly, quarterly), and produces both an executive summary and a detailed diagnostic report. Covers volume, qualification rates, source effectiveness, stage velocity, stuck deals, and actionable recommendations. Tool-agnostic — works with any CRM (Salesforce, HubSpot, Pipedrive, Close, Supabase, CSV).

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。

Assemble a whiteboard explainer video ad from a beats file — a narrator explains one idea while a real whiteboard fills up in black marker, with a hand-lettered title, bulleted rows down the left, a drawing filling the right, and a payoff that letters the closing line and rings the claim. The board is a PHOTOGRAPH and the ink is multiplied onto it, so the surface sheen and window light come through the strokes; the drawings are generated as line art and traced to ordered strokes, then revealed in drawing order on the word they belong to. The layout is SOLVED from the beats, never hand-placed. FREE assembly (Pillow plus ffmpeg) apart from one board photo and one drawing per subject, both one-time per brand. Use for the whiteboard format.

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。

Render the logo-equation card ad (7s, 9:16) — partner icon + brand icon joined by a plus sign as an equation, a two-line "= outcome" headline, four numbered steps, and a CTA that hard-cuts in at 2s, on a static black card over timelapse b-roll, no voice. render_card.py draws the two card plates with measured geometry and hard guardrails (the user must confirm the integration is real, no partnership/endorsement/ranking words, no comment-bait CTA, no dashes); compose.py lays the card over the b-roll sped up to read as a timelapse, with an optional bed; measure_motion.py gates the b-roll (a real-time shot reads as a photo). Free, local.

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。

Assemble a multi-scene GRWM beauty-demo ad from a config — a locked-identity creator applies ~5 products step by step while a SEPARATE ElevenLabs voiceover narrates and every scene cut is snapped to the VO's product-name word-starts (Whisper word-level timestamps), then ~5 Playwright product overlay cards (real PDP-verified taglines) are composited onto the master each on its product-NAME word-start, the SEPARATE VO is mixed on top of a ducked music bed at loudnorm I=-14, clean-white 3-words/cue captions are burned, and the video closes on a flat-lay end card. This is the FREE deterministic assembly stage (re-cut to the VO word-starts, hard-concat, Playwright card render + card composite, VO plus music mix, caption burn, flat-lay end card); the VO, scene clips, product cutouts, and music come from create-music-elevenlabs / create-image-gpt-image-fal / create-video-fal. Use for the glassy-matte-grwm format.

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。

Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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 provides 1-50 concrete bad-case search queries for one Viking Search app and wants local deterministic fixes. This skill only verifies request-level fine-operation interventions against a read-only baseline scene and delivers a console-ready configuration sheet, validated payloads, and a replay script. It must not mutate scenes, apps, dictionaries, datasets, recall core parameters, or online defaults.

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。

Automated outreach and sales pipeline — cold email sequences, partnership outreach, lead generation, and prospect management. Use when the user needs cold email campaigns, sales sequences, lead generation, partnership outreach, B2B sales, or says 'cold email', 'outreach', 'lead gen', 'find prospects', 'sales sequence', or 'partnerships'.

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。

When the user wants to write or optimize short-form marketing copy—headlines, CTAs, ad copy, landing page copy, email copy. Also use when the user mentions "copywriting," "headline," "CTA copy," "ad copy," "landing page copy," "sales copy," "conversion copy," "PAS," "AIDA," "BAB," "copy formula," or "differentiation." For long-form article bodies (blog posts, guides), use article-content.

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 automating a Unity 6.3 content pipeline or build: importing an art drop (FBX, PNG) with AssetPostprocessor rules and naming conventions, generating prefabs or editing scenes from script with correct Undo and SetDirty, Addressables groups, labels and content builds, content-validation tests, batch-mode, command-line or CI builds (Build Profiles, GameCI, GitHub Actions, Accelerator), editor tools; or when textures import wrong or reimport every run, a scripted edit vanishes, bundles duplicate assets, a batch build ignores its defines, or CI cannot activate Unity.

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 drawing or animating with Grease Pencil in Blender 5.x from Python: 2D or 2.5D illustration, frame-by-frame animation, a cutout or part-based 2D character, strokes with pressure, filled shapes with holes, in-betweens and interpolation, onion skin, Line Art outlines, strokes on 3D meshes, NPR or hybrid motion graphics. Also when GP code fails (GPENCIL, frame.strokes, grease_pencil_modifiers), fills do not show, colors render dark, or draw brushes will not run from a script.

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。

Apply pre-composed aesthetic archetype defaults to jumpstart brand design work covering color, typography, layout, voice, and imagery direction. Use this skill whenever the user wants a starting point for brand visual and verbal direction, references a brand they want to design near (for example 'something like Stripe' or 'editorial like Linear'), needs to converge faster from many directions to one, or wants known-good defaults for a specific industry vertical. Triggers on archetype, design archetype, brand archetype, near to (brand), in the style of, similar aesthetic, design starting point, brand template, vertical-typical, industry-typical. Also triggers when the user has a brief and wants to map it to a known aesthetic family rather than design from first principles.

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。

Multi-style article creation skill. Supports 5 writing styles (deep analysis, practical guide, story-driven, opinion, news brief), including complete workflow: material collection → outline → content → formatting. Activated when users mention "write article", "write post", "create", or "draft".

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。

Assess, report, and manage serious incidents involving high-risk AI systems under Article 73 EU AI Act. Covers incident qualification, deadline buckets (2/10/15 days), authority notification, deployer duties (Art. 26(5)), cross-regulation mapping (GDPR/NIS2/MDR), corrective measures, and DACH-specific routing. Use when asked to evaluate whether an AI incident is reportable, draft an Article 73 notification, identify the right authority, or build an AI incident response process.

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。