1.3万 件 ・ 関連度順
概要と使いどころ
NGS analysis toolkit. Used for BAM to bigWig conversion, quality control (correlation, PCA, fingerprint plots), heatmaps/feature plots (TSS, peaks), suitable for ChIP-seq, RNA-seq, ATAC-seq visualization.
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。
1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported as.
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。
Derive security requirements from threat models and business context. Use when translating threats into actionable requirements, creating security user stories, or building security test cases.
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。
Scene and sequence craft (场景/段落/细节/道具/悬念/情趣) — merged from Robert McKee's scene design and five-step scene analysis (value turn, beats, text/subtext, turning points, pacing, image systems), McKee's Dialogue scene types (balanced, comic, asymmetric, indirect, reflexive, minimal), Syd Field's sequence and scene elements, Neill Hicks (less is more, enter late leave early, energy), Richard Walter (action over talk, small gestures, avoid phones/cars/restaurants, list twelve alternative locations), Wendy Henson (action paragraphs, transitions, story-doing), Julian Hoxter (scene beats, objectives and tactics), Mei Feng's value test, and Lu Jun's chapters 意趣要足 (suspense, delay, wit), 细节要妙, 道具要精, 场景要当. Use when writing or fixing a single scene, choosing a location, building a sequence, planting a prop or detail, designing suspense and delay, cutting a scene that "nothing happens" in, or turning talk into action.
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。
Snapshot the computational environment for a replication package — detects the analysis stack (R / Stata / Python) and emits the right lockfiles (renv.lock + sessionInfo.txt, requirements.txt / environment.yml / uv.lock, Stata version + ado package list), records seeds and RNG kind, optionally writes a pinning Dockerfile, and produces a paste-ready "Computational requirements" block. Use when user says "capture the environment", "snapshot my dependencies", "pin the versions", "make a renv.lock / requirements.txt", "make this byte-reproducible", or before releasing a replication package to openICPSR / the AEA Data Editor.
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。
Create or revise Chinese social-media covers through the registered Skill-封面 asset system, reusable styles, presenter identities, ratio-specific layouts, and exact replacement prompts. Use only when the user explicitly says“skill-cover”“Skill-封面”“实战风格”, requests the registered style/gesture assets, asks for simultaneous 3:4 and 4:3 registered-layout output, replaces a reference cover's person with the current digital-human frame, or extends this registered cover system. Do not use for a generic“制作封面/生成封面”request; generic cover requests belong to dog-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。
Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".
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。
Act as the user's live meeting sidekick inside the current terminal agent session. Use when the user explicitly asks you, the terminal agent, to watch a meeting, follow the live transcript, answer during the call, offer strategist thoughts, silently watch for risks, or track decisions. Do not use this skill to start or control the separate Minutes Coach HUD; explicit Coach or HUD lifecycle requests belong to minutes-copilot, and an ambiguous request such as "coach me live" requires one short surface clarification.
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。
Review, approve, request changes on, or reject pending wiki edit drafts in Arkon. Requires editor or admin role. Triggers on: review drafts, pending reviews, check draft queue, approve draft, reject draft, request changes, send draft back, review wiki changes.
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 a tailored code review checklist for any pull request based on the language, type of change, and risk level. Use when asked to review code, check a PR, review a pull request, or generate a code review checklist. Produces a focused checklist with language-specific checks, risk-level-appropriate depth, and a clear approve/request-changes recommendation.
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。
Write a polite-but-firm payment-reminder / collections email sequence for overdue invoices. Use when asked to write a collections email, a payment reminder, a dunning sequence, or to chase an overdue invoice. Produces a staged sequence — gentle pre-due nudge through escalating overdue reminders to a final notice — that stays professional, keeps the relationship intact, and makes paying easy. Not legal advice.
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。
Octane bindings for TanStack DB. useLiveQuery hook with dependency arrays (8 overloads: query function, config object, pre-created collection, disabled state via returning undefined/null). useLiveSuspenseQuery for Octane Suspense with Error Boundaries (data always defined). useLiveInfiniteQuery for cursor-based pagination (pageSize, fetchNextPage, hasNextPage, isFetchingNextPage). usePacedMutations for debounced Octane state updates. Return shape: data, state, collection, status, isLoading, isReady, isError. Import from @octanejs/tanstack-db (re-exports all of @tanstack/db).
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。
Detect Server-Side Request Forgery (SSRF) vulnerabilities in a codebase using a three-phase approach: recon (find outbound call sites), batched verify (trace user input to destinations in parallel subagents, 3 sites each), and merge (consolidate batch results). Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/ssrf-results.md. Use when asked to find SSRF or server-side request forgery bugs.
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。
Research LinkedIn profiles and write personalized messages for any LinkedIn message type — connection requests, InMails, DMs, message requests, post comments, and comment replies. Takes LinkedIn URLs as input, researches each person (profile data + recent posts via Apify), and generates messages tailored to each lead's background, interests, and recent activity. Exports tool-ready CSVs for Dripify, Expandi, Botdog, PhantomBuster, or generic format. No LinkedIn cookies or login required.
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。
For churned accounts, research what has changed since they left — new funding, team growth, competitor dissatisfaction, product updates that address their pain — then assess re-engagement potential and generate a personalized win-back email sequence with timing recommendations. Chains web research and LinkedIn monitoring with email sequence generation.
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。
Estimate frequency response from Simulink models using frestimate. Use when frequency response should be obtained from simulation rather than model linearization.
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。
Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.
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。
Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.
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。
Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
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 genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.
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 volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
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。