「arm」の検索結果

Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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。

Write and run B2B cold outbound that gets replies, from cold emails and follow-ups to sending setup, LinkedIn, multichannel cadences, and reply handling. Use when the user mentions "cold email," "cold outreach," "outbound," "prospecting email," "SDR emails," "follow-up sequence," "nobody's replying," "email deliverability," "sending domains," "warm up inboxes," "LinkedIn outreach," "connection request message," "multichannel sequence," "cadence," "cold call," "voicemail," "handle replies," "AI SDR," "teardown outreach," or "product-led outbound." Send from secondary domains, verify lists first, turn off open tracking, and stop every channel when a prospect replies. Copy avoids AI tells like 'it's not X, it's Y' reveals. For building the list and account research, see prospecting. For lifecycle email, see emails. For call scripts and sales collateral, see sales-enablement.

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, inspects, tests, and analyzes bounded process-based discrete-event simulations with SimPy. Use for event scheduling, resource queues, interrupts, monitoring, independent replications, warm-up, and reproducible output 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。

Processes, cleans, compares, and searches tandem mass spectra with matchms. Use for MS/MS file I/O, metadata harmonization, peak filtering, spectral similarity, library matching, score matrices, and molecular-similarity networks. Use pyopenms instead for LC-MS feature detection or proteomics pipelines.

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。

Featurizes small molecules with Molfeat for QSAR/QSPR, chemical similarity, virtual screening, and molecular ML. Covers ECFP/MACCS fingerprints, RDKit descriptors, pharmacophores, pretrained embeddings, configuration persistence, and molecule-to-label alignment.

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。

Analyze text and images for harmful content with customizable blocklists.

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 Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

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。

AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture.

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 structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.

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。

Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.

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 user mentions connect/disconnect wallet, sign in, sign out, web3 wallet, wallet address, check balance, how much crypto do I have, send BNB/USDT/crypto, transfer tokens, swap tokens, buy/sell token, DEX trade, limit order, market order, cancel order, get a quote, transaction history, wallet settings, daily limit, slippage, MEV protection, supported chains, available networks, prediction market, predict.fun, YES/NO market, place a prediction, redeem winnings, claim payout, prediction portfolio, prediction PnL, x402 payment, HTTP 402 Payment Required, pay a known x402 API, check approvals, view token approvals, revoke approval, manage approvals, wallet approvals, authorization management, token authorization, DeFi protocols, DeFi position, DeFi portfolio, staking, liquidity pool, LP, yield farming, health factor, APY, TVL, DeFi investment, DeFi deposit, DeFi redeem, DeFi stake, DeFi unstake, add liquidity, remove liquidity, claim rewards, claim fees, or any on-chain wallet operation.

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。

Triage and resolve Debian Linux issues with apt, systemd, and AppArmor-aware guidance.

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。

Reusable investigation patterns for AWS CloudWatch: Logs Insights query templates, alarm-to-deployment correlation, blast-radius narrowing decision tree, and PromQL-style metric query patterns for structured incident triage.

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。

Cancel any active OMC mode (autopilot, ralph, ultragoal, swarm, ultrapilot, pipeline, team) and clean up retired legacy state

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。

Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.

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。

Professional finance research toolkit — backtesting (11 engines + benchmark comparison panel), factor analysis, Alpha Zoo (478 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract → backtest → render) across 29 market-data sources (tushare, yfinance, okx, binance, akshare, baostock, tencent, mootdx, ccxt, futu, mt5, tickerall, local, eastmoney, sina, stooq, yahoo, pykrx, india_broker, qveris, longbridge, nobitex, wallex, plus optional-key finnhub/alphavantage/tiingo/fmp/gildata).

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。

DeFi yield analysis and optimization — lending rates, LP yields, staking returns, yield farming strategies, risk-adjusted yield comparison, and protocol-level sustainability assessment.

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。

Install and run Stratus Red Team to detonate granular, MITRE ATT&CK-mapped AWS, Azure, GCP, and Kubernetes attack techniques through their warmup-detonate-revert-cleanup lifecycle, then verify the telemetry they generate. Use to validate that cloud detections (CloudTrail, GuardDuty, Microsoft Sentinel, GCP SCC, Falco) actually fire, or to run a repeatable purple-team exercise generating realistic attacker telemetry.

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。

Analyze malicious Linux ELF binaries — botnets, cryptominers, ransomware, and rootkits targeting Linux servers, containers, and cloud infrastructure — through static analysis, dynamic tracing, and reverse engineering of x86_64 and ARM samples. Use when investigating Linux malware, triaging a suspicious ELF binary, assessing a compromised Linux server, or analyzing container-targeted malware.

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。

Cancel any active OMX mode (autopilot, ralph, ultrawork, ecomode, ultraqa, swarm, ultrapilot, pipeline, team)

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。

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

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。

Research a specific person using Common Room data. Triggers on 'who is [name]', 'look up [email]', 'research [contact]', 'is [name] a warm lead', or any contact-level question.

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 user needs to query, filter, reshape, extract, create, or construct JSON data — including API responses, config files, log output, or any structured data — or when helping the user write or debug JSON transformations.

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 creating a new builtin skill for Crush, editing an existing builtin skill (internal/skills/builtin/), or when the user needs to understand how the embedded skill system works.

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。