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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。

Get detailed data about a shader asset — properties, subshaders, passes, compilation messages, and supported status. Supports token-saving path-scoped reads via `paths` or `viewQuery`. Use 'assets-find' with `t:Shader` or 'assets-shader-list-all' to locate the shader first.

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 this skill when the user asks to convert a file from one format to another (e.g. csv to xlsx, xlsx to json, pdf text to csv) and you're not sure which library handles that specific pair, or whether it's supported in this environment at all. Dispatches to the right library per format pair and explicitly lists conversions that are NOT supported here so you don't attempt one that will silently fail or produce a wrong result.

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。

Add or extend coding-agent support in cc-sdd by executing the SOP in docs/cc-sdd/sop-new-agent.md end-to-end. Use when introducing a new agent, adding a subagent-capable variant, or evaluating migration of an existing supported agent to skills-based templates.

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 this skill when the user is invoking `doca_telemetry_utils` on a host with DOCA installed — discovering the diagnostic-counter schema, translating counter names to binary Data IDs, validating per-device counter support before committing a DOCA Telemetry exporter config, or reverse-resolving a captured Data ID. Trigger even when the user does not explicitly mention "doca_telemetry_utils" or "Data ID" — typical implicit phrasings include "my exporter ships but the collector sees nothing", "this metric silently drops downstream", "which counters does this BlueField expose", "translate this 0x... back to a counter name", "what do node / pcie_index / depth mean here", or "is this counter supported on this device before I commit it". Refuse and route elsewhere for developer-side collector / exporter library programming, DTS deployment, or DOCA install / repair — those belong to doca-telemetry, doca-public-knowledge-map, and doca-setup.

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 this skill when the user is doing DOCA version handling — detecting the installed release, validating the four-way match across pkg-config doca-common, applications/VERSION, doca_caps --version, and bfver/mlnx-release on BlueField, reasoning about NGC container tags, looking up whether a capability is on the installed release, or diagnosing build-vs-runtime drift. Trigger even when the user does not explicitly say "DOCA version" or "four-way match" — typical implicit phrasings include "program built but does nothing on the wire", "undefined reference to a symbol the docs claim exists", "DOCA_ERROR_NOT_SUPPORTED at runtime", "counter didn't increment", "what does `latest` mean for this tag", or "is my LTS still supported". Refuse and route elsewhere for installing or choosing DOCA packages (doca-setup), per-library API/capability questions (matching library skill), the cross-library DOCA_ERROR_* taxonomy (doca-programming-guide), or the general debug ladder (doca-debug) — those belong to other skills.

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 this skill when the user is doing hands-on DOCA AES-GCM work on a BlueField DPU or ConnectX NIC — configuring `doca_aes_gcm_task_encrypt` / `_task_decrypt`, querying `doca_aes_gcm_cap_*` for per-key-type (only `DOCA_AES_GCM_KEY_128` / `_256` — AES-192 not supported) and per-task support, sizing plaintext against the max-buf cap, setting source / destination mmap permissions, validating with a NIST GCMVS or RFC 5288 vector, or debugging DOCA_ERROR_* including the security-critical tag-verification-failed outcome on decrypt. Trigger even when the user does not explicitly mention "DOCA AES-GCM" or "AEAD" — typical implicit phrasings: "decrypt completion IO_FAILED", "auth tag isn't verifying", "NOT_PERMITTED on my encrypt buffer", "is AES-192-GCM on this BlueField" (no), or "encrypted record came back tampered". Refuse and route elsewhere for non-GCM AES modes (CBC / CTR / XTS — CPU OpenSSL), key management (KMS / HSM / rotation), SHA (doca-sha), or general AEAD background.

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 this skill when the user is doing hands-on DOCA Rivermax work on a BlueField DPU or ConnectX host — standing up `doca_rmax_in_stream` (receive) sessions for timing-precise media-over-IP (SMPTE ST 2110 video/audio, market data, scientific feeds), confirming the Rivermax SDK + license precondition before any DOCA-side code, running `doca_rmax_get_*_supported` capability queries, pairing with `doca-eth` queues and `doca-flow` steering, or debugging `DOCA_ERROR_*` from a Rivermax call. Trigger even when the user does not explicitly mention "DOCA Rivermax" or "rmax" — implicit phrasings include "ST 2110 receive isn't getting frames", "sub-microsecond jitter on BlueField", "NOT_SUPPORTED from doca_rmax_init", "no recv events after stream start", or "license check failing on a media receiver". Refuse and route elsewhere for installing the Rivermax SDK or its license, programming the underlying queue (`doca-eth`), steering rules (`doca-flow`), or best-effort packet I/O — those belong to other skills.

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 this skill for hands-on DOCA Compress programming on a BlueField DPU, ConnectX NIC, or host with DOCA — enabling compress-deflate, decompress-deflate, decompress-lz4-stream, or decompress-lz4-block tasks on a doca_compress context (the hardware supports DEFLATE both directions plus LZ4 decompress; LZ4 encode is NOT supported), sizing source / destination doca_buf against the per-task cap query, setting mmap permissions, deciding offload vs CPU zlib / zstd, validating with a round-trip smoke, or debugging DOCA_ERROR_* from a Compress call. Trigger on phrasings like "offload this gzip", "decompress incoming network data", "compress task returns INVALID_VALUE on alloc_init", "submitted a task but no completion arrives", or "decompress LZ4 on the BlueField." Refuse and route elsewhere for non-DEFLATE / non-LZ4 algorithms (zstd / Snappy / brotli), LZ4 encode (route to a CPU LZ4 library), pure mmap-to-mmap copies (doca-dma), or DOCA Core lifecycle internals.

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 MCP servers for Copilot Chat using the OpenAI Apps SDK or MCP Apps SDK widget rendering support (any language). Use this skill when: - Creating MCP servers that integrate with M365 Copilot declarative agents - Building rich interactive widgets (React + Fluent UI) that render in Copilot Chat - Implementing tools that return structuredContent for widget rendering - Adapting an existing MCP server to support Copilot widget rendering - Setting up devtunnels for localhost MCP server exposure - Configuring mcpPlugin.json manifests with RemoteMCPServer runtime Do NOT use this skill for general agent development (scaffolding, manifests, deployment) — use declarative-agent-developer instead. This skill is ONLY for MCP server + widget development. Triggers: "MCP server for Copilot", "OpenAI Apps SDK", "Copilot widget", "structuredContent", "MCP plugin", "devtunnels MCP", "OAI app", "widget rendering", "UI widget"

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。

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".

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 and open FinViz screener URLs from natural language requests. Use when user wants to screen stocks, find stocks matching criteria, filter by fundamentals or technicals, or asks to open FinViz with specific conditions. Supports both Japanese and English input (e.g., "高配当で成長している小型株を探したい", "Find oversold large caps with high ROE").

Produce a redacted AssemblyAI diagnostic bundle for support and incident triage. Use when evidence is needed without exposing keys, URLs, audio, transcripts, or prompts. Trigger with "AssemblyAI debug bundle" or "support evidence".

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。

Collect a deterministic, secret-safe Alchemy diagnostic bundle for triage and escalation. Use when a provider issue needs reproducible evidence. Trigger with "Alchemy debug bundle", "collect Alchemy diagnostics", or "prepare an Alchemy support case".

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。

Collect a bounded, secret-safe Algolia diagnostic bundle for an incident or support escalation. Use when a reproducible search or indexing failure needs portable evidence. Trigger with "Algolia debug bundle", "collect Algolia diagnostics", or "Algolia support evidence".

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。

Assemble a minimal, reproducible Adobe diagnostic bundle with default-deny collection and verifiable redaction. Use for engineering or Adobe support escalation. Use when the task requires adobe redacted diagnostic bundle. Trigger with "Adobe debug bundle", "collect Adobe diagnostics", or "prepare Adobe support case".

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。

Prepare and validate research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Use for aggregate or synthetic research documentation and traceability—not patient care or live clinical operation.

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。

Owns the customer's experience after the sale — support, success, escalation, and the feedback loop back into product. Use this for a decision spanning support and product, when service quality and cost are in tension, when deciding what to staff or automate, when a customer relationship is at risk above the account-manager level, or when nobody owns a recurring customer problem.

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 professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses (biomarker-stratified with outcomes) and treatment recommendation reports (evidence-based guidelines with decision algorithms). ...

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。

Verifies whether a scientific or biomedical claim is actually supported by the cited original papers rather than by citation drift, overstatement, selective citation, or correlation-to-causation inflation. Use this skill whenever a user wants to check whether a repeated statement, slide claim, manuscript sentence, review assertion, or “people often say” scientific conclusion is truly supported by the underlying primary literature. Always separate the claim itself, the cited paper(s), what the paper actually showed, what it did not show, and whether later retellings drifted beyond the original evidence. Never fabricate references, findings, study features, or citation chains.

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。

Designs cell-based and animal-based validation plans that translate computational, omics, biomarker, genetic, or clinical findings into experimentally testable validation routes. Always use this skill whenever a user wants to move from an in silico, statistical, or clinical association finding toward wet-lab validation using cell systems, organoid-like systems, xenograft or genetically relevant animal models. It should define the exact claim to test, separate mechanism-testing from association-support and translational-support goals, choose the best-fit model family, specify perturbation strategy, readouts, controls, sequencing of experiments, and four workload configurations (Lite / Standard / Advanced / Publication+) with one recommended primary plan. Never fabricate model availability, reagent availability, species relevance, assay feasibility, phenotype penetrance, expected effect sizes, validation success, or literature references.

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。

Reads a paper figure by figure before re-integrating the full narrative, so the user can identify the core findings quickly and check whether each visual actually supports the authors' main claims. Always separate figure content, figure-linked claim, evidentiary strength, and unsupported interpretation. Never fabricate references, PMIDs, DOIs, figure content, panel labels, result values, or study details that were not actually provided.

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。

Assess whether source code is ready to deploy to Azure — the check BEFORE infrastructure work. Evaluates build health, app completeness, dependencies and local services, stack compatibility, and deployment feasibility. Answers questions about what your app needs before it can be deployed — frameworks, dependencies, and configuration. Checks whether dependencies are compatible and identifies deployment blockers and unsupported frameworks. WHEN: "evaluate my repo", "is my app ready to deploy", "what does my app need to deploy", "what do I need before deploying", "does my app need", "can I ship this to Azure", "scan my repo for issues", "is this app deployable", "check if my app is ready for Azure", "do I need a Dockerfile", "what's blocking my deployment", "are there any blockers", "are my dependencies compatible", "does Azure support my framework", "what needs to change before deploying", "check my app configuration".

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。

Architect game input — action mapping (abstracting keys into named actions), rebinding with conflict detection and persistence, multi-device support (keyboard, gamepad, touch), analog deadzones, and feel features like input buffering and coyote time, plus accessibility. Engine-neutral. Use when the user mentions input mapping, rebind controls, gamepad support, deadzone, input buffering, coyote time, or accessible controls.

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。