236건 · 관련도순
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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Converts a priced estimate spreadsheet into a DRAFT statement of work populated from the user's own SOW template, after checking that line items, hours, rates, subtotals and the grand total add up. Returns the populated SOW and a validation report as Markdown, flags every discrepancy and marks missing fields TBC. Use when the user asks to "turn this estimate into an SOW", "build the statement of work from the pricing workbook", "convert the quote into a scope document", "populate our SOW template from the estimate" or "draft the engagement document from the rate card". Do not use for reviewing or editing an existing SOW, use contract-review-pack instead; for a proposal before pricing is fixed use proposal-skeleton; for RFP or tender answers use rfp-response-drafter. Drafts for human review; never approves, authorises or signs off.
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Produces WordPress draft posts from research findings — converts Paper Store research records or raw research data into publishable blog posts, buying guides, brand spotlights, and how-to articles. Use when the task asks to "turn research into a post", "create a blog post from findings", "publish my research", "research to content", or "write up the findings". Covers the full research-to-post pipeline with Paper Store integration.
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Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, per
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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or
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Converts TypeScript to idiomatic Dart 3 code, covering syntax mapping, type system translation, async patterns, and module migration. Activates when translating TypeScript interfaces, classes, enums, generics, union types, async/await patterns, or modules to Dart; migrating frontend or Node.js logic to Flutter/Dart; converting RxJS Observables to Dart Streams; adapting TypeScript utility types to Dart equivalents; or producing idiomatic Dart 3 output (sealed classes, records, pattern matching) from TypeScript source.
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Email has the highest ROI of any marketing channel. $36 for every $1 spent. Yet most startups treat it as an afterthought - bulk blasts, no personalization, landing in spam folders. This skill covers transactional email that works, marketing automation that converts, deliverability that reaches inboxes, and the infrastructure decisions that scale. Use when: keywords, file_patterns, code_patterns.
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Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extracts the WACC, assumptions, and DCF projections from the markdown metadata file and converts them into a clean, structured JSON file for the frontend interactive viewer.
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Converts Claude Code skills to Gemini CLI extensions and vice versa. Use when the user wants to make a skill cross-platform compatible, port a skill between platforms, or create a universal extension that works on both Claude Code and Gemini CLI.
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Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.
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Rory Sutherland's 'psycho-logic' approach to marketing, product, and business problems — solve the human perception, not just the engineering or economic metric. Use when the obvious 'logical' fix is expensive, slow, or already done by every competitor; when the user asks 'how do we make people want X', 'the product/price/funnel is fine but nobody converts', 'how do we stand out', 'is there a cheaper way to fix this', or mentions behavioral economics, nudges, irrational behavior, perception, or 'it doesn't make sense but it works'. This is the ROUTER skill: it diagnoses the problem in human terms, then dispatches to reframing, costly-signaling, counterintuitive-tests, or experience-design. Inspired by Rory Sutherland's book *Alchemy*. 日本語の相談（「商品はいいのに売れない」「値下げしかない？」「競合と差別化できない」）にも使う。中文咨询（“产品很好却卖不动”“只能降价吗？”“无法差异化”）也适用。For a catalogue of individual biases, use marketing-psychology (separate plugin) instead.
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Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.
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Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.
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Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.
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Resolves rsIDs, navigates RsMergeArch/SNPHistory merge chains, and converts between rsID, SPDI, HGVS, and VCF representations using the dbSNP Build 156 JSON architecture. Use when normalizing variant identifiers, joining variant databases by cluster ID, or tracking deprecated rsIDs through historical merges.
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Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, MMTF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
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Production pipeline for ASCII art video — any format. Converts video/audio/images/generative input into colored ASCII character video output (MP4, GIF, image sequence). Covers: video-to-ASCII conversion, audio-reactive music visualizers, generative ASCII art animations, hybrid video+audio reactive, text/lyrics overlays, real-time terminal rendering. Use when users request: ASCII video, text art video, terminal-style video, character art animation, retro text visualization, audio visualizer in ASCII, converting video to ASCII art, matrix-style effects, or any animated ASCII output.
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Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.