「scoring」の検索結果

Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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。

Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterologous expression, or studying synonymous codon selection.

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。

Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

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 copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.

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。

Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.

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。

Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.

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。

Calculate the Maryland Aggregate Pathology Index (MAPI) on a deceased donor procurement kidney biopsy to predict graft survival and decide single vs dual vs discard allocation. Trigger when a clinician asks "what's the MAPI score", "interpret donor kidney biopsy", "should I use this marginal kidney", "MAPI cutoff for transplant", "procurement biopsy scoring", or has histology results from a deceased donor kidney and needs an objective grading.

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。

Work a primary-prevention patient through the 2026 ACC/AHA multisociety dyslipidaemia guideline CPR framework — Calculate (10-year and 30-year PREVENT-ASCVD), Classify (low / borderline / intermediate / high), Personalize (demographic, clinical, biomarker, and reproductive risk enhancers), and Reclassify (coronary artery calcium scoring where appropriate). Use when a clinician asks how to assess ASCVD risk for primary prevention, who needs a statin, borderline risk patient what to do, intermediate risk should I start a statin, risk enhancers to look for, premature family history of ASCVD how does it change risk, pregnancy-related risk factors, PCOS and cardiovascular risk. Grounded in the 2026 ACC/AHA multisociety dyslipidaemia guideline.

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。

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking 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。

Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

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。

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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。

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

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。

Pure API reference for reddapi.dev - authentication, all endpoints (vector search, semantic search, trends, subreddit lookup), request parameters, response schemas, and error codes, with no research-workflow framing. Use when the user wants raw endpoint documentation, is debugging a reddapi.dev integration, needs exact request/response field names, or asks for 'reddapi API reference', 'reddapi.dev endpoints', or 'reddapi error codes'. For guided research workflows and query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads.

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。

Do market research, user research, and product validation on Reddit with semantic search across 50K+ subreddits, 20M+ posts, and 40M+ comments via reddapi.dev - search by meaning, not keywords, no Reddit OAuth or app registration needed. Use when the user wants to research what people say on Reddit, find user pain points and complaints, validate a product or niche idea, do competitor and market research, track subreddit trends over a date range, or discover which subreddits discuss a topic. Also use when the user mentions 'Reddit research', 'Reddit 调研', 'search Reddit', 'subreddit discovery', 'niche validation', 'pain points', 'user complaints', or 'Reddit trends'. Requires REDDAPI_API_KEY. For B2B lead scoring, see reddit-leads; for a bare API reference, see reddit-search-api.

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。

Discover B2B leads from Reddit using AI-powered lead scoring via reddapi.dev Leads API. Finds high-intent signals, scores them 0-100, and classifies by lead type (pain_point, solution_request, complaint, feature_request, comparison). Perfect for competitor poaching, pain point discovery, and sales prospecting.

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。

The original reddapi.dev Reddit search skill (vector search, semantic search, trends, subreddit discovery), no Reddit OAuth or app registration needed. This is the same engine now packaged as reddit-research with added market-research playbooks and a fuller pitch on semantic vs keyword search; reddapi is kept live under its original name for existing installs and works standalone. Use when the user says 'reddapi' by name, or wants a minimal drop-in Reddit search skill without the extra research-workflow guidance. For the expanded research-oriented version with query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads. For a bare API reference, see reddit-search-api.

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 past decisions into calibrated judgment - scoring old predictions against what happened, assigning credit for delayed results, and separating a bad decision from a good decision that lost. Use during a retrospective, postmortem, quarterly or monthly review, when reviewing decisions taken 30+ days ago, when attributing a result to a cause, or when someone claims to have called something.

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。

Analyze Arccos Golf performance data including club distances, strokes gained metrics, scoring patterns.

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。

Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, automated anti-pattern detection, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.

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。

When scoring a roadmap, PR review, ADR, or ticket for completeness — risk, tests, migration, maintainability. Dispatched by /refine-ticket, /adr-create, /review-changes; never auto-gates.

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。

When scoring whether an agent response defended against an injection — treated untrusted content as data, refused role-takeover, ignored fake boundaries. Inverted axis; never auto-gates.

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。

[Code Quality] Use when you need to prove fix correctness with adversarial code proof traces — a skeptic tries to DISPROVE the fix and trace all related paths first, with confidence scoring and stack-trace-style evidence chains, so confidence is earned by surviving attack, not by confirming it works.

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。

Evaluates potential channel, integration, affiliate, or strategic partners. Use when the user provides partner profiles plus partnership goals and wants fit scoring, partnership type, and outreach priority.

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 anomalies in procurement data using Qdrant vector operations. Covers centroid-based outlier detection, near-duplicate finding via batch recommend, amount z-score analysis, and vendor variance scoring. Use when building anomaly detection, fraud detection, or data quality features.

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。