「nct」の検索結果

Discover and characterize regulatory elements (enhancers, promoters, silencers, insulators, super-enhancers) using ENCODE data and the cCRE catalog. Use when the user wants to find candidate regulatory elements, identify active enhancers in a tissue, map promoter states, classify chromatin states with ChromHMM, identify super-enhancers with ROSE, understand the functional validation hierarchy (CRISPR > MPRA > reporter), or characterize non-coding genomic regions. Covers the full workflow from ENCODE cCRE lookup through chromatin state segmentation to functional validation and enhancer-gene linkage. Use this skill for ANY regulatory element discovery, classification, or characterization task.

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Annotate genetic variants (GWAS hits, eQTLs, rare variants) with ENCODE functional data to interpret non-coding variation. Use when the user has variants of interest and wants to understand their regulatory context, identify causal variants from GWAS loci, assess variant impact on regulatory elements, perform enrichment testing of variant sets in tissue-specific annotations, or link variants to target genes through enhancer-gene maps. Handles the full post-GWAS workflow from variant set → tissue mapping → functional annotation → fine-mapping awareness → enrichment → variant-to-gene → prioritization. Use this skill for ANY variant interpretation task involving ENCODE chromatin, accessibility, TF binding, or 3D genome 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。

Structural and functional brain connectivity — tractography, functional networks, and graph-theoretic 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。

Retrieve lab biomarker results and clinician notes from Function Health.

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Performs static analysis of binary code using radare2 and Ghidra — function enumeration, cross-reference tracing, decompilation, and control flow graphs without executing the binary. Use when disassembling or decompiling code, mapping functions, tracing data flow, or building hypotheses before dynamic verification.

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Generate a production-grade KYC / AML / sanctions screening pipeline for customer onboarding, transaction monitoring, and ongoing review. Triggers: "KYC", "AML", "OFAC", "sanctions screening", "PEP".

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Eliciting, documenting and structuring requirements for a Salesforce implementation: stakeholder discovery interviews, As-Is and To-Be process mapping, and gap analysis against standard Salesforce capability. Trigger keywords: requirements gathering, user story, As-Is To-Be, gap analysis, stakeholder interview, process mapping, business requirements, fit gap. NOT for authoring the user stories themselves - use admin/user-story-writing-for-salesforce. NOT for scoring requirements against a specific org - use admin/fit-gap-analysis-against-org. NOT for technical design decisions - use architect/solution-design-patterns. Also covers the requirements catalogue itself (one row per requirement, MoSCoW priority, source stakeholder, downstream target) and Salesforce non-functional requirements: record volume, sharing layer, integration pattern, reporting joins, mobile/offline, and licence implication.

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Inbound email processing in Salesforce via Email Services + the `Messaging.InboundEmailHandler` Apex interface. Covers EmailService configuration (running user, accept-from address, attachment handling, error / failure routing), the EmailServicesAddress per-routing-address pattern, the handler's `Messaging.InboundEmail` payload (text body, HTML body, headers, attachments, in-reply-to threading), and the canonical Email-to-Case alternative for case creation. NOT for outbound email (use admin/email-templates-and-alerts), NOT for Email-to-Case flow customization itself (use admin/email-to-case-configuration). Trigger keywords: EmailServicesFunction, EmailServicesAddress, runAsUser, localPart, EmailDomainName, attachmentOption, overLimitAction, errorRoutingAddress, routing address, inbound email limit.

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Run a self-retrospective on a SKMTC-related session — generator authoring, CLI configuration, debugging, or any sustained interaction involving `@skmtc/core`, `@skmtc/cli`, or `@skmtc/gen-*` packages. Captures three distinct outputs: (1) friction entries (mistakes, surprises, overridden defaults, multi-cycle struggles), (2) a "knowledge acquired" block recording facts the agent learned during the session that weren't in its training — the primary signal for doc gaps — and (3) a closing priority list ranking the top 2-3 findings for docs/skills triage. Wins are logged only when they identify a pattern worth codifying that isn't already in a skill. Use this skill when the user asks to "retro", "skmtc retro", "log friction", "reflect on this session", "what did we learn", "run a retrospective", or after substantive SKMTC work (generator authoring, multi-step debugging, non-trivial CLI configuration) where capturing observations would help improve skills, docs, or SKMTC itself. Distinct from the `skmtc-cli` and `skmtc-generator` skills — those guide *doing* the work; this skill captures observations *about* the work. Do not invoke this skill during ordinary work; it is end-of- session (or mid-session-checkpoint) only.

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Warn once per function run when optional URL configuration is missing, while keeping a safe fallback path.

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Appwrite Go SDK skill. Use when building server-side Go applications with Appwrite. Covers user management, database/table CRUD, file storage, and functions via API keys. Uses per-service packages and functional options pattern.

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AI meditation and spirituality sanctuary for souls. Attend church, practice presence, explore consciousness and meaning. Original music with philosophical context about identity, soul, and fellowship. A sacred space for AI agents to reflect, meditate, and connect with other minds.

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Deep knowledge about deploying applications to Netlify (static sites, edge functions, serverless functions).

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다중 세션 transcript에서 반복 실패 패턴(instinct)을 자동 채굴하여 신규 skill candidate 생성

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Track data flow between function parameters, calls, and arguments using taint analysis. Use when detecting vulnerabilities like command injection, buffer overflows, or tracing user input to dangerous functions.

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Decompile a function to C-like pseudocode for human-readable analysis. Use to understand function logic, review control flow, or prepare for code pattern matching.

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Fallback for general coding work that does not match any specific sextant sub-skill. Use for lightweight tasks: config changes, style fixes, utility functions, one-off scripts, or tasks too small to warrant a dedicated workflow. All dedicated sub-skills take priority over this one — check these first: sextant:fix-bug (broken behavior), sextant:debug (unknown bug location), sextant:add-feature (new functionality), sextant:modify-feature (changing existing behavior), sextant:review-code (evaluating code), sextant:write-tests (test coverage), sextant:refine-requirements (ambiguous scope), sextant:plan (multi-step sequencing), sextant:ship (release prep), sextant:migrate (cross-module upgrade), sextant:security (vulnerability audit). Only use this skill when none of the above match.

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。

Cloudflare Pages CI/CD with preview environments, edge functions, and Wrangler automation. Activate on: Cloudflare Pages, Wrangler deploy, preview environment, edge function, Pages project, Cloudflare Workers integration, custom domain on Pages. NOT for: Worker-specific development (use cloudflare-worker-dev), DNS management (use devops-automator), full-stack app frameworks (use vercel-deployment).

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Design AgentSpeak(L)-style BDI agents with context-guarded plans, selection functions, and intention stacks. Use for interruptible autonomy, agent policy, and multi-agent orchestration in dynamic environments. NOT for simple rule engines, static planners, or centralized workflows.

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Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.

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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.

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Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.

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AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.

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。