「sel」の検索結果

Query Fintel (fintel.io) institutional market data through its REST API or official MCP server. Use this skill whenever the user mentions Fintel or asks about short interest, short interest ratio or days to cover, short squeeze data, borrow rate or cost to borrow, shares available to borrow, daily short volume, fails-to-deliver (FTD), 13F institutional owners ("who owns X"), insider buying or selling from SEC Form 3/4/5, analyst price targets, ratings and forecasts, dividend or earnings history and calendars, EOD or last prices, security lookup by ticker, CUSIP, ISIN, or FIGI, Fintel leaderboards, or their Fintel watchlists and alerts. Read-only: GET endpoints only.

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 sell-side analyst estimates and how they are changing, using Yahoo Finance data (yfinance): EPS and revenue consensus by period, estimate ranges and dispersion, revision trends over 7/30/60/90 days and up/down revision breadth, growth estimates vs industry, sector, and the S&P 500, and historical estimate accuracy. Use this skill when the user wants more than a single estimate lookup: estimate revisions or momentum, EPS trend, consensus changes, forward or next-quarter and annual estimates, the bull vs bear estimate spread, or growth projections across periods.

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。

Query TradingView market data through the bundled tradingview MCP server without a desktop app or login. Use whenever the user wants a technical analysis readout (RSI, MACD, Bollinger rating, BUY/SELL summary), multi-timeframe alignment (weekly→daily→4h→1h→15m), exchange-wide scans (Bollinger squeeze, volume breakout, consecutive candles, smart volume), top gainers/losers by exchange, futures market data (NQ/ES/CL/GC overview, overnight movers, category snapshots), pre-market / after-hours quotes, a quick options chain or unusual options activity (volume/OI by strike), bulk stock prices, a global market snapshot, strategy backtests (Sharpe, drawdown, win rate), or crypto exchange scans. Triggers include "TA on X", "multi-timeframe read", "BB squeeze scan", "futures movers", "NQ overnight", "pre-market price", "unusual options activity", "backtest RSI on AAPL", and "top gainers on NASDAQ". For options greeks, watchlists, alerts, or chart screenshots use tradingview-reader instead.

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。

Check the live status of the Strait of Hormuz from the Hormuz Strait Monitor dashboard: open, restricted, or closed status, tanker transits vs normal, stranded vessels, Brent price impact, war-risk insurance levels, cargo throughput, tanker freight rates, diplomatic developments, and global trade impact. Use this skill whenever the user asks about the Strait of Hormuz or the Persian Gulf in the context of oil, shipping, or geopolitical risk — whether Hormuz is open, chokepoint or transit disruption, Gulf tanker traffic, war-risk premiums, Middle East shipping routes, or energy supply-chain risk.

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 how a private SaaS company's ARR valuation multiple changed across funding rounds, and attribute the compression or expansion to rate cycles and macro selloffs, growth deceleration, narrative shifts (including an AI premium), competition, and investor demand, benchmarked against private-market medians and peers. Use this skill whenever the user asks about valuation compression, ARR multiples, round-to-round valuation or multiple changes, down rounds, or wants to compare a VC-backed software company's funding rounds. Research the rounds rather than answering from memory.

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。

[omh] Missed route or run lessons to record: classify and review self-improvement store routes as an auxiliary review lane before durable writes, then record workflow attempts as metadata-only traces, evals, review queues, patch proposals, regression cases, audits, indexes, and exports. Use when the user says: workflow-learning, workflow learning, route-signal, self-improvement store routing, store route review, memory skill wiki routing, learning trace, learning audit.

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。

[omh] Coding owner already chosen, handoff pending: prepares the handoff for the coding agent you already chose, composing its prompt from that agent's own installed skills; never selects the owner and never executes the work itself. Use when the user says: ulw-maestro, coding handoff, prepare the handoff, prepare a coding handoff, hand off the coding work, external executor handoff, handoff prompt, delegation prompt.

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。

[omh] Buzz community agent setup or relay trouble: connect and operate Hermes as a native Buzz community agent, deliver local media with verified relay receipts, or diagnose a self-hosted Buzz relay without inventing transport evidence. Aliases: omh-buzz. Use when the user says: connect Hermes to Buzz, Buzz community agent, Buzz gateway setup, Buzz media attachment, Buzz relay self-hosting, Buzz connection diagnostics, 버즈 커뮤니티 연결, Buzz 메시지 첨부.

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。

[omh] Explaining OMH itself: current-source-first documentation for OMH itself: product identity, public capability catalog, model routing, local state, and long-term memory. Use when the user says: product-docs, OMH documentation, oh-my-hermes documentation, what is OMH, what is oh-my-hermes, how does OMH work, OMH capability catalog, OMH skill catalog.

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。

Price changes within the store's guardrails, rate recommendations from demand, occupancy, or sell-through pace, promotions with their scope, depth, and dates, the markdown option for stock or capacity that is not selling, and the margin preview before any of it is staged. Not needed for explaining why revenue moved (performance-insights) or writing promotional copy (marketing-campaigns).

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。

Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).

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 Key Vault Certificates library for Rust. Create, manage, and use X.509 certificates including self-signed and CA-issued. Triggers: "keyvault certificates rust", "CertificateClient rust", "create certificate rust", "self-signed certificate rust", "X.509 rust".

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。

Filter and select sequences by criteria (length, ID, GC content, patterns) using Biopython. Use when subsetting sequences, removing unwanted records, or selecting by specific criteria.

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。

Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).

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。

Screen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, exhaustion setup, selling exhaustion, hammer reversal, undercut reclaim, near-close reversal candidates, or pullback entries in high-quality funds-owned stocks.

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。

Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when users ask for 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.

Use when the user asks to "turn this idea into posts for every platform", "write the X thread / LinkedIn post / 小红书 note", or "spec the carousel slides"; turns one idea into N platform-native ready-to-paste packages — post/thread, caption, Threads text, 小红书 note, link post with first-comment placement — each adapted per the dated platform norm card (never verbatim cross-posting), every product claim held to approved claims-ledger wording or flagged [needs source], every variant tagged {formula | hook family | CTA type | signal optimized}, plus a carousel slide-spec mode (intro/content/outro slide roles, per-element char budgets, 小红书 3:4 / IG 4:5 / LinkedIn document PDF artboards, text-safe-zone + alt-text checklist). Not for creator deliverable briefs — use brief-generator; not for repurposing existing assets with paid boost — use content-amplifier. 社媒文案/一稿多发改写/小红书笔记/轮播图脚本

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。

SkillOpt-flavored offline training loop for any SKILL.md. Treats accumulated learn-rule corrections as training trajectories, proposes bounded patches via an optimizer LLM, gates each candidate against a held-out validation set built from the user's own past corrections, and ships only candidates that demonstrably improve the score. Inspired by Microsoft SkillOpt's ReflACT pipeline (rollout → reflect → aggregate → select → update → evaluate) adapted to pro-workflow's SQLite store. Use when a skill has accumulated 8+ learn-rule rows and the user wants the skill itself to get better, not just longer.

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-curated copy-paste prompts for AI coding agents (MVP scaffolding, RAG chatbot with Claude on Bedrock, security baseline evaluation, cost anomaly detection, GPU quota requests, EKS deployment, Well-Architected review, etc.) plus downloadable installable agents (Multi-Account Transition Advisor, Bill Shock Preventer, Service Quota Agent, Bedrock Model Availability Agent, AWS DB Advisor). Use when the user asks for a prompt to do X on AWS, wants an installable agent for multi-account / cost monitoring / quota management / Bedrock model availability / database selection, or asks how to use AWS prompts. For migration intent (GCP to AWS, Azure to AWS, OpenAI/Gemini to Bedrock), route to the migration skills in this plugin (`gcp-to-aws`, `azure-to-aws`, `heroku-to-aws`, `llm-to-bedrock`). Do not use for: factual AWS Activate / programs / credits questions, learn articles, sample architectures, or for prompts that are not in the bundled `references/prompt-library/` tree.

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。

Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.

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。

Selects, deploys, and customizes AI models on Amazon SageMaker. Training or Processing jobs, fine-tuning (SFT/DPO/RLVR/RLAIF), model selection, dataset preparation, evaluation, SageMaker or Bedrock deployment, inference optimization and endpoint diagnostics. Covers the full lifecycle from planning through production. Use when fine-tuning models on SageMaker, choosing which base model to customize, fine-tune, or deploy from SageMaker JumpStart or Hub, SageMakerPublicHub, or the SageMaker public model catalog, transforming or validating training data, evaluating model quality, deploying or optimizing endpoints, configuring IAM/S3 for training, or managing SageMaker Managed MLflow. Use for endpoint health, failures, latency, logs, metrics, errors. Covers Serverless Model Customization, Nova and OSS deployment paths, and PySDK v3. NOT for Ground Truth labeling, Feature Store, or general-purpose AWS infrastructure.

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。

Selects, investigates, and compares AWS object, file, and block storage services, and answers cost, performance, configuration, security, and troubleshooting questions about storage services. Applies when a user asks where to store or archive data based on their usage patterns; which storage service to choose or how two compare; how to migrate data from on-premises or between AWS services; how to protect, replicate, or recover data; how to optimize storage costs; where to deploy shared NFS, SMB, or POSIX file systems; where to store vector embeddings or tabular data; what storage backs enterprise file shares, self-managed databases on EC2, VMware, or stateful containers; or asks what an AWS storage service can do or how it works. Relevant for storage needs for workloads such as AI/ML, analytics, EDA, HPC, media, genomics, or financial trading. Not applicable for SQL query engines (Athena, Spark, Redshift, EMR), ETL (Glue), streaming (Kafka, MSK, Kinesis), or managed database services (RDS, Aurora, DynamoDB).

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 generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore (including the Harness managed agent loop). Applies when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, migrating/porting/converting a Bedrock Agent (including inline agents) to an AgentCore Harness, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). Also for prompt caching, quota and throttling diagnosis, cost tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, paid endpoints, agent payments). NOT for custom model training, Rekognition, or Comprehend.

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。

Researches the app being cloned and reads what its real users say in public reviews (App Store, Google Play, G2, Capterra, Trustpilot, Reddit, Hacker News, its own feature-request board), then ranks what they hate, what is missing and what is unsolved, turns it into a fix plan for the clone and a positioning angle you can sell. Every quote is real, verbatim and linked. Use when the user says "what do people hate about X", "read the reviews", "how do I make mine better", "find the gap", "what features are missing", "how do I position this", "make it sellable", or after /replica-diff.

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。