1,018 件 ・ 関連度順
概要と使いどころ
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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Used when the user asks to "create slides", "make a presentation", "build a deck", "design slides", "generate a pitch deck", "create a conference talk", "make a keynote", "presentation design", "slide layout", "slide template", "presentation structure", or mentions slides, presentations, decks, or talks in any context. Also triggers when the user wants to plan the content and visual direction of a presentation before generating it. This is the entry-point orchestrator that drives the full slide creation workflow across both HTML and PPTX formats. Examples: "I need to create slides for my conference talk about AI adoption", "can you turn this markdown into a presentation?", "I have a product demo tomorrow and need visual materials", "convert my existing PowerPoint to interactive HTML slides".
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。
Scaffold and run Karpathy-style autoresearch loops in any git repo. This skill should be used when setting up autonomous code improvement, generating adversarial eval harnesses, running hypothesis-implement-eval-keep/discard loops, or checking autoresearch progress. Triggers on "autoresearch", "autonomous improvement", "eval loop", "hypothesis loop", "self-improvement loop".
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。
OpenAI GPT Image generation and editing (gpt-image-2, 1.5, 1, mini). Text-to-image, mask-based inpainting, multi-reference composition, multi-turn conversational editing via Responses API, streaming with partial images. This skill should be used when generating or editing images via OpenAI's image models, when near-perfect text rendering in images is needed, when mask-based region-aware editing is required, when multi-turn conversational image editing is desired, or when streaming progressive image delivery is needed. Complements nano-banana-pro (Gemini) as a parallel image generation backend.
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。
Report ground-truth Claude token consumption and estimated cost by parsing JSONL session files directly. Use when checking API spend, auditing token usage by project/session/model, generating daily/weekly/monthly cost reports, or diagnosing ccusage undercounting. Includes subagent files that ccusage ignores.
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。
This skill should be used when creating, auditing, or maintaining AGENTS.md files and Codex CLI configuration for any project—including initializing AGENTS.md for cross-agent compatibility (Codex, Cursor, Copilot, Devin, Jules, Amp, Gemini CLI), generating config.toml or .rules files, scaffolding .agents/skills/, converting CLAUDE.md to AGENTS.md, or auditing existing agent configs for bloat and staleness. Complementary to codex-orchestrator (which executes subagents; this skill creates the config files they consume).
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。
Package codebases into LLM-optimized markdown, and run the Deep Think Authority pipeline (context-builder → Deep Think review → structured AUTHORITY → Flash/Antigravity implementation agent). Use when generating project context, preparing Deep Think reviews, or binding a weaker coding agent to a senior reasoning pass.
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 deploy, operate, secure, or scale running infrastructure — cloud architecture (multi-region, VPC, FinOps), CI/CD and deployment strategies, secrets/IAM/supply-chain security, observability and SLO alerting, SRE and incident response, Kubernetes vs ECS and autoscaling, Terraform/Pulumi/CDK, or platform engineering and DORA metrics. Also for generating runbooks, pipeline configs, Terraform modules, or Kubernetes manifests. For choosing the system's architecture or stack before it runs (monolith vs microservices, which database), use software-architect.
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。
Configure render settings, compositing nodes, output formats, and execute renders via Cycles or EEVEE engines using Python API or command-line interface. Use when automating render execution for batch processing, configuring quality and performance trade-offs, setting up compositing pipelines for post-processing, generating multiple output formats from a single render, or producing final output for publication or presentation.
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。
Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
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。
Create, merge, and deduplicate BibTeX bibliography files using R packages (RefManageR, bibtex). Parse .bib files into structured R objects, merge multiple bibliographies with deduplication by DOI or title similarity, generate entries from DOI/ISBN/arXiv ID, and export clean sorted .bib files. Use when creating a new .bib file for an R Markdown or Quarto project, merging bibliographies from multiple collaborators, deduplicating a .bib that has grown through copy-paste accumulation, or generating BibTeX entries programmatically from DOIs or other identifiers.
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。
Design monochrome ornamental patterns grounded in Alexander Speltz's classical ornament taxonomy. Covers historical period selection, motif structural analysis, prompt construction for line art and silhouette rendering, and AI-assisted image generation via Z-Image. Use when creating decorative borders, medallions, or friezes in a single color, exploring historical ornament styles through generative AI, producing line art or pen-and-ink renderings of classical motifs, or generating reference imagery for design or educational materials.
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。
Format citations across academic styles (APA 7, Chicago, Vancouver, IEEE) using CSL processors and R tooling. Convert between citation styles, generate in-text citations and reference lists, and validate formatting against style guides using citeproc, knitcitations, and Quarto's built-in citation engine. Use when rendering a Quarto or R Markdown document with formatted citations, converting a bibliography between citation styles, generating a standalone reference list, or setting up citation infrastructure for a multi-document project.
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。
Format a Quarto or R Markdown report following APA 7th edition style. Covers apaquarto/papaja packages, title page, abstracts, citations, tables, figures, and reference formatting. Use when writing an academic paper in APA format, creating a psychology or social science research report, generating reproducible manuscripts with embedded analysis, or preparing a thesis or dissertation chapter.
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。
Generate jigsaw puzzles via generate_puzzle() or geom_puzzle_*() with parameter validation against inst/config.yml. Supports rectangular, hexagonal, concentric, voronoi, and snic puzzle types with configurable grid, size, seed, offset, and layout parameters. Use when creating puzzle SVG files for a specific type and configuration, testing generation with different parameters, generating sample output for documentation or demos, or creating ggplot2 puzzle visualizations.
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。
Generate publication-ready statistical tables using gt, kableExtra, or flextable. Covers descriptive statistics, regression results, ANOVA tables, correlation matrices, and APA formatting. Use when creating descriptive statistics tables, formatting regression or ANOVA output, building correlation matrices, producing APA-style tables for academic papers, or generating tables for Quarto and R Markdown documents.
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。
Generate themed Mermaid flowchart diagrams from putior workflow data. Covers theme selection (9 themes including 4 colorblind-safe), output modes (console, file, clipboard, raw), interactive features (clickable nodes, source info), and embedding in README, Quarto, and R Markdown. Use after annotating source files and ready to produce a visual diagram, when regenerating a diagram after workflow changes, or when switching themes or output formats for different audiences.
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。
Solve Diophantine equations (integer-only solutions) including linear, quadratic, and Pell equations. Covers the extended Euclidean algorithm, descent methods, and existence proofs. Use when finding all integer solutions to ax + by = c, solving Pell's equation, generating Pythagorean triples, proving no integer solutions exist via modular constraints, or finding the fundamental solution from which all others are generated.
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。
Create a Quarto document for reproducible reports, presentations, or websites. Covers YAML configuration, code chunk options, output formats, cross-references, and rendering. Use when creating a reproducible analysis report, building a presentation with embedded code, generating HTML, PDF, or Word documents from code, or migrating an existing R Markdown document to Quarto.
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。
Conduct a project or sprint retrospective by gathering data from status reports and velocity metrics, structuring what went well and what needs improvement, and generating actionable improvement items with owners and due dates. Use at the end of a sprint, after a project phase or milestone, following a significant incident or success, at a quarterly review of ongoing processes, or before starting a similar project to capture lessons learned.
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。
Set up a Blender scene programmatically via Python (bpy) with objects, materials, lighting, camera, and environment configuration. Use when creating reproducible 3D visualization scenes, automating product or architectural rendering setup, generating multiple scene variations programmatically, building template scenes for batch rendering workflows, or integrating 3D visualization into data 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。
Create parameterized Quarto or R Markdown reports that can be rendered with different inputs to generate multiple variations. Covers parameter definitions, programmatic rendering, and batch generation. Use when generating the same report for different departments, regions, or time periods; creating client-specific reports from a single template; building dashboards that filter to specific subsets; or automating recurring reports with varying inputs.
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 an arbitrary codebase to identify functions, APIs, and data sources suitable for exposure as MCP tools, producing a tool specification document. Use when planning an MCP server for an existing project, auditing a codebase before wrapping it as an AI-accessible tool surface, comparing what a codebase can do versus what is already exposed via MCP, or generating a tool spec to hand off to scaffold-mcp-server.
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。
Operator-side guide for generating a target-specific training curve chart for a legacy experiment PR. Use when auditing or presenting historical runs.
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。