4,410 件 ・ 関連度順
概要と使いどころ
Analyze a single FASTA file (nucleotide or protein), compute sequence-level metrics (GC, ORFs, MW, pI, GRAVY, secondary-structure fractions) with Biopython, and write a Markdown report plus structured JSON for downstream chaining.
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。
Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.
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。
Workflow track for tasks that need no TDD — documentation edits, governance count bumps, vendored-skill content updates, configuration tweaks, formatting, typo fixes, dependency bumps where no project code changes. Skips `/scenario` and `/implement` (no failing test to drive) and runs the work directly. `archive`, `memory-sync`, `/grant-commit`, and `/commit` remain mandatory. `verify`, `simplify`, `integrate`, and `document` are conditional — required when the diff hits one of the listed triggers, optional otherwise. `verify` is skipped only when the diff is pure-docs/prose AND `project.json → test.kind` is `behavior` (absent/invalid `test.kind` → `structural` → verify runs). Chore is a stripped-down pipeline, not a bypass; never silently skip a conditional phase whose triggers apply.
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。
Bring documentation prose down to a high-school reading level without losing precision. Runs after the content is written and before humanizer. Targets grade 9 on four measures — sentence length, vocabulary, clause load and jargon density — and carries the rewriting moves that actually shift them.
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。
Find web sources with query-relevant page excerpts and optional full-page content, and discover workflows, data APIs, and indexes. Use for web research or finding structured records, listings, transcripts, and datasets. Supports semantic tool discovery, domain matching, and progressive catalogue browsing.
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。
Read a known webpage or execute a discovered workflow or data-provider capability. Use for page content or structured results once the URL or tool is selected.
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。
Bulk-extract many pages from one site or section. Use for "crawl", "everything under /docs", or content spanning linked pages.
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。
Convert a local file (PDF, DOCX, XLSX, HTML, …) to markdown, or answer questions about its content. Use whenever the input is a file path, not a URL.
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 Text Translation SDK for real-time text translation, transliteration, language detection, and dictionary lookup. Use for translating text content in applications.
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。
Audit and rewrite content to remove 21 categories of AI writing patterns with a 43-entry replacement table
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。
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
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 competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com, Facebook, Instagram, 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。
Discover and track emerging trends across Google Trends, Instagram, Facebook, YouTube, and TikTok to inform content strategy.
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。
Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.
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。
Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.
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 any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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 planning or upgrading an internal skill/workflow for business operations, especially when turning a messy idea into a structured skill plan, migration plan, acceptance checklist, or packaging decision. Particularly useful for Obsidian knowledge base consolidation, workspace migration, content library cleanup, and integrating multiple workbenches into one operating system.
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。
Implement a repository code or configuration change through reproduction, focused checks, review, and evidence. Use for end-to-end features and fixes; not for explanation-only, review-only, research, content publishing, or production operations.
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 data from websites, including JavaScript-rendered SPAs and dynamic content
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。
YouTube transcripts to summaries, threads, blogs.
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。
Mint a new YYYY-MM-DD-exact-coding-baseline snapshot under research/workflow-dev/export/. Detects the current best correctness- oriented workflow from research/workflow-dev/workflow-construction.md (or takes an explicit source-workflow argument) and transforms it from a lab workflow into a consumer-ready one on three axes: strips lab-only measurement content, re-enables human-in-the-loop checkpoints, and converts auto-loading config into an explicitly-invoked skill. Supports both promoted EXACT Coding lines: Opus/Hybrid and SOL/Predictive TDD. Exports Claude Code, pi, OpenCode, cursor-agent and, for SOL, Copilot. Trigger when the user says "exact-coding baseline export", "neue exact-coding baseline", "exact-coding-baseline-export", or asks to refresh the baseline snapshot.
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 chill.institute (web UI) to search for content and click.
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 competitor strategies, content, pricing, ads, and market positioning across Google Maps, Booking.com.
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 for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.
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。