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Apply IRQL graph functions to KQL or IRQL query results for Kusto Explorer visualization. Generates Lift_To_Graph mappings and composes Graph_Render_View, Graph_Fold_By_Property, Extract_Node_*, Enrich_Node_*, and Enrich_Graph_* calls. Accepts a supplied query or limited basic natural-language source request; it is not a general natural-language-to-KQL/IRQL skill. WHEN: Lift_To_Graph, Graph_Render_View, Graph_Fold_By_Property, IRQL graph enrichment, graph mapping for existing query results, icon-decorated graph, fold graph nodes. Use azure-kusto-graph for native make-graph analysis, graph-match, shortest paths, components, or persistent graphs.

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Experiment design reference for problem-class framing, Minimum Viable Experiment coaching, hypothesis formation, vetting and red flags, and experiment readiness. Use when translating a stated business outcome into candidate data-science problem classes, or when framing, vetting, scoping, or evaluating an experiment of any kind, including data feasibility, architecture, LLM, performance, use-case, UX, prototyping, and hardware experiments.

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Build and ship Unity 6.3 LTS players: build settings and scenes, player/quality settings, the IL2CPP vs Mono scripting backend, managed code stripping, scripted BuildPipeline.BuildPlayer, and CI/headless builds. Use when configuring or automating a build, choosing a scripting backend, shrinking build size, or when the user mentions Unity build, player settings, IL2CPP, code stripping, or Addressables.

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Architect game input — action mapping (abstracting keys into named actions), rebinding with conflict detection and persistence, multi-device support (keyboard, gamepad, touch), analog deadzones, and feel features like input buffering and coyote time, plus accessibility. Engine-neutral. Use when the user mentions input mapping, rebind controls, gamepad support, deadzone, input buffering, coyote time, or accessible controls.

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Build branching dialogue and narrative — a node/choice graph with conditions, variables, and localization hooks — and choose between authoring tools Ink and Yarn Spinner or a custom data-driven runner. Engine-neutral. Use when the user mentions dialogue system, branching dialogue, conversation tree, choices, Ink (.ink), Yarn Spinner (.yarn), or NPC dialogue.

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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.

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Turn one long video, podcast, or stream transcript into 8-12 short-form clips — each with a hook line, cut timestamps, captions, and a platform note for TikTok/Reels/Shorts — plus an honesty gate that kills clips that misrepresent the source. Use when someone says 'clip this podcast', 'make shorts from my video', 'what's clippable here', or runs a clipping side hustle. Produces a ranked clip sheet ready for an editor or a clipping app.

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Set up a chart of accounts and rules for categorizing transactions. Use when asked how to categorize expenses/transactions, set up a chart of accounts, organize bookkeeping, or sort bank transactions into the right buckets. Produces a practical chart of accounts for the business, categorization rules with examples and edge cases, and a clean-books routine — so the books are consistent and ready for an accountant. Not tax/accounting advice.

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Help users accelerate their product development cycles by implementing high-intensity rituals, ruthless scoping, and technical guardrails that allow teams to ship faster without sacrificing quality.

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Render a Claude-style prompt typing animation video by calling Remotion CLI against the remote site https://www.laosunwendao.com. Use when the user asks for "做一个 claude 的提示词打字机动画", "做 Claude 打字动画", "创建提示词动画", or similar requests that convert a text prompt into a typing-animation video.

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。

Compare independent opinions from several models or contexts without inflating agreement. Use when: wanting a second opinion or debate, or summarizing several reviewers' results.

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。

AI image generation for SEO assets: OG/social preview images, blog hero images, schema images, product photography, infographics. Powered by Gemini via nanobanana-mcp. Requires banana extension installed. Use when user says "generate image", "OG image", "social preview", "hero image", "blog image", "product photo", "infographic", "seo image", "create visual", "image-gen", "favicon", "schema image", "pinterest pin", "generate visual", "banner", or "thumbnail".

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Kotlin Multiplatform structure and language traps: module splits, expect/actual, Koin and ViewModel scoping, layers, version catalogs, erased overloads, Char and string-resource formatting limits. Use when shaping modules or DI, when a format specifier renders verbatim, or code misbehaves on one platform.

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Use when strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

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Mandatory pre-publish review gate for a UGC video render. Transcribes the finished render's AUDIO with Whisper and word-diffs it against the approved spoken script, then gates pinning the final render (video_project_upsert patch.final_render_id) — blocking a render whose generated audio mis-voices a word (e.g. the approved "human-vetted" spoken as "human witted"), says a different number or brand name, flips a negation, drops an approved phrase, or comes back silent. Correct speech written differently ("5mg" said "five milligrams", "30%" said "thirty percent", a spoken URL, "don't" said "do not", "braxleybands" said "braxley bands", a confirmed pronunciation like "AG1" said "A G one") passes. Runnable, gating counterpart to content-goose's review-transcript-integrity atom. Every ugc-video-formats recipe runs this after render and BEFORE pinning the final render.

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。

Compatibility alias for the original ScrapeCreators social scraping skill. Use scrapecreators-api for current endpoint guidance and the task-specific Brand Growth research skills for finished analysis.

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。

Web scraping, crawling, and AI-powered answer extraction at scale

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Web scraping with structured data extraction - define your output schema

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。

AI-powered web scraping - extract data using natural language prompts

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Research a company or idea, define the Ideal Customer Profile, and route to the right next step — either mapping the TAM or finding leads/prospects directly. The entry point for any "find me leads", "map my market", or "who should I sell to" request. Auto-loads when a user provides a company URL or idea and asks for leads or market mapping.

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 a 'search-grid' (Pinterest search-moodboard) video from a config — a real-DOM page with four continuous beats (masonry search grid + typing hook with counter-drift columns → 3 cards slide in from the right and stack → the top card box-grows to fullscreen then swipe-left ×2 through captioned feature shots → end card), frame-stepped via Chromium and encoded with FFmpeg — deterministic assembly, FREE (real brand photos + logo; the optional music bed comes from create-music-elevenlabs), so type/logo/photos stay pixel-crisp. Use for the search-grid 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。

Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

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Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.

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Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

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。