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概要と使いどころ
Build audio transcription pipelines with Whisper, Deepgram, and AssemblyAI including speaker diarization and real-time streaming. Activate on: transcription, speech-to-text, diarization, audio processing, meeting transcripts. NOT for: text-to-speech synthesis (voice-audio-engineer), music generation (ai-engineer).
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Generate llms.txt files from project documentation following the llmstxt.org spec. Use when asked to create AI-friendly documentation indexes, generate llms.txt, or make a project discoverable by AI assistants.
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Use when transforming existing code into agent-consumable surfaces — CLI (npm-publishable), MCP server (stdio/SSE/Streamable HTTP), and a companion skill. Triggers on 'agentize', 'henshin', 'expose as MCP', 'wrap as CLI', 'publish to npm', 'make LLM-accessible', 'turn into agent tool', 'expose feature as tool'. Runs when the user has existing code and wants to ship it to agents. NOT for building new code from scratch (see mk:bootstrap); NOT for reviewing or verifying existing code (see mk:review).
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Use when running an autonomous multi-hour build of a green-field product — orchestrates planner → contract → generator ⇄ evaluator loop with adaptive scaffolding density per model tier. Triggers on /mk:autobuild, "build me a kanban app", "build a retro game maker", "autonomous build", or any green-field product spec. NOT for scoped single-task work (see mk:cook); NOT for initial project scaffolding only (see mk:bootstrap).
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Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.
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。
Extract canonical spec from existing agent or skill files
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。
Scan the repository for hardcoded secrets, exposed API keys, insecure network configurations, and vulnerable dependencies. Use when the user asks for a security audit, secret detection, credential scan, vulnerability check, or wants to find leaked tokens in the codebase.
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。
Develop, challenge, and document Chisel processor microarchitecture designs before implementation. Use when discussing or writing design documents for processor pipelines, queues, issue logic, rename, ROB, LSU, caches, MSHRs, forwarding, wakeup, replay, flush, privilege, exceptions, or other cycle-accurate hardware mechanisms; when converting design conversations into stable Markdown specifications; or when reviewing a proposed Chisel CPU design for correctness, timing, area, verification cost, and cross-document consistency.
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。
Turn a reference image or mockup into a working local frontend that matches it closely. Inspects the image, extracts the design system (layout, type, spacing, color, components), implements it, then screenshot-compares and iterates to close the gap. Use when the user says "image to code", "build this mockup", "code this screenshot", "make this design real", or provides a UI image to implement. Part of the Product Design set. Uses a browser tool for screenshot verification.
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。
Build visual territories (mood boards) for team alignment before production. Groups references into 2-4 named territories, each with palette, texture, typography feel, and 4-6 generated sample tiles, so a team can agree on a direction. Use when the user says "moodboard", "visual territories", "align the team on a look", "reference board", or needs stakeholder sign-off on aesthetic before generating final assets. Part of the Creative Production set.
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Turn a live website into a working local frontend you can edit. Fetches the page, extracts its design system and structure, and rebuilds it locally in your stack, then screenshot-compares to the original. Use when the user says "url to code", "clone this site locally", "make it look like linear.app", "rebuild this page", or provides a live URL to recreate. Part of the Product Design set. Uses web-fetch + browser automation.
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。
Take a chosen visual to finished, production-ready state. Runs cleanup + refinement passes (fix artifacts, balance composition, color-grade, sharpen text, conform to spec/aspect) using image edit/regenerate. Use when the user says "polish this", "finalize", "clean up the image", "production-ready", "fix the artifacts", or has a selected visual that needs finishing. Final stage of the Creative Production set.
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。
Audit the code for vulnerabilities, leaked secrets and risky dependencies before it ships.
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Build enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.
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。
Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or flagging problematic samples before clustering or differential analysis.
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Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
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Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS. Handles silent-miscounting traps (featureCounts -p v2.0.2 API break, STAR strandedness column choice, salmon NumReads-sum without tximport, RSEM non-integer expected_count, GENCODE _PAR_Y suffix, zero-length-transcript TPM divide-by-zero), and encodes the tximport countsFromAbundance decision tree with the "lengthScaledTPM is not TPM" warning. Use when assembling a gene-by-sample count matrix from aligner or quantifier output, importing salmon/kallisto for DESeq2 vs limma-voom, choosing strandedness column for STAR, debugging zero-count panics, or building tx2gene mapping.
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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
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。
Build sequence logos from aligned DNA, RNA, or protein motifs using ggseqlogo (R), Logomaker (Python), or WebLogo with explicit bits vs probability encoding, background-frequency correction, custom alphabets, and multi-logo stacking. Use when visualizing motif PWMs (TF binding, splice sites, CRISPR spacers), aligned-position composition, or comparing two motif sets.
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。
Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.
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。
Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
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。
Build UpSet plots to visualize set intersections beyond 4 sets (where Venn fails) using ComplexUpset (modern, ggplot2-grammar) or the unmaintained UpSetR, with explicit cardinality vs degree sorting, attribute panels, and query highlighting. Use when comparing overlap across many gene sets, peak sets, variant lists, or any set membership matrix where Venn diagrams become illegible.
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Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.
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Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.
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。