9,245 件 ・ 関連度順
Use when onboarding to an unfamiliar codebase, before a major refactor, on a brownfield project before /gsd-new-project, or to refresh .planning/codebase/. Spawns up to 4 parallel mapper agents that write 7 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。
Use when a phase has unresolved decisions before planning and no CONTEXT.md exists. Asks adaptive questions on the phase's gray areas and writes {phase_num}-CONTEXT.md with locked decisions; --auto, --chain, --power change the questioning.
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 wants to park an idea or feature request that is not ready for active planning, or says add it to the backlog. Writes a 999.x entry to ROADMAP.md and creates its phase directory for later promotion.
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 onboarding someone to a finished milestone or preparing a team review. Reads ROADMAP, REQUIREMENTS, CONTEXT, SUMMARY, and VERIFICATION artifacts into one MILESTONE_SUMMARY in .planning/reports/, then offers Q&A.
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 every phase in a milestone is complete and the user wants to archive it, tag the release, and start the next version. Archives roadmap and requirements to .planning/milestones/, updates PROJECT.md, then tags the version.
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 urgent work surfaces mid-milestone and must land between two planned phases without renumbering the roadmap. Inserts it as a decimal phase such as 72.1 after the phase the user 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。
Use when a bug, failing test, or unexpected behaviour needs investigation, especially across sessions or context resets. Isolates it in a debugger subagent with persistent .planning/debug state, unlike skills/systematic-debugging.
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 a completed phase needs unit or E2E tests, or the user asks to add tests for phase N. Classifies implementation files into TDD, E2E, or skip, gets plan approval, then generates RED-GREEN tests from SUMMARY.md and UAT 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。
Use when the user says run the rest autonomously, overnight, or hands-off through the remaining phases. Runs discuss, plan, execute per phase, pausing only for user decisions, then milestone audit, complete, and cleanup.
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。
Capture a forward-looking idea with trigger conditions — surfaces automatically at the right milestone
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 what UAT or verification items are still outstanding across phases, or wants a prioritized manual test plan. Scans every phase for pending, skipped, blocked and human_needed items and flags stale docs.
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 wants to see what the agent assumes about a phase's approach, order, scope, risks and dependencies before planning starts, instead of running a full discuss session. Read-only; ends with a prompt for feedback.
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 you have a spec or requirements for a multi-step task, before touching 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。
Use when executing implementation plans with independent tasks in the current session
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 starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
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 packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged in
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 a user's TrailSnap photo library through scoped MCP tools to find photos, reconstruct memories, build person timelines, create evidence-backed narratives, or propose an album plan for explicit in-app approval. Use for the user's own TrailSnap library; never claim a proposal was executed or changed the library.
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。
Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).
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。
Expert knowledge for Microsoft Foundry (aka Azure AI Foundry) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Foundry agents with MCP tools, Azure OpenAI/Claude, Agent 365, voice/telephony, or Terraform, and other Microsoft Foundry related development tasks. Not for Content Safety in Foundry Control Plane (use azure-content-safety), Azure Content Understanding in Foundry Tools (use azure-content-understanding), Azure Speech in Foundry Tools (use azure-speech), Microsoft Foundry Classic (use microsoft-foundry-classic).
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。
Expert knowledge for Azure Static Web Apps development including troubleshooting, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when wiring SWA APIs to Azure DBs, configuring custom domains/backends, Entra auth, CI/CD, or plan limits, and other Azure Static Web Apps related development tasks. Not for Azure App Service (use azure-app-service), Azure Functions (use azure-functions), Azure Container Apps (use azure-container-apps), Azure Web Application Firewall (use azure-web-application-firewall).
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。
Expert knowledge for Azure Table Storage development including best practices, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when securing Table Storage, tuning telemetry, planning scale limits, designing keys, or scripting with PowerShell, and other Azure Table Storage related development tasks. Not for Azure Cosmos DB (use azure-cosmos-db), Azure Blob Storage (use azure-blob-storage), Azure Queue Storage (use azure-queue-storage).
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。
Expert knowledge for Azure SQL Managed Instance development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning MI tiers/networking, configuring HA/geo-replication, Entra/TDE security, or app connectivity patterns, and other Azure SQL Managed Instance related development tasks. Not for Azure SQL Database (use azure-sql-database), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines), Azure Cosmos DB (use azure-cosmos-db), Azure Data Factory (use azure-data-factory).
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。
Expert knowledge for SQL Server on Azure Virtual Machines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when planning SQL VM vs Managed Instance, Always On/FCI HADR, Azure storage/backups, Key Vault/Entra auth, or KQL telemetry, and other SQL Server on Azure Virtual Machines related development tasks. Not for Azure SQL Database (use azure-sql-database), Azure SQL Managed Instance (use azure-sql-managed-instance), Azure Virtual Machines (use azure-virtual-machines), Azure Data Science Virtual Machines (use azure-data-science-vm).
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。
Expert knowledge for Azure Resiliency development including best practices, security, configuration, and deployment. Use when configuring backup/replication policies, vault lifecycle, zone-down drills, RBAC roles, or recovery plans, and other Azure Resiliency related development tasks. Not for Azure Reliability (use azure-reliability), Azure Service Health (use azure-service-health), Azure Monitor (use azure-monitor).
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。