「aria」の検索結果

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.

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**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

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Expands one confirmed or suspected vulnerability into a Trailmark graph neighborhood of variant candidates by finding sibling functions, shared callers and callees, common sensitive sinks, common entrypoint paths, interface implementations, override relationships, type/reference neighbors, and structurally similar nodes. Use after one issue is found to seed variant-analysis, semgrep-rule-creator, static-analysis, or manual review with graph-derived candidate locations.

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Dispatch WGS, WES, or targeted DNA variant requests to germline, somatic, or UMI-panel skills, then plan public nf-core/sarek, GATK4, DeepVariant, samtools, or bcftools workflows.

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Submit compact ClinVar Clinical Tables and NCBI Variation requests for search, VCV, RCV, SCV, and RefSNP lookups. Use when a user wants variant-level summaries or identifier mapping

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。

**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

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。

**MANDATORY prerequisite** — you MUST invoke this skill BEFORE every `use_figma` tool call. NEVER call `use_figma` directly without loading this skill first. Skipping it causes common, hard-to-debug failures. Trigger whenever the user wants to perform a write action or a unique read action that requires JavaScript execution in the Figma file context — e.g. create/edit/delete nodes, set up variables or tokens, build components and variants, modify auto-layout or fills, bind variables to properties, or inspect file structure programmatically.

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Parallel control variable combination search via Stata. Exhaustively searches all subsets of optional controls to find the combination that maximises |t| of the independent variable. Activates when user says: "控制变量搜索", "搜控制变量", "control variable search", "跑控制变量组合", "暴力搜索", "调控制变量".

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Analyzes your top performing ads, identifies what's working in the hooks, CTAs, messaging angles, and formats, then generates new variants that follow the same winning patterns while introducing enough variation to test meaningfully. Platform: Google and Meta.

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Generate and A/B test Google Ads copy. Use when asked to write ad copy, headlines, descriptions, create ad variants, test ad messaging, improve CTR, or generate RSA (Responsive Search Ad) components. Trigger on "ad copy", "write ads", "headlines", "descriptions", "RSA", "responsive search ad", "ad text", "ad creative", "improve CTR", "ad A/B test", "ad variants", "write me an ad", "ad variation experiment", or when the user wants to improve click-through rate on existing ads.

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Validate, prepare, or run public CodonFM Encodon masked-codon variant scoring and review compatibility of its scoring workflows. Use only when the user explicitly requests CodonFM or Encodon, or that context is already established in the conversation. Do not select this skill for a generic variant-scoring request without that context; ask for the variant and intended analysis first.

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Constructs deep-links and URLs for the AlphaGenome Atlas website. Supports generating single-variant exploration links (1-based chr:pos:ref>alt), genomic locus views (1-based closed chr:start-end), candidate summary tables, and AlphaGenome reference vs. alternate predictions. Use whenever visualizing, exploring, charting, or linking genetic variants and genomic loci on the AlphaGenome Atlas, or when asked to inspect, view, or link predictions for a genomic variant.

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Query the Genome Aggregation Database (gnomAD). Use when determining the rarity or allele frequency of specific genetic variants, retrieving gene constraint metrics (pLI, LOEUF) to assess loss-of-function intolerance, finding variants in a genomic region or gene, or querying structural variants. Don't use for analyzing individual patient genomes, tracking somatic mutations in cancer (use COSMIC), or requesting raw sequencing reads (use ENA).

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Deep learning-based variant calling from long reads using Clair3 for SNPs and small indels. Use when calling germline variants from ONT or PacBio alignments, particularly when high accuracy is needed for clinical or research applications.

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Filter and prioritize variants by pathogenicity, population frequency, and clinical evidence for rare disease analysis. Use when identifying candidate disease-causing variants from exome or genome sequencing.

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Test whether two traits share a causal variant at a genomic locus using Bayesian colocalization with coloc. Computes posterior probabilities for shared vs distinct causal variants between GWAS and eQTL signals. Use when determining if a GWAS signal and an eQTL share the same causal variant.

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Identify likely causal variants within GWAS loci using SuSiE for sum of single effects regression and FINEMAP for shotgun stochastic search. Computes posterior inclusion probabilities and credible sets to prioritize variants for functional follow-up. Use when narrowing GWAS association signals to candidate causal variants or building credible sets for functional validation.

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Query gnomAD for population allele frequencies to assess variant rarity. Use when filtering variants by population frequency for rare disease analysis or determining if a variant is common in the general population.

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Detect crowded speculative positioning in CFTC futures markets (COT report analysis) to find contrarian setups using Jason Shapiro's methodology. Screens large-speculator ("non-commercial") net positioning across 65 futures markets (indices, rates, FX, metals, energy, crypto) via the FMP Commitment of Traders API, computes a 3-year and 26-week COT Index per market, and classifies extremes as CROWDED_LONG / CROWDED_SHORT. Use when the user asks about COT report analysis, crowded positioning, "who is trapped", speculative positioning extremes, contrarian futures setups, or wants to run Jason Shapiro-style analysis. This skill automates crowding DETECTION only (step 1 of 5) — it does not generate trade signals by itself.

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Use when the user asks to "generate subject line variants", "pre-score my subject lines", or "will this subject get truncated / trigger spam filters"; produces a labeled subject + preheader variant set and a per-variant heuristic pre-score card — spam-trigger flags, length/truncation across desktop + mobile, emoji-count, and the inbox preview render (from-name + subject + preheader) — before any test is run. Not for the body copy or CTA — use email-creative-builder; not for the A/B test design or significance read — use send-experiment-designer; not for the profile-weighted EQS or the S1/S2/N1/D1 vetoes — use email-quality-auditor. 邮件主题行生成/主题行预打分/截断与垃圾词检查

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Advises on Amazon RDS open-source engines (MySQL, MariaDB, PostgreSQL) for instance creation, upgrade planning, commitment pricing, proxy evaluation, and Blue/Green deployments. Handles any RDS MySQL, MariaDB, or PostgreSQL question, including create a production-ready RDS MySQL instance, provision an RDS PostgreSQL database, run the RDS upgrade advisor for my RDS MySQL instance, what are my upgrade options, upgrade RDS MariaDB from 10.6 to the latest version, should I buy reserved instances or a savings plan for db.r7g.2xlarge RDS MySQL, change a VARCHAR to INT column on RDS MySQL 8.0 with Blue/Green, and does RDS Proxy help when PgBouncer already runs in transaction mode. Covers instance creation with production best practices, describe-db-instances and describe-db-engine-versions upgrade-target workflow, live prechecks via SSM or direct connection, RI versus DSP commitment pricing, RDS Proxy versus PgBouncer, and Blue/Green lifecycle with binlog replay compatibility.

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Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.

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Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.

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Comprehensive manuscript review with three modes: single-pass (default), --adversarial critic-fixer loop, and --peer [journal] simulated peer-review pipeline (editor + 2 dispositioned referees + editorial decision, calibrated to a target journal). R&R continuation via --peer --r2/--r3; hostile-editor stress test via --peer --stress; reviewer-disposition variance reporting via --peer --variance N. Auto-invokes /review-r + /audit-reproducibility on referenced scripts unless --no-cross-artifact.

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