データ・AIのスキル

Reconcile baseline-versioned files that the `create-baseline upgrade` CLI staged for LLM-assisted semantic merge. Use when the CLI prints "Open Claude Code and run /upgrade-project to reconcile". Reads the stage manifest at `.claude/state/upgrade/<ts>/manifest.json`, reasons through each pending file's three-way delta in main context, writes a reconciled LOCAL, then deletes the stage when every file lands. Supports `--dry-run` (preview the reconciled diff without writing) and a structured "needs-user-input" fallback when the conflict cannot be disambiguated automatically.

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。

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep watching open PRs so fresh review feedback is surfaced promptly. Use when the user asks Codex to monitor a PR, watch CI, handle review comments, or keep an eye on failures and feedback on an open PR.

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 asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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。

An infographic skill for AI agents — works inside Claude Code, Codex, Hermes, OpenClaw, Cursor, and any agent host that loads skills. Create professional infographics as self-contained HTML files with PNG exports. Use when the user asks to create, design, generate, build, or make an infographic, data visualization, visual explainer, chart poster, cheatsheet, or pocket guide. Trigger phrases include "make this visual", "turn this into a graphic", "visualize this data", "infographic for X", "cheatsheet for X", or when the user provides structured data (tokenomics allocations, ecosystem/partner lists, timelines, comparisons, step-by-step processes) AND asks for a visual output. Covers tokenomics, ecosystem overviews, game overviews, cheatsheets, airdrop guides, comparisons, and process flows. Do NOT trigger for plain text answers, markdown tables, bulleted summaries, questions asking for advice or explanations about a topic (even when the topic is tokenomics, ecosystem, vesting, etc.), inline chat diagrams, or data processing with no visual output requested. The signal is "asks for a visual", not "mentions a keyword".

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。

Outlines: structured JSON/regex/Pydantic LLM generation.

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。

Build a throwaway prototype to flush out a design before committing to it. Routes between two branches — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route. Use when the user wants to prototype, sanity-check a data model or state machine, mock up a UI, explore design options, or says "prototype this", "let me play with it", "try a few designs".

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。

W&B: log ML experiments, sweeps, model registry, dashboards.

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。

vLLM: high-throughput LLM serving, OpenAI API, quantization.

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。

OBLITERATUS: abliterate LLM refusals (diff-in-means).

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。

Axolotl: YAML LLM fine-tuning (LoRA, DPO, GRPO).

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。

TRL: SFT, DPO, PPO, GRPO, reward modeling for LLM RLHF.

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。

Karpathy's LLM Wiki: build/query interlinked markdown KB.

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。

Decomposition playbook + specialist-roster conventions + anti-temptation rules for an orchestrator profile routing work through Kanban. The "don't do the work yourself" rule and the basic lifecycle are auto-injected into every kanban worker's system prompt; this skill is the deeper playbook when you're specifically playing the orchestrator role.

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。

Systematic VPS cleanup — analyze files, categorize by importance, safely delete temporary/old data to free disk space

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。

Pitfalls, examples, and edge cases for Hermes Kanban workers. The lifecycle itself is auto-injected into every worker's system prompt as KANBAN_GUIDANCE (from agent/prompt_builder.py); this skill is what you load when you want deeper detail on specific scenarios.

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。

HuggingFace hf CLI: search/download/upload models, datasets.

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。

Songwriting craft and Suno AI music prompts.

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。

Iterative Python via live Jupyter kernel (hamelnb).

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。

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data 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。

Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.

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。

AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.

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 Asin Data API 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。

Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.

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。

Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.

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。