「ara」の検索結果

BOM management, sourcing, pricing, export, and fabrication preparation. Load the canonical Circuit Weaver skill from `skills/bom/SKILL.md`.

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 HyperFrames video composition from a brief/script autonomously. Uses separated builder and critic subagents — builders never review their own work. Quality decisions are grounded in project context (identity, audience, goals) and established visual/motion design principles. Measurable criteria (text size, contrast, overflow) are verified with tooling, not visual judgment. Use when the user says "make this video", "implement this brief", "build this composition", or provides a video script/brief to implement.

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。

Orchestrate work as a cyclic directed graph of subagent-executed nodes with transition criteria on edges, budgeted cycles, and per-node gates (runtime verification, metric, or artifact). Use when the user says "workgraph", "run this as a graph", "graph this work", or when a goal has parallel branches, feedback loops, or mixed gate types that a linear loop cannot express. Every node is sized to a 64k context budget, so small goals run as a two-node do/verify graph and large goals scale out without caps. For clock-driven or metric-driven single loops, timeboxed-iterating and autoresearch remain lighter.

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 timeframe-bounded research dossier by decomposing a topic into year orchestrators and month-level research passes that each write one condensed paragraph plus sources directly into a target file. Use when the user asks for a dossier, timeline, chronology, release history, or month-by-month research bounded by specific years or months. Not for timeless explainers or single-document summaries with no temporal scope.

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。

Catalog-then-fix slop removal on an existing prose draft. Parallel catalog subagents inventory four slop categories (theatrics, overwrought register, corrective antithesis, dramatic short sentences), fix subagents repair only what a catalog names, mechanical lint + frequency analysis bracket the run, and a regression gate (lint delta, word count, cold meaning check) decides whether the result ships. Use when the user says "deslop this draft", "catalog deslop", "strip the AI patterns from this file", or as the final stage of the article-factory and mosaic-writing skills. For writing or redrafting prose from scratch, use the prose skill instead - this skill only repairs an existing draft in place.

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。

Run a task iteratively over a user-specified duration by dispatching subagents. The orchestrator stays strictly linear and time-checked, but each iteration fans out MULTIPLE subagents in parallel over independent units (and those subagents may fan out further). Shared state — a compact progress digest and a growing environment cheatsheet — lives on disk and is passed to every subagent BY REFERENCE, killing the per-subagent rediscovery tax. Every subagent first reads an initialiser preamble that points it at that shared state and makes it write findings back, so the cheatsheet populates itself; role prompts are produced ONCE on the filesystem by a single scaffold command and dispatched by path, never re-typed per dispatch. Subagents persist their results to disk and return only a tiny status, so the orchestrator's context stays small and lasts. The orchestrator classifies the goal as FINITE or OPEN-ENDED: finite lists may finish early; open-ended goals use the full duration, never idle, never manufacture busywork. Use when the user gives a task and a duration and wants it ground out iteratively by subagents over that time.

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。

Fully autonomous bug hunting pipeline — discover bugs in a scoped area using parallel subagents, independently triage each finding, fix confirmed issues with subagents, then audit all fixes against repo constraints and target platforms. Runs end-to-end without user interaction.

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。

Write the SQL or MongoDB query that answers a question and keeps answering it: the right grain, no fan out, parameterised window, checked against something known. Use when a number is needed that no report carries, when an existing query returns something that looks wrong, when a query is slow or expensive, or when somebody asks for a one off pull.

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 the complete Google Play Store publishing pipeline — upload key generation, AAB bundle optimization, Fastlane supply lanes for internal/beta/production tracks, staged rollout strategy, store listing metadata, data safety form guidance, and content rating preparation

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。

Full quality pass -- chains parallel UX + scalability audit, then QA verification, then consistency gate. Fixes everything it finds. Works with any stack.

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 corporate tax compliance systems across jurisdictions. Analyzes tax engine configuration (Avalara, Vertex, ONESOURCE), nexus determination and Wayfair economic nexus tracking, sales/use tax calculation accuracy, federal and state income tax compliance (book-to-tax adjustments, NOL.

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。

Displays the full skills catalog with descriptions, autonomous build chains, recommended pipelines, and parallelization rules.

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 a real estate analytics platform -- evaluate comparable sales and rental engines, automated valuation models (AVM), demographic and economic indicator pipelines, submarket scoring, gentrification detection, price forecasting accuracy, market cycle analysis, and risk modeling.

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。

Reconstructs the development cycle from git history, distills sequential/parallel patterns, and produces actionable insights for improving future iterations. Triggers: recall, retrospective, development analysis, what happened, dev cycle 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。

Audit content licensing and rights management systems for territory and window tracking, royalty calculation accuracy, rights clearance workflows, and contract compliance across music, film, television, and digital media..

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 litigation analytics and case outcome prediction systems -- ML outcome models (logistic regression, gradient boosting, neural nets with temporal train/test splits), settlement range modeling (Monte Carlo simulation, comparable case matching, BATNA 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。

Maps dependencies between stories, code modules, tickets, or specs. Computes optimal implementation order with parallel batches, cycle detection, and critical path 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。

Run multi-source web research with adversarial verification and produce a cited report: decomposes the question into 3-6 distinct queries (broad, specific, comparative, recency-anchored), searches and ranks sources by authority, recency, and specificity.

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 ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.

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。

Evaluate elder care fall risk prediction systems including wearable sensor integration, gait and balance analytics, risk scoring algorithms, environmental hazard detection, medication interaction flagging, mobility trend tracking, and alert/response workflow effectiveness.

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 pharmaceutical batch production records for yield optimization, process parameter tuning, deviation trending, and cycle time reduction under cGMP compliance. Triggers: phrases: "optimize batch yield", "analyze batch records", "pharma manufacturing 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。

Audit credit risk modeling software for scoring algorithm accuracy, regulatory compliance (ECOA, FCRA, SR 11-7), bias and disparate impact testing, model governance lifecycle, and explainability..

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 branded PowerPoint decks (.pptx) for pitches, internal updates, sales decks, customer QBRs, board updates, conference talks using the Pyramid Principle and SCQA framework (Barbara Minto, The Minto Pyramid Principle). Every deck starts with the answer - conclusion on slide 2, evidence after. Slide titles are conclusions, not topics. Use when the user asks for a deck, presentation, slides, pitch deck, sales deck, board deck, QBR, "make slides for X", or "build a presentation". Uses templates/ for brand assets. Generates structured slide content with speaker notes. For the narrative content of the deck, see thought-leadership-writer or campaign-brief. For a case study slide, see case-study-writer.

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。

**WORKFLOW SKILL — MANDATORY for ANY hook work.** Create, configure, edit, debug, or review lifecycle hooks for VS Code Copilot (default) and Claude Code. USE FOR: criar/configurar/editar hook, hook do Copilot/Claude/agente, lifecycle hook, PreToolUse, PostToolUse, SessionStart, SubagentStop, Stop hook, UserPromptSubmit, PreCompact, bloquear comando, validar tool call, injetar contexto, guard, .agent.md frontmatter hooks, hooks.json, .github/hooks, .claude/settings.json, hook Node.js, cross-platform, hook não dispara/silencioso/ignorado, permissionDecision, hookSpecificOutput, additionalContext. **DEFAULT = VS Code GitHub Copilot.** Switch to Claude Code only when user explicitly says "Claude" OR workspace has `.claude/`. NEVER assume Claude. DO NOT USE FOR: general coding, creating agents (use agent-creator), creating skills (use skill-creator).

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。