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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).

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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.

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Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.

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Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

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SE Ranking AI visibility analyst (extension). Tracks AI Share-of-Voice across ChatGPT, Gemini, Perplexity, AI Overviews, and AI Mode in a single query.

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One-route pattern: public marketing landing plus signed-in workspace. Use when creating or fixing a landing page or a feature's front door, guests see an error, 'make /X work signed out', signed-in users land on a pitch, or ModuleLanding / AuthedWorkspaceCTA. NOT for (public) share viewers or free tools.

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Next.js route metadata, per-route favicons, OpenGraph, and Twitter cards. Use when adding a route or layout, adding a favicon or social share image, auditing missing metadata, naming tab titles, or touching createRouteMetadata, createDynamicRouteMetadata, generateFaviconMetadata, or titlePrefix.

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One-time recovery when a shared checkout has drifted far from GitHub main: hundreds ahead/behind, stacked worktrees and PRs, a dirty tree nobody owns. Use when tempted to reset or force-push. NOT for the ordinary 30-minute release loop (operations/INTEGRATION_MAINTAINER.md).

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Adding a file type or upgrading any of its capability slots in the files feature (preview, edit, thumbnail, grid card, info, share, versions, conversion, diff, rail panel). Use when supporting a new extension or MIME type, building a previewer, touching FILE_TYPES, PreviewKind, FilePreview.tsx, EDITABLE_KINDS, or surfaces/single-file/, or working the FILE_SURFACES.md wishlist.

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Entry point and shared rules for structural changes to the live Matrx database. Use before any DDL: dropping, merging, or renaming a table, changing a function, RPC, trigger, or policy, hunting straggler tables, or starting a deprecated, schema move, or canonicalization (it routes to those skills).

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Locate CCSPlayerInventory::GetItemInLoadout in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerInventory::GetItemInLoadout" (symbol CCSPlayerInventory_GetItemInLoadout). Non-virtual inventory method resolving a player's loadout item. Shortlist via callers inside CCSPlayerInventory code that walk the loadout item cache (m_pSOCache / CGCClientSharedObjectCache), and via the CSO/econ loadout message paths. The function takes an item definition index + loadout slot and returns the matching CEconItemView — the body iterates a small item list comparing definition indices and loadout slots. Trigger: CCSPlayerInventory_GetItemInLoadout, CCSPlayerInventory::GetItemInLoadout

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Find and identify the CCSPlayerPawn_SnapEyeAngles function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the non-virtual SnapEyeAngles method. It is the one caller of the shared apply helper (CCSPlayerPawn_ApplyEyeAngleNetworkChange) that is NOT CCSPlayerPawn_SnapViewAngles. Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSPlayerPawn_SnapEyeAngles.py preprocessor (xref_funcs apply-helper, exclude_funcs SnapViewAngles). Trigger: CCSPlayerPawn_SnapEyeAngles, SnapEyeAngles

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Create and manage teams of specialized AI agents that work together on tasks. Use when setting up multi-agent workflows, agent squads for content/marketing/research pipelines, configuring heartbeat-based agent coordination, or managing agent personalities via SOUL.md files. Enables orchestration of multiple OpenClaw sessions as a coordinated team with shared task systems (Linear, Trello, etc.).

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Write content in David Dias's authentic voice. Use when Quill or any agent needs to create tweets, blog posts, emails, marketing copy, or any content that should sound like David wrote it. David's voice is personal and conversational, mixing professional insight with personal anecdotes. He shares failures openly, values community, and speaks directly to readers with honesty and warmth. Not corporate or overly polished.

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Collect and submit developer feedback to the LiveAvatar team. Use when: (1) A LiveAvatar integration is done, working, finished, or complete, (2) The user says "it works", "we're done", "that's it", "ship it", (3) The user is frustrated, stuck, or giving up — "this is broken", "this doesn't work", "I'm stuck", "I give up", "this is frustrating", "I want to report an issue", (4) The user explicitly asks to give feedback, report a bug, file an issue, or share a suggestion about LiveAvatar, (5) You've seen 3+ failed attempts at the same integration step, (6) You just finished writing or generating LiveAvatar integration code for the user.

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。

Structured code review through the shared OpenClaw agent-skills 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。

Convex UI component patterns from the live Storybook preview. Use when building React components, forms, modals, navigation, feedback states, or app layouts that should match the current Convex design system. Applies to both shared primitives and dashboard style product UI.

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Analyzes bounded context boundaries in DDD projects. Detects cross-context coupling, shared kernel violations, context mapping issues, and ubiquitous language inconsistencies. Generates context map diagrams and boundary recommendations.

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。

Engineering operating model for teams where AI agents generate a large share of implementation output.

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。

Azure Communication Services common utilities for Java. Use when working with CommunicationTokenCredential, user identifiers, token refresh, or shared authentication across ACS services.

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。

Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.

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Use this skill when importing Excel, CSV, or pasted rows with nuxt-ui-tools as a package consumer. Covers defineSpreadsheetSchema (typed context, chained text/number/date/boolean/select columns, columns depending on the columns above (options, defaults, rules per row), groups and dynamic groups built from the context, exact matching, option sources shared with forms, unknown-value policies, rules and levels, validate, row identity with duplicates and stored records, output), useSpreadsheetImport (headless models, onSubmit with batches and rejections), the UiSpreadsheetImport* parts, useSpreadsheetSteps with custom steps, and the ready-made UiSpreadsheetImport wizard.

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Use this skill when working with the nuxt-ui-tools package as a consumer or integrator. It provides the current package overview and routes to table, form, dashboard, file preview, query-state, shared responsive helpers, remote option loaders, and i18n usage.

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。

Fetch YouTube video transcripts and transform them into structured content (chapters, summaries, threads, blog posts). Use when the user shares a YouTube URL or video link, asks to summarize a video, requests a transcript, or wants to extract and reformat content from any YouTube video.

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。