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概要と使いどころ
Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.
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Build and script 2D tilemaps in Unity 6.3 LTS: the Grid + Tilemap components, the Tile Palette, tilemap colliders, rule tiles, and runtime SetTile/GetTile painting. Use when painting tile levels, adding a TilemapCollider2D, using rule or animated tiles, generating tilemaps from code, or when the user mentions Unity tilemap, tile palette, rule tile, or Grid.
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。
Fetches Twitter/X post and Article content — text, author, engagement metrics and embedded media — downloading images locally and generating complete Markdown with image references. Use when retrieving a tweet's text or an X Article with images, or pulling post metadata for a Markdown/PKM record. Preferred over Jina alone for X Articles with images.
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。
Places existing images onto an Excalidraw whiteboard, turns a slide deck into clean per-slide images, and inspects a .excalidraw file. Use whenever someone mentions Excalidraw, a whiteboard/canvas, .excalidraw, 白板 or 画板 — e.g. "put these screenshots on my board" — or a workshop delivered by hand-drawing over screenshots on a shared canvas. Not for generating a diagram from a text description.
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。
Convert a git log, commit list, or release notes into a polished, user-facing changelog. Use when writing release notes, generating a CHANGELOG.md entry, or documenting what changed in a version. Produces a structured changelog section with version header, categorised changes, and migration notes. For an already-curated change list use changelog-writer instead.
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Map where you are against the next level and build a concrete plan to close the gap. Use when asked to map a career ladder, find the gap to the next level, build a development/growth plan, or figure out what to work on to get promoted. Produces a level-gap map — current vs. target competencies side by side, the specific gaps, and a prioritised 1–2 quarter plan of evidence-generating projects to close them.
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。
Generate educational practice questions from lecture notes to test student understanding. Use when users request practice questions, exam preparation materials, study guides, or assessment items based on lecture 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。
Generate pytest tests for Typer CLI commands. Includes fixtures (temp_storage, sample_data), CliRunner patterns, confirmation handling (y/n/--force), and edge case coverage. Use when user asks to "write tests for", "test my CLI", "add test coverage", or any CLI + test request.
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。
HyperFrames CLI tool — hyperframes init, lint, preview, render, transcribe, tts, doctor, browser, info, upgrade, compositions, docs, benchmark. Use when scaffolding a project, linting or validating compositions, previewing in the studio, rendering to video, transcribing audio, generating TTS, or troubleshooting the HyperFrames environment.
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 asks the openkb chat to make a deck / slide presentation / PPT / slides / 演示稿 / 幻灯片 from their compiled KB content. Generates a polished single-file HTML deck in the Editorial Monocle visual direction (warm cream background, serif type, brick-red accent) — designed to be opened in a browser, full-screened, and shared. Does NOT apply to generating skills (that's `openkb skill new`), long-form research reports, or interactive prototypes.
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 asks the openkb chat to make a deck / slide presentation / PPT / slides / 演示稿 / 幻灯片 from their compiled KB content AND wants a dark, high-tech, neon / glow / glassmorphism look (赛博 / 科技风 / 暗色 / 霓虹 / 炫酷). Generates a polished single-file HTML deck in the Aurora Glass visual direction (near-black background, teal/sky/magenta/amber neon accents, glassmorphism panels, aurora gradient atmosphere) — opened in a browser, full-screened, shared. For the warm, printed, serif look use openkb-deck-editorial instead. Does NOT apply to generating skills (`openkb skill new`), research reports, or scrolling long-form documents.
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 you create a JuliaLang package
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 reading, editing, or generating a Bethesda Game Studio load-order file (plugins.txt / loadorder.txt) for Fallout 4, Skyrim SE/AE/VR, Fallout 76, Starfield, or when launching xEdit with a custom plugin list. Authoritative reference for the asterisk-prefix format, official masters detection, ESL handling, and the routing rules between plugins.txt editing and xEdit daemon commands.
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Answers natural-language business questions over existing Power BI reports and semantic models through the FabricIQ MCP endpoint, discovering artifacts, resolving entities, generating the DAX itself, and returning a plain-language answer. Use when the user asks a data question, including one that says to query the model directly. For DAX the user supplies, or measures to save, use semantic-model-authoring.
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Monitor competitor content across blogs, LinkedIn, and Twitter/X on a recurring basis. Surfaces new posts, trending topics, and content gaps you can own. Chains blog-feed-monitor, linkedin-profile-post-scraper, and twitter-mention-tracker. Use when you want a weekly digest of what competitors are publishing and which topics are generating engagement.
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Replace an existing video's opening with a supplied clip or free kinetic text hook while retaining and verifying every original body frame, audio, captions and ending. Use for surgical hook tests rather than regenerating the complete ad.
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。
Interactive cell annotation and image QC for IMC/MIBI using napari, napari-imc, Mantis Viewer, and cytomapper, covering the pixels-to-cell-table bridge, overlaying masks to catch segmentation/spillover artifacts, inter-annotator variability as the accuracy ceiling, contrast-as-threshold, and building class-balanced ground-truth label sets. Use when manually labeling cells, generating training data for a classifier, QC-ing segmentation on the image, confirming clusters are spatially real, or choosing an annotation viewer.
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。
Turns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked. Covers the bwa mem -SP5M / bwa-mem2 / chromap --preset hic alignment idiom (mates mapped as independent single-end reads), pairtools parse vs parse2 and the walks-policy choice (5unique pairwise vs all for Pore-C/Micro-C concatemers), pair-type classification (keep UU and rescued UC), dedup (PCR vs optical/by-tile), select by pair_type/MAPQ/distance, restriction-fragment handling (restrict, Arima dual-enzyme, Micro-C/DNase fragment-free), and allele-specific phasing (pairtools phase to two coolers). The library-QC decision uses % long-range cis as the one-number quality metric, trans as the noise floor, orientation balance as fragment-map-free dangling-end/self-circle QC, and % duplicates as a complexity proxy. Use when processing Hi-C/Micro-C/Omni-C reads into pairs, judging library quality, handling multi-enzyme or restriction-agnostic protocols, or generating allele-specific contacts.
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Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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。
Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.
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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。
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.
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。
Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.
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。