データ・AIのスキル

Build a pre-earnings briefing for a stock from Yahoo Finance data (yfinance): the upcoming report date and timing, consensus EPS and revenue estimates with their range, the beat/miss track record, analyst ratings and price targets, and what to watch in the print. Use this skill whenever the user is preparing for an upcoming earnings report or asks what the street expects — consensus or whisper numbers, EPS expectations, whether a company will beat, an earnings setup, or an earnings-season preview — and whenever a ticker comes up in the context of upcoming earnings, even without the word "preview". For results that are already out, use earnings-recap.

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。

Fetch financial and market data with the yfinance Python library (Yahoo Finance). Use this skill whenever the user wants stock data: current quotes and price history, financial statements (income statement, balance sheet, cash flow), options chains, dividends and splits, earnings and analyst estimates, price targets and ratings, institutional and insider holdings, news, multi-ticker comparisons, stock screens, or sector and industry data. Use it even when the user gives only a ticker symbol (AAPL, MSFT, TSLA) and the intent has to be inferred. For earnings previews or recaps, estimate revisions, valuation, correlation, liquidity, or ETF premium analysis, prefer the dedicated skill.

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 a company's most recent (or a specified past) earnings report from Yahoo Finance data (yfinance): actual vs estimated EPS, surprise size, revenue and margin trends, and the stock's price reaction. Use this skill whenever the user asks how earnings went — beat or miss, earnings surprise, quarterly results, the post-earnings move, or an earnings call recap — including casual references to a past report such as "AMZN reported last night" or "how did they do". For an upcoming report, use earnings-preview.

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。

Calculate an ETF's premium or discount to NAV from Yahoo Finance data (yfinance), compare or screen ETFs by premium, explain why a gap exists, and decompose a sudden ETF move into NAV-driven vs structural components (dealer gamma exposure, blocked AP arbitrage, sentiment). Use this skill whenever the user asks whether an ETF trades above or below NAV, compares ETF premiums or discounts, screens for the biggest ones, asks about ETF arbitrage or premium convergence, or wants to know why an ETF jumped or diverged from its holdings — including gamma squeezes, dealer gamma exposure (GEX), and blocked creation/redemption. Especially relevant for leveraged, inverse, international, bond, commodity, and crypto ETFs (IBIT, BITO, HYG, KWEB).

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 liquid a stock is using Yahoo Finance data (yfinance): bid-ask spreads, volume and dollar volume (ADTV), top-of-book and options depth, square-root market impact and slippage estimates, turnover ratio, and Amihud illiquidity, rolled into a liquidity grade. Use this skill whenever the user asks about liquidity or trading costs — how easily a position can be entered or exited, what a large order would do to the price, spread or execution-cost estimates, order book depth, volume patterns, or liquidity comparisons — especially for small caps, penny stocks, and thinly traded names.

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。

Control the live Pebrel terminal workspace from Codex or Claude Code. Use whenever the user asks to split panes, open a tab or file, run a command in another pane, start or prompt Codex/Claude, delegate a task to another agent, read output, wait for an agent, or says 分屏、开一个 Codex/Claude、打开 README、在上面/下面/左边/右边操作、把这个任务派给 Codex/Claude、让 codex 去改、问问另一个 agent、看看它跑完了没. Run `pebrel env` to orient, then use the supported Runtime API immediately instead of scanning processes or source code.

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] Spending money on the user's behalf -- a card checkout, an HTTP 402 or pay-per-request call, a wallet transfer, a booking, a paid data endpoint: show a pre-spend card (payee, total with fees, cap, refund terms, approver) first, and call it done only on an observed receipt. Use when the user says: spend-gate, spend gate, pre-spend, pre-spend card, spend cap, spending cap, spend limit, spending limit.

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] Before data leaves the team -- a dataset, log, ticket export or transcript excerpt bound for a vendor, a public issue, a model provider or a training corpus: classify every field, minimize first, pseudonymize with the mapping kept local, and call it redacted only after an observed residual scan. Use when the user says: data-share-review, data share review, pii redaction, redact pii, pseudonymize, de-identify, anonymize the dataset, residual pii.

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] Gathering candidate papers, datasets, or repos: source candidate inventory - prepare typed source candidates and acquisition status before downstream work; use ulw-research to fetch and cite them, or research-brief to turn them into a decision-ready brief. Use when the user says: source-finder, source finder, source acquisition, source intake, find papers and datasets, find datasets and repos, find papers, find arxiv link.

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] Turn repeated lessons into written rules: extract repeated principles from skills, prompts, traces, reviews, and failures into reviewed rule candidates without auto-mutating guidance. Use when the user says: rules-distill, rules distill, distill rules, rule distillation, principle distill, skill principles, extract agent rules, turn traces into rules.

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] Run efficiency and context usage report: report supplied local run efficiency while provider data stays unobserved. Use when the user says: run-efficiency, run efficiency report, local run efficiency, context utilization, tool duration report, 실행 효율 리포트, 컨텍스트 사용량, 도구 지연 시간.

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] Live rate, score, or local time a work task needs: policy overlay for live lookups - add provider, freshness, units, and source-quality gates after preferring native live-data tools for ordinary weather, finance, sports, maps, and time-zone requests. Use when the user says: live-info-operator, live info operator, live information, real time information, real-time information, weather today, current weather, weather forecast.

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] LLM-powered feature to build: LLM app development: prepare a build handoff for an LLM-powered feature with a pinned provider boundary, schema-first outputs, versioned prompt files, grounded retrieval, and an eval suite as a shipped deliverable. Use when the user says: llm-app-dev, llm app development, llm application development, build an llm app, build an llm feature, llm feature development, build a rag pipeline, rag pipeline.

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] Fine-tuning a model on your own data -- SFT, DPO, RLVR or a LoRA adapter: decide first whether prompting or retrieval already closes the gap, choose the method from the data you have, and promote a checkpoint only when it beats the untuned baseline on a held-out eval. Use when the user says: model-finetuning, model finetuning, model fine-tuning, fine-tune a model, fine-tune the model, fine tune a model, fine tune the model, fine-tune.

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] Importing prompt files for Hermes: prompt import readiness - review and normalize external CLI-agent prompt files before offering slash-command candidates; use external-connector-readiness for plugin or API adoption and toolbelt-readiness for missing runtime capabilities. Use when the user says: prompt-import-readiness, prompt import readiness, slash prompt import, slash prompts import, slash command prompt import, prompt library import, prompt folder import, prompt directory import.

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] Context window or token budget at risk: plan compact context, token/cost budgets, summarization checkpoints, and overflow recovery before long agent work. Use when the user says: context-budget-review, context budget review, context budget, token budget review, token budget, prompt budget, prompt caching, prompt cache.

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] Dataset or table to analyze: scope supplied data with provenance, causal-claim, and hallucination guards. Use when the user says: data-analysis, data analysis, dataset analysis, csv analysis, json analysis, log analysis, table analysis, analyze csv.

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] Data pipeline work -- an ETL or streaming job, a backfill or replay, duplicate events, a schema change downstream, a lineage question, a data-quality regression: make every rerun idempotent, bound every replay, and gate each load on observed checks. Use when the user says: data-pipelines, data pipeline, data pipelines, etl, elt, etl pipeline, etl job, etl backfill.

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 acpx as a headless ACP CLI for agent-to-agent communication, including installed-agent inspection, prompt/exec/sessions workflows, session scoping, queueing, permissions, output formats, system-prompt overrides, and multi-agent flows authored with defineFlow/decision/decisionEdge.

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。

Search this repository's awesome-ChatGPT-repositories list for open-source ChatGPT and LLM projects such as RAG frameworks, agents, chatbots, CLIs, prompts, and browser extensions. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, or AI development; not for editing or adding list entries.

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。

Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.

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。

Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.

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。