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Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.

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。

Enumerates virtual chemical libraries via reaction SMARTS transformations using RDKit and Reaction templates, with explicit handling of atom mapping, template extraction (RDKit reaction mining), product validation, RECAP/BRICS fragmentation, R-group decomposition, matched molecular pair analysis (MMPA), and Free-Wilson analysis. Use when generating combinatorial libraries from building blocks, enumerating analog series, deriving structure-activity rules, or extracting transformations from reaction 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。

Estimate bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).

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。

Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CTCF-only/DNase-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.

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。

Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.

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。

Use when users need bioinformatics analysis on the NCPGR cluster, especially when converting analysis requests into local software choices, resource plans, and LSF job scripts.

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 when the user asks about CMV operation types — Dedicated, OTR (Over-the-Road), Regional, Local, and the differences in driver lifestyle, pay, equipment, home time, hours, and customer mix. Reference industry segmentation conventions.

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 a carrier asks how to calculate cost-per-mile (CPM) at the fleet, truck, lane, or driver level — separating fixed vs variable costs, allocating overhead, accounting for empty miles, and using CPM to negotiate rates, build pricing tiers, or evaluate lane profitability.

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 failure modes in Coval runs, uploaded conversations or local conversation exports using scoped sampling and transcript, audio and trace evidence. Prepare human review without inventing ground truth or launching new runs.

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。

Comprehensive SEO analysis for any website or business type. Full site audits, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, and GEO for AI Overviews/ChatGPT/Perplexity. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed.

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-page Lighthouse audit via the MIT-licensed Unlighthouse CLI. Free-tier alternative to running PageSpeed against every URL on a site, no API quota burn, runs locally.

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。

Moves a person's whole Notion workspace into AI Matrx using only the AI Matrx and Notion connectors — every page and sub-page becomes an AI Matrx page (a Space) with its blocks, icons, covers, files and comments, and every database becomes a real table with typed columns, links, roll-ups, row pages, templates and views. Safe to run again; nothing duplicates. Use when the person says "move my Notion into AI Matrx", "copy my Notion", "import my Notion", or anything like it.

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。

Decision-ready scouting record for a user-connected provider access campaign. Use when an assigned Provider Access Launch task needs its easiest official route, minimum scopes, cost, account needs, approval path, implementation consumer, and easy-execute verdict established before submission. NOT for client Local Listings distribution.

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。

Delegating work down to subagents: briefs, report statuses, independent review. Use when writing an implementer or verifier brief, dispatching a subagent to build, fix, or verify work you own, or a subagent reports DONE, blocked, or concerns. NOT for campaign-scale doctrine (use campaign-pattern).

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。

Discovery batch that grows the Provider Access Launch queue. Use when asked to find more user-connected OAuth, API, API-key, remote MCP, marketplace, or partner-access campaigns for a capability, provider family, MCP/plugin catalog, or competitive gap. NOT for client Local Listings distribution.

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。

Relocating a live, still-used table to another Postgres schema with every reference intact. Use when asked to move or rehome a table into a schema, or to pull a table out of public into a domain schema. NOT for retiring a table (use db-deprecate-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。

The gate and path for any question an agent wants Arman to decide. Use when about to ask him anything, ending a turn on a question for him, writing 'needs Arman's decision', marking work blocked on him, or on /ask-arman. NOT for a step only he can perform (use a guided session).

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。

Retiring a dead table by moving it to the deprecated schema and clearing every reference in both repos. Use when asked to deprecate or retire a table, when a table is no longer used, or to take a table offline without dropping it. NOT for relocating a still-used table (use db-move-table-schema).

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 matrx-frontend side of the matrx-extend Chrome extension bridge. Use when adding or changing a FRONTEND_RPC action handler, a window-panels deep-link the extension opens, a headless API route it calls, or debugging a silent chrome.runtime.sendMessage or Broadcast round-trip. NOT for code in matrx-extend, matrx-local, or aidream (each has its own connect-* 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。

Permanent-delete grooming for leftover Docker builds, agent worktrees, and .next caches. Use when creating or finishing a git worktree, after a local docker build, when the disk is full, or when /tmp or .wt trees were left behind.

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。

Agent fallback for CEngineServer_IsLogEnabled (auto-generated, category: func). Locate CEngineServer_IsLogEnabled in the CS2 engine module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CEngineServer_IsLogEnabled, CEngineServer_IsLogEnabled

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。

Agent fallback for CEngineServer_CreateFakeClient (auto-generated, category: vfunc). Locate CEngineServer::CreateFakeClient in the CS2 engine module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CEngineServer_CreateFakeClient, CEngineServer::CreateFakeClient

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。

Agent fallback for CEngineServer_GetPlayerNetworkIDString-inlined (auto-generated, category: func). Locate CEngineServer_GetPlayerNetworkIDString in the CS2 engine module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CEngineServer_GetPlayerNetworkIDString-inlined, CEngineServer_GetPlayerNetworkIDString

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。