198 件 ・ 関連度順
概要と使いどころ
Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.
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 Arcade MCP to create custom MCP servers and tools with OAuth-aware authorization, evals, and deployment paths for agent tool-calling workflows.
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。
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.
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。
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.
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 somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number 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。
Framework para construir aplicações com LLM usando agentes, chains e RAG. Suporta múltiplos provedores (OpenAI, Anthropic, Google), 500+ integrações, agentes ReAct, tool calling, gerenciamento de memória e recuperação de vector stores. Use para construir chatbots, sistemas de perguntas e respostas, agentes autônomos ou aplicações RAG. Ideal para prototipagem rápida e deployments em produção.
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。
Complete operational guide for the NV oOS (Open Operator System) WordPress plugin in Docker/WSL2 — setup, assistant creation, credential tokens, MCP tool calling, API key auto-detection, env var bridging, common fixes, and IGCSE study configuration. Use when setting up the plugin for the first time, creating assistants programmatically, generating MCP bridge tokens, troubleshooting Docker path issues, bridging API keys, or calling tools via JSON-RPC over HTTP.
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。
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
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。
Orchestrates an end-to-end MeRIP-seq / m6A-seq analysis from raw FASTQ to differential m6A peak calls and metagene plots, chaining fastp adapter trimming, STAR splice-aware alignment (NO deduplication for non-UMI MeRIP), deepTools replicate-concordance + IP-enrichment QC, PreSeq saturation curves, exomePeak2 (transcript-aware Poisson GLM) peak calling, optional MACS3 broad-peak cross-check, DRACH motif confirmation as a sanity check (NOT a per-peak filter), exomePeak2 differential calling via the four-BAM-vector interface (bam_ip + bam_input control; bam_treated_ip + bam_treated_input treatment), ChIPseeker annotation, and the canonical Guitar metagene with stop-codon enrichment as the biological QC anchor. Use when running a complete MeRIP analysis from raw reads, when chaining the constituent epitranscriptomics skills (merip-preprocessing -> m6a-peak-calling -> m6a-differential -> modification-visualization), or when wrapping the pipeline in Snakemake / Nextflow.
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 somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number 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。
INVOKE THIS SKILL when adding Arize AX tracing to an application. Follow the Agent-Assisted Tracing two-phase flow: analyze the codebase (read-only), then implement instrumentation after user confirmation. When the app uses LLM tool/function calling, add manual CHAIN + TOOL spans so traces show each tool's input and output. Leverages https://arize.com/docs/ax/alyx/tracing-assistant and https://arize.com/docs/PROMPT.md.
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。
Automate Callingly tasks via Rube MCP (Composio). Always search tools first for current schemas.
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。
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool use, function calling.
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。
Develop AI-powered applications using Genkit in Go. Use when the user asks to build AI features, agents, flows, or tools in Go using Genkit, or when working with Genkit Go code involving generation, prompts, streaming, tool calling, or model providers.
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。
Automate Callingly tasks via Rube MCP (Composio). Always search tools first for current schemas.
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 launching anything that runs longer than a minute, anything that streams logs, anything you want to keep running while doing other work — or when terminal_exec auto-backgrounded on you and returned a job_id. Teaches the start→poll→wait pattern with terminal_job_logs offset bookkeeping, bounded blocking polls, platform-specific process control via terminal_job_manage capabilities, and the hard rule that jobs die when the terminal-tools server restarts. Read before calling terminal_job_start, or right after terminal_exec auto-backgrounded.
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。
Dispatch WGS, WES, or targeted DNA variant requests to germline, somatic, or UMI-panel skills, then plan public nf-core/sarek, GATK4, DeepVariant, samtools, or bcftools workflows.
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。
Builds AI agents on Cloudflare using the Agents SDK with state management, real-time WebSockets, scheduled tasks, tool integration, and chat capabilities. Generates production-ready agent code deployed to Workers. Use when: user wants to "build an agent", "AI agent", "chat agent", "stateful agent", mentions "Agents SDK", needs "real-time AI", "WebSocket AI", or asks about agent "state management", "scheduled tasks", or "tool calling". Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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 a GraphQL endpoint, OpenAPI spec, or MCP server into bash CLI commands and a `tools.*` JS API runnable inside `just-bash`'s `js-exec` sandbox. Use when the user wants to expose a remote API to a sandboxed bash agent, build a tool-calling agent on top of just-bash, or generate CLI commands from an existing API spec.
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 AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent agents", "agent threads", "agent runs", "streaming agents", "function calling agents .NET".
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 THIS agent needs to pay for x402-protected content at runtime: hitting a paywall mid-task, settling it via AgentCore Payments, and applying operator-defined spend limits. Covers payment setup, policy, session budgets, and troubleshooting. Triggers on: "my agent hit a 402 while calling an API", "a tool call returned 402 Payment Required", "my agent needs to pay for x402-protected content", "let the agent pay for content, capped at $5 per session", "set a spend limit for the agent", "ProcessPayment failed", or "why did my agent refuse to pay". Not for BUILDING payment capability for end users, including wallets and framework middleware; use agents-build and references/payments.md. For non-paid APIs via Gateway use agents-connect. For inbound auth use agents-harden. For project scaffolding use agents-get-started.
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。
Run inference on Fireworks AI — direct, through the Leanmcp AI Gateway proxy (aigateway.leanmcp.com) for observability, or via LiteLLM / the OpenAI SDK / raw HTTP. Covers the endpoint-and-key matrix for each route, serverless vs dedicated deployments and their cold starts, browsing and filtering the model catalogue, reasoning models that return content:null with the answer in reasoning_content, function calling, concurrency and load testing, and runner scripts where many models share one protocol with a --use-proxy toggle. Use this skill whenever the user mentions Fireworks, FIREWORKS_API_KEY, api.fireworks.ai, fireworks_ai/ model ids, accounts/fireworks/models/..., a Fireworks deployment id, the Leanmcp gateway or LEANMCP_API_KEY, app.leanmcp.com observability, or gpt-oss / kimi / glm / qwen / deepseek served on Fireworks; wants to route LLM calls through a proxy for logging or cost tracking; is debugging empty responses, 404s, scope errors, cold starts, or "the gateway isn't working"; wants to pick a Fireworks model with tool calling; or wants to sweep several models over one benchmark. Reach for it even on vague asks like "why is this model returning nothing" or "set up inference for these models" when Fireworks is in play.
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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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 pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.
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。