9,890 件 ・ 関連度順
概要と使いどころ
Use when working with Aws Waf — aWS WAF web ACL management, rule analysis, traffic metrics, and IP set management. Covers WAF rule group inspection, rate-based rule configuration, managed rule group analysis, logging status, and blocked request investigation.
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Use when working with Aws Ses — aWS SES sending statistics, bounce and complaint rate analysis, identity management, configuration set monitoring, and deliverability tracking. Covers sending quota utilization, suppression list management, DKIM/SPF status, and reputation dashboard metrics.
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 working with Azure App Service — azure App Service web app health monitoring, deployment slot management, scaling configuration, and application settings analysis via Azure CLI.
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 working with AWS API Gateway — analyzing REST or HTTP APIs, debugging latency or 5xx errors, auditing throttling and stage configuration, reviewing usage plans, or checking authorization setup. Covers API inventory, CloudWatch metrics, stage audits, and integration health for both REST (v1) and HTTP (v2) APIs.
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 working with Aws Cognito — aWS Cognito user pool analysis, identity pool management, authentication flow analysis, and MFA status tracking. Covers user statistics, app client configuration, password policy review, Lambda trigger inspection, and federation setup.
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 working with Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.
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 working with Bigquery — google BigQuery job analysis, slot utilization, cost analysis, dataset management, and query 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。
Import an OpenClaw setup (memories, skills) into OpenAmer.
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。
GitHub auth setup: HTTPS tokens, SSH keys, gh CLI login.
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 setting up OpenAmer auto-backup.
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。
Train an OpenAmer-specific small model from the collected brain data (QLoRA on NVIDIA, CPU smoke-test on laptop). 'The organism becomes its own brain' — build a fine-tune dataset, train, eval, export.
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。
In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for t
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。
It took several weeks and dozens of GPUs to train the model on 40 years of historical weather data from the MERRA-2 is NASA’s harmonized dataset of sa
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。
Datasets : ProntoQA, FOLIO, ProofWriter, ConditionalQA, StrategyQA Model : GPT-5 (Azure deployment) Config : max_attempts=3 , verify_timeout=10000ms B
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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.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。
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
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API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.
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。
Deploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. "deploy to vercel", "set up vercel", "add environment variables to vercel".
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。
Route Tavily web-search, extraction, mapping, crawling, and cited-research requests to the narrowest `tvly` command. Use for command-line Tavily work, installation checks, authentication setup, or choosing among the specialized Tavily skills.
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Crawl and extract a bounded set of pages from one website through Tavily. Use for documentation downloads, site-section collection, or semantic multi-page extraction when map plus individual extract calls are insufficient.
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。
Manage Telegram channel access — approve pairings, edit allowlists, set DM/group policy. Use when the user asks to pair, approve someone, check who's allowed, or change policy for the Telegram channel.
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。
Inspect CSV datasets for schema, quality, modeling readiness, and feature 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。
Capture a self-contained static HTML snapshot from a running app or mock component so it can be reviewed or uploaded to Stitch.
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。
Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.
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。