214 件 ・ 関連度順
概要と使いどころ
Produce a human-facing visual report — a chart, dashboard, scorecard, or styled summary — that renders as a live page in the screenpipe viewer instead of plain text. Use when the task asks for a visual/graphical output rather than a plain note; do NOT use for plain text or raw data (prefer a markdown note for those).
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。
Legal document drafting -- contracts, memos, briefs, complaints, demand letters, opinions, discovery, settlements, ToS, privacy policies. Full pipeline: document structure, per-section writing, Bluebook citation, case law lookup (CourtListener API), regulation lookup (eCFR API), DOCX output, and TDD-style verification (defined terms, cross-references, placeholders, boilerplate, citation format). Triggers on: 'draft a contract', 'write a legal memo', 'create an NDA', 'write a brief', 'legal document about', 'draft a complaint', 'terms of service', 'privacy policy', 'demand letter', 'settlement agreement', 'legal opinion', 'discovery requests', any request to produce a legal or law-related document.
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。
Professor's review queue — student output waits here for professor approval before going to clients or courts. Only active if "formal review queue" supervision style was chosen at setup; otherwise dormant. Use when the professor wants to see what's waiting for review, approve, edit-then-approve, or return an item.
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。
Guided customization of your legal clinic profile — change one thing without re-running the whole cold-start interview. Adjust clinic profile, jurisdiction, supervision style, practice-area templates, semester configuration, or output safeguards. Use when the user says "change my [thing]", "new semester", "add a practice area", "update my config", or "customize".
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。
Prompt- and template-complete Google Docs creation and editing with explicit-instruction-authoritative structural preservation, including semantic roles, relationships, comparison dimensions, and instructed extensions; full-topology native-copy routing; source-grounded per-tab adaptation for past/example references; style-preserving hyperlink and table edits; canonical smart-chip-first authoring for dates and relevant supported people or Google resources; a file-backed advisory trusted read before existing-document writes; automatic protected-control awareness; direct connector APIs by default; DOCX-first import only when no supplied Google Doc template/reference constrains the output; and checked-in code mode only for exact native dropdown mutation. Use when Codex must create, edit, fill, adapt, redesign, or verify Google Docs without overriding explicit user/template instructions, adding unrequested document scope, or carrying stale reference facts into a new deliverable.
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。
Apply consistent photo adjustments across a set of images so they look like they were edited together. Use this skill whenever the user says "make my photos look cohesive", "give all these the same style", "apply a warm and golden feel to all of these", "make this cinematic", "match the look across my photos", "edit all my travel photos the same way", "batch edit these", "make these consistent", "fix my phone photos", or uploads a folder of photos and wants a unified, polished result. Also triggers for requests like "apply a preset to all of these", "make these look professional", or "they were shot in mixed lighting — can you fix them all". Outputs direct final image URLs plus an in-chat preview grid and optional Firefly Board link. Access: 🔐 Signed-In required | Gen AI: ❌
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 only for MSBuild failure investigation, build-performance analysis, or review of existing build XML/extension contracts. A plan to convert a working legacy/non-SDK project to SDK style or migrate packages.config is a project-system migration, NOT a build-file review: do not activate, even to check scope. Framework/API upgrades are also excluded. Activate for advisory Import/Exists hook and NuGet packed-import questions even without a checkout or failed build; .csproj/.fsproj/.vbproj/.props/.targets conditions; custom-target inputs/outputs; .binlog capture/replay; failing dotnet build/restore/pack/publish or MSBuild-based test builds; slow evaluation, compilation, copying, or no-change builds; and controlled timing comparisons. Exclude SDK installation alone, C# refactoring, runtime profiling, assertions after successful builds, and non-MSBuild builds.
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 Go struct tags to render styled terminal output in gh-aw.
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。
Kode capability-management playbook: treat features (LSP, statusline, output styles, plugins, notifications) as configurable capabilities and drive changes through the agent CLI (SlashCommand/Task) instead of pushing users into menu-like flows. Use when the user asks to enable/disable/configure a capability, diagnose why it’s off, or wants the agent to self-manage Kode’s own features (meta capability management).
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。
Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.
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。
Cleans a Quarto (.qmd) file by replacing LaTeX holdovers with proper Quarto/Pandoc syntax so the document renders correctly to HTML, PDF (Typst/LaTeX), and Word. Fixes citations (\citep → [@key]), cross-references (\ref → @label), figures (\includegraphics → markdown), tables, text formatting (\textbf → **bold**), inline math (\( \) → $ $), hyperlinks (\href → [text](url)), footnotes (\footnote → ^[]), list environments, section headings, and R Markdown chunk options (dot-style → #| YAML). Use when asked to clean, fix, modernize, or port a Quarto or R Markdown document; when LaTeX commands appear in a .qmd file; when a document fails to render to Word or Typst; or when citations/cross-references do not appear in non-LaTeX output.
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 economic analysis outputs (fiscal briefings, macro briefings, market research, longlists, and other quantitative economic documents) against methodology standards, academic literature, and common errors. Runs structured checks across core categories including counterfactual, additionality, discounting, double counting, distributional analysis, Aqua Book RIGOUR, and Flyvbjerg-style strategic misrepresentation detection. Returns a RAG scorecard with issues ranked by severity.
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。
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".
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。
Academic paper writing skill with 12-agent pipeline. v2.5: Style Calibration (learn author's writing voice from past papers) + Writing Quality Check (writing quality checklist for natural prose). Supports IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures. APA 7.0 (default), Chicago, MLA, IEEE, Vancouver citation formats. Bilingual abstracts (zh-TW + EN). Multi-format output (LaTeX, DOCX, PDF, Markdown). Triggers on: write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖.
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 academic research proposals for PhD applications. Use when user asks to "write a research proposal", "create PhD proposal", "generate research plan", "撰写研究计划", "写博士申请", "doctoral proposal", or mentions specific research topics for PhD application. Supports STEM, humanities, and social sciences with field-specific adaptations. Follows Nature Reviews-style academic writing conventions. Supports both English and Chinese output based on user preference.
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 and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer suggestions to help refine future writing.
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。
Implements the NOWAIT technique for efficient reasoning in R1-style LLMs. Use when optimizing inference of reasoning models (QwQ, DeepSeek-R1, Phi4-Reasoning, Qwen3, Kimi-VL, QvQ), reducing chain-of-thought token usage by 27-51% while preserving accuracy. Triggers on "optimize reasoning", "reduce thinking tokens", "efficient inference", "suppress reflection tokens", or when working with verbose CoT outputs.
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。
Comprehensive markdown and Mermaid diagram writing skill that establishes text-based diagrams as the DEFAULT documentation standard. Use this skill when creating ANY scientific document, report, analysis, or visualization — it ensures all outputs are in version-controlled, token-efficient markdown with embedded Mermaid diagrams as the source of truth, with clear pathways to downstream Python or AI-generated images. Includes full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates ready to use.
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 AI-powered podcast-style audio narratives using Azure OpenAI's GPT Realtime Mini model via WebSocket. Use when building text-to-speech features, audio narrative generation, podcast creation from content, or integrating with Azure OpenAI Realtime API for real audio output. Covers full-stack implementation from React frontend to Python FastAPI backend with WebSocket streaming.
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 Stockbee-style setup model book from momentum-burst screener candidates, then update 3-day and 5-day forward outcomes with MFE/MAE, stop-hit status, outcome tags, and cohort statistics. Use when the user wants to study Stockbee Momentum Burst examples, track failed candidates, build setup fluency, review A/B setup quality, or convert screener outputs into a learning loop rather than immediate trade signals.
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 Minervini-style breakout trade plans from VCP screener output with worst-case risk calculation, portfolio heat management, and Alpaca-compatible order templates (stop-limit bracket for pre-placement, limit bracket for post-confirmation). Use when user has VCP screener results and wants actionable trade plans with entry/stop/target levels and position sizing.
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。
Author and edit `.svg` files in a Grida editor session — live-canvas binding, SVG output style, and parse-error recovery. Use when creating or modifying an SVG document.
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。
Generates complete NHANES-style cross-sectional epidemiology + retrospective clinical validation research designs from a user-provided disease and biomarker direction. Always use this skill whenever a user wants to design, plan, or build a population-level biomarker association study using NHANES or similar survey datasets, especially when the article logic includes disease definition, biomarker formula derivation, multivariable logistic regression, restricted cubic spline analysis, subgroup stability testing, and a secondary hospital-based retrospective validation cohort. Covers five study patterns (cross-sectional association, dose-response / RCS, subgroup-stability, NHANES + retrospective validation, preliminary screening-performance) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
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。
Generates complete bidirectional multi-phenotype Mendelian randomization research designs from a user-provided exposure family and outcome family. Always use this skill whenever a user wants to design, plan, or build a genome-wide causal-inference study based on publicly available GWAS summary statistics, especially when the article logic includes multiple exposures, multiple outcomes or subtypes, bidirectional MR, IV filtering, IVW as the main estimator, weighted median / MR-Egger / MR-PRESSO sensitivity analyses, leave-one-out testing, heterogeneity / pleiotropy checks, and multiple-testing control with FDR. Covers five study patterns (single-family bidirectional MR, multi-phenotype screening MR, subtype-resolved MR, phenome-style bidirectional causal map, mechanism-prioritized MR follow-up) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable 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。