「aml」の検索結果

Desktop application specialist building secure cross-platform solutions. Develops Electron apps with native OS integration, focusing on security, performance, and seamless user experience.

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Expert payment integration specialist mastering payment gateway integration, PCI compliance, and financial transaction processing. Specializes in secure payment flows, multi-currency support, and fraud prevention with focus on reliability, compliance, and seamless user experience.

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。

Creates, debugs, and optimizes GitHub Actions workflow YAML files. Recommends current action versions with SHA pinning from a daily-updated index of 120+ actions. Configures matrix builds, dependency caching, reusable workflows, OIDC authentication, secrets management, and runner selection. Reviews existing workflows for outdated actions, security issues, and performance problems. Use when the user asks about GitHub Actions, workflows, CI/CD, .github/workflows, any action by name (actions/checkout, setup-node, setup-python, etc.), or workflow optimization.

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。

Generate Skills from HTTP MCP servers with async job patterns (submit/status/result). Use when converting MCP specifications (.mcp.json) into reusable Skills using mcp_tool_catalog.yaml. Supports --lazy mode for context-saving generation. Also use for calling async MCP tools via JSON-RPC 2.0 with session-based polling.

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。

Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.

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。

Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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。

Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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 the project follows API-first / OpenAPI-first approach: generating controller interfaces, DTOs, and clients from an OpenAPI spec. Use when you see openapi.yaml, openapi-generator-maven-plugin, or ApiDelegate pattern in the project.

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。

AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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.

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。

Convert Claude markdown subagent files to Codex CLI TOML format. Parses YAML frontmatter and body, applies model/permission/tool mappings, and outputs to agents/codex/<name>.toml.

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。

Streamline code by removing redundancy and aligning with repo style. Use when you need to consolidate logic, reduce indirection, clean up repeated patterns, or make code more concise.

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。

Helps understand and write EAS workflow YAML files for Expo projects. Use this skill when the user asks about CI/CD or workflows in an Expo or EAS context, mentions .eas/workflows/, or wants help with EAS build pipelines or deployment automation.

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。

Create, inherit, compose, localize, and validate YAML prototypes in the correct resource root.

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。

A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.

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。

AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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.

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。

Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.

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。

Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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。

Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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。

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

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。

Find and identify the CCSPlayerPawn_SnapViewAngles 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 SnapViewAngles method, anchored on the "setang" console-command usage string and its non-teleport call branch. Emits a unique IDA-style byte signature and a func YAML. Trigger: CCSPlayerPawn_SnapViewAngles, SnapViewAngles

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。

Find and identify the CDecoyProjectile_Create factory in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the decoy projectile spawn factory. It is a non-virtual, direct-call static factory found via the entity classname string "decoy_projectile" it references (excluding the RTTI class string "CDecoyProjectile"). Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CDecoyProjectile_Create.py preprocessor (xref_strings FULLMATCH:decoy_projectile). Trigger: CDecoyProjectile_Create, CDecoyProjectile::Create, decoy projectile Create

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。

Find and identify the CCSGameRules_ResetRound function in CS2 server binary using IDA Pro MCP. Use this skill when reverse engineering CS2 server.dll or libserver.so to locate the round-reset routine. It is the only function that references the "GMR_ResetRound\n" debug log string, and is the predecessor anchor for find-CCSGameRules_PostCleanUp (which it calls). Emits a unique IDA-style byte signature and a func YAML. Mirrors the find-CCSGameRules_ResetRound.py preprocessor (xref_strings "GMR_ResetRound\n"). Trigger: CCSGameRules_ResetRound, ResetRound, GMR_ResetRound

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。