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概要と使いどころ
Turn a request for a report or a dashboard widget into something buildable: the question it answers, the metric and its definition, where the data comes from, the query shape, and what it will not answer. Use when a new report or dashboard is requested, when an existing one needs a metric added or changed, when a widget is asked for in a sentence and needs pinning down, or when two reports disagree about the same number.
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。
Fix data that has gone wrong and change shape without breaking what reads it: duplicates, nulls, inconsistent document shapes, a field that means two things, a column that has to change. Use when a report cannot be built because the data is not in the right shape, when duplicates, nulls or inconsistent shapes are producing wrong numbers, when a field is needed or has to change, before a migration, or when onboarding data from a new source that does not match the standard shape.
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。
Try to break a piece of UI by feeding it worst-case data — long names, unbreakable emails, one-letter names, missing fields, huge counts, zero items, long labels, non-Latin text, emoji, extreme numbers — then render it behind a "Demo data / Worst case" toggle and report everything that broke, with the fix for each. Use when the user asks to stress-test, break, or find edge cases in a component or screen, or to "try the worst case". For visual design critique use emil-design-eng; for motion use review-animations.
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。
Pick the right library for a given frontend task from a curated, opinionated list — numbers, OTP inputs, charts, command menus, virtualization, drag and drop, toasts, state, styling, and more. Only runs when explicitly invoked; it does not trigger on its own.
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 this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document 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。
Production-ready CSS transition snippets for web apps. Use when adding or reviewing UI motion for badges, dropdowns, modals, panel/page reveals, card resizes, number pop-ins, text/icon swaps, success checks, avatar-group hovers, and validation shakes. Supports namespaced commands: transitions reveal (list catalog), transitions review (audit project fit), and transitions apply (install the best-fit snippet with hooks and reduced-motion handling).
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。
End-to-end workflow for resolving one GitHub issue in dynamic-neural-field-composer - branch, TDD, build, review, docs, PR. Use when asked to fix, implement, or close a specific issue number.
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 and redact PII and sensitive data (emails, phone numbers, SSNs, API keys, IP addresses, credentials, amounts, company/person names) from repository files. Includes a pre-commit hook to block commits containing PII. Use when asked to audit code for sensitive data, sanitize files before publishing, or install PII detection hooks. For application security hardening see security-engineering.
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 a 13-week rolling cash flow forecast from your actual numbers.
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。
Send and receive P2P messages using disposable numbers and PINs. No servers, no accounts. Use for human notifications, approval flows, and agent-to-agent communication.
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 facing SEO algorithm questions, content strategy decisions, Threads growth planning, GEO optimization, AI tool selection for SEO, or evaluating SEO vendors. Triggers on any algorithm or content marketing decision — even without mentioning darkseoking. Every insight is backed by patent numbers or tested data.
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。
Corrects output that refers to entities by bare identifier, restating it with a human-readable label beside every ID, and keeps that rule for the rest of the conversation. Use when the user invokes `/ids` or `$ids`, or complains that bead IDs, PR numbers, commit SHAs, or ticket keys were given without titles.
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。
Drafts code-compliant California responses to Requests for Production (CCP § 2031.010 et seq.) for Superior Court. Use when the user mentions California RFP responses, CCP 2031, discovery objections, compliance statements, inability to comply, privilege objections, Bates numbering, verification requirements, or responding to document requests in California state court. California Superior Court only — not for federal court without substantial modification.
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。
Generates appellate-filing-ready Tables of Contents, Tables of Authorities, and Certificates of Compliance under FRAP 32(g) and related rules. Produces defensible word-count calculations with transparent exclusions and anti-hallucination guardrails for pagination and citations. Covers federal circuits, state appellate courts, and U.S. Supreme Court variations. Use when building TOC, TOA, compliance certificates, formatting briefs for filing, or when the user mentions word count calculation, FRAP 32 compliance, appellate brief assembly, or page numbering.
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。
Drafts adversary proceeding complaints for U.S. Bankruptcy Court under FRBP Rule 7001. Covers dischargeability disputes (§ 523), preference actions (§ 547), fraudulent transfers (§ 548), and stay violations (§ 362). Produces a filing-ready pleading with caption, jurisdictional basis, numbered allegations, count structure, and prayer for relief. Triggers when a trustee, creditor, or party in interest needs to initiate litigation within an existing bankruptcy case.
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。
Multi-pass structural code review with adversarial analysis, scope-aware dispatch, adversarial persona passes, and forced-finding protocol. Supports input modes: branch diff (default), PR number (#123), commit hash, pending changes (--pending). Use when asked to "review this PR", "code review", "pre-landing review", "check my diff", or "review #123". Proactively suggest when the user is about to merge or land code changes. NOT for behavioral verification against a running build (see mk:evaluate); NOT for post-implementation simplification (see mk:simplify).
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。
Look up company/business data through Openapi - Italian companies by VAT/tax code or name, European and worldwide company search, shareholders, UBO, AML, PEC/SDI codes, company monitoring. Use for questions like "find data about company X", "who owns VAT number Y", "search companies in France/EU/worldwide".
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。
Verify data validity through Openapi Trust - email deliverability, mobile number HLR checks, IP reputation, URL safety scans, document-based identity verification (IDV). Use for anti-fraud validation of user-provided contact data and identities.
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。
Save important context to persistent memory for future sessions. PROACTIVELY USE THIS when you detect: - Decisions: "let's go with", "decided to", "chose", "went with" - Learnings: "I learned", "turns out", "TIL", "realized", "discovered" - Errors resolved: "fixed", "the problem was", "solution was" - Client info: names, emails, phone numbers, addresses, invoice amounts - Preferences: "I prefer", "I like", "don't like", "always use" - References: API keys locations, file paths, URLs, credentials - Patterns: "pattern", "convention", "always do", "standard approach" - Important commands run worth remembering Do NOT ask permission - just capture silently when these patterns appear.
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。
Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-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。
Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
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。
Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering populations without predefined gates, choosing a clustering algorithm, selecting the number of metaclusters, or annotating clusters into cell types.
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。
Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.
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。