「table」の検索結果

Tabular and numerical data analysis with descriptive statistics and insights. Use when the user provides data, tables, CSVs, or numbers and wants analysis.

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。

Search the documents uploaded for the CURRENT task/row (e.g. an Agentic Table row's attached files) via the `unique-cli uploaded-search "<query>"` command, with the same per-turn citation tracking as `unique-cli search` so cited facts render as `<sup>N</sup>` footnotes and clickable reference chips on the Unique platform. ALWAYS use this skill when the user refers to documents they uploaded/attached to this task, or when you need facts from the task's own attached files. These uploaded files are scoped to the chat, NOT to a knowledge-base folder, so they will NEVER appear in `unique-cli search` results no matter the folder or metadata filters. Use `unique-cli search` for the knowledge base and this command for the task's uploaded documents; the two are complementary and citation numbering is shared across them within a turn.

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 to create, update or verify the Lean 4 executable specification under lean/ from the exploration documents (event-timeline, hotspots, ubiquitous-language, ux-review, model-review) — initial generation, reflecting a session's results, fixing lake build, adding a command or a screen, checking a proposed feature against the model. Formalization questions go to model-review.md as MQ, never to the expert directly.

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 the infrastructure implementation phase after the backend follows the model — turning documents/infra-design decisions into infra/ (local container stack, production IaC), wiring cradle.json infra.up / infra.containers, adding IaC validate and image build steps to CI, and, once E2E passes, applying production once in a sandbox and recording it in the deployment table. Not for designing infrastructure (infra-design).

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 whenever a question is about what the specification says or does — "what happens if", "can X do Y", "which errors can this return", "what does the screen show for", "what fields does this command take", "is there an operation for". Answers by running the Lean executable spec (scenarios, commands,

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 to start a new product on Cradle or to add the Cradle skeleton to an existing repository — writes cradle.json, the project-facts instruction (.apm/instructions/project.instructions.md), documents/{ddd,ai-notes,infra-design,codebase} templates, CI, and a compiling Lean executable-spec scaffold with a minimal example domain, mockup and CLI.

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 construction plan for a multi-step engineering task that requires multiple PRs or a phased approach. Produces a self-contained plan file with dependency graph, review gates, and step-by-step execution instructions for coding agents. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex task, or describes work that clearly needs multiple PRs. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, user asks to modify/update an existing plan, user says "just do it", or user asks for code review/audit.

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。

Curate Busabase training examples, create immutable dataset snapshots, run reproducible LoRA or QLoRA fine-tunes on a local Apple Silicon Mac with MLX-LM, compare the untouched baseline and adapter on locked evaluations, and register approved adapters. Use when the user wants to train, fine-tune, evaluate, compare, promote, or manage a local language model with Busabase as the workflow source of truth. Do not use for RAG-only knowledge retrieval, cloud training, or training a foundation model from scratch.

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。

Busabase App-in-Skill dashboard over a fixed, deterministic mock user-behavior funnel dataset (browse → search → compare → booking attempt/abandon → complete) for a generic consumer booking product. Use when the user invokes $kelly-behavior-predict or /kelly-behavior-predict, wants to review funnel drop-off, per-segment predicted next actions, or backtest a rule-based "predicted next action" heuristic against a mock historical sample. Fully deterministic mock data and a hand-recomputable rule — never a real ML/LLM model, never a live 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。

Busabase App-in-Skill review queue that scores candidate SME financing deals (revenue-based/RBF-style credit) with a deterministic, fully auditable rule-based rubric — never an LLM or API call. Use when the user invokes $kelly-deal-scorer or /kelly-deal-scorer, wants to review a deal-underwriting queue, score financing candidates, compute a composite score breakdown, see a suggested revenue-share rate range, or record approve/send-back/reject decisions for a private-credit or RBF-style lending pipeline.

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 invoices, receipts, credit notes, statements, PDFs, images, docs, and spreadsheet-like invoice exports into a reviewable table with field confidence, line items, approval decisions, and CSV/JSON export. Use when the user invokes /kelly-invoice-sheet or $kelly-invoice-sheet, asks for "Invoice转表格", invoice OCR, receipt-to-spreadsheet, invoice data extraction, bookkeeping import prep, or a Lido-style Extract Data workflow with a Busabase-backed App-in-Skill UI.

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 the monthly revenue-accountable marketing report that B2B SaaS marketing leaders deliver to CEOs, CFOs, executive teams, and boards. Use when preparing the monthly CEO report, drafting the marketing section of a board deck, defending budget decisions, diagnosing pipeline gaps, or transitioning marketing from MQL/SQO reporting to revenue, CAC payback, and forward pipeline coverage. Designed for B2B SaaS demand generation leaders who own pipeline targets across segments, geos, and motions, and need to explain marketing's revenue contribution in executive language.

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。

Execute a single deterministic Python script to compute all derived financial metrics (EBITA, Invested Capital, Tax Rates, Summary Table) from extracted financial data.

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 timeline.json exists and the user asks to export the rough cut or an editable handoff packet to Premiere Pro.

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 repeatable dialogue MA, Studio-based or automatic portrait reframing, overlay-safe rendering, and loudness/sync QA to an existing interview or talking-head rough cut. Use when the user asks to make speech easier to hear, perform MA, enlarge or rebalance a person in frame, crop or punch in an interview, automatically propose framing, or prepare a finishing preview before final approval.

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 a URL and save the body to a temp file so you can pipe it through rg / jq / awk instead of dumping the whole page into the conversation. Use this whenever a user gives you a URL to read - articles, docs, PRs, gists, README files, blog posts, anything HTTP. Returns a path on disk; the agent then greps the path for the relevant section and quotes only what matters. Strongly preferred over the built-in webfetch tool when (a) the page is more than a few KB, (b) the user wants to find a specific phrase / table / code block on the page, (c) you need to keep the long page out of context, or (d) you want to inspect raw HTML alongside rendered markdown. Triggers: 'fetch', 'fetch this URL', 'fetch and save', 'fetch the page', 'pull this', 'grab this article', 'read this URL', 'read the docs at', 'mirror this page', 'page is too long', 'save the page so I can grep it', 'put it on disk so I can rg', 'cache this 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。

Build, improve, package, and review Codex skills from rough ideas, workflows, scripts, README/API/CLI docs, or existing skill folders. Use when the user wants to create a new Codex skill, convert a repeatable workflow into a reusable skill, scaffold SKILL.md plus references/scripts/assets, validate a skill, or prepare a skill for sharing on GitHub.

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 verify Comparison table for Documentation and knowledge with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design 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。

Build and verify Comparison table for Data-heavy application with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design 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。

Build and verify Comparison table for Mobile-first interface with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design 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。

Build and verify Comparison table for Marketing landing with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design 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。

Build and verify Comparison table for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design 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。

Build and verify Chart with table fallback for Commerce and checkout with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design 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。

Build and verify Chart with table fallback for Mobile-first interface with Bootstrap v5.3 using a working semantic implementation, responsive RTL/LTR behavior, theme tokens, test plan and design 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。