「eas」の検索結果

Generate AGENT.md files with proper YAML frontmatter, role definitions, expertise areas, and prompt templates following Babysitter SDK conventions.

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。

Guide for authoring specialized AI agents. Use when creating, updating, or improving agents, choosing models, defining focus areas, configuring tools, or learning agent best practices.

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。

Validates agent configurations for model selection, tool permissions, focus areas, and approach quality. Use when reviewing, auditing, improving agents, or learning agent best practices.

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 local Granola archive search, sync freshness, notes/transcripts/panels, Markdown export, snapshots, Keychain-safe source debugging, and Graincrawl repo/release work.

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。

Mosaic environment-sensitive areas (tenant name, user avatar, subscription name / ID, resource group, internal hostnames) on Azure Portal screenshots before publishing to blogs, Qiita, Medium, slides, or demos. Use when sanitizing screenshots, masking env info, or preparing portal captures for public sharing. Keywords: Azure Portal, screenshot, mask, mosaic, sanitize, tenant, subscription, public publishing, blur, redact.

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。

Design, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.

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 historical VS Code GitHub Copilot Chat sessions by model, reasoning effort, AIU, time, reliability, workflow behavior, and external quality evidence, or safely prune workspace-scoped local chat history by age. Use for session analysis, repeated-task retrospectives, model evaluation, cost/performance analysis, and old session 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。

Build PFC 5.0 asphalt-mixture specimens with explicit aggregate objects, a declared homogenized-mastic or fine-particle abstraction, residual air voids, measured gradation conversion, contact-pair assignment, compaction, equilibrium, and mass-volume closure; use before Marshall, rutting, creep, or strength tests.

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。

Govern Asphalt-Codex-Skills-5 as a PFC 5.0-only asphalt-mixture package, enforcing version purity, external fistPkg boundaries, portable assets, validation, and release hygiene.

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。

Redistricting analysis in R using the redistverse ecosystem. Use whenever the user is working with redist, redistmetrics, ggredist, geomander, adj, alarmdata, PL94171, censable, easycensus, tinytiger, baf, rict, or redistio. Covers the complete pipeline: Census and spatial data loading, adjacency graph construction, SMC/MCMC simulation, constraints (population balance, county splits, VRA compliance), convergence diagnostics, plan metrics (compactness, partisan fairness, splits), visualization, summary tables, and interactive plan drawing. Invoke whenever the user mentions redistricting, gerrymandering, district plans, simulation ensembles, or any redistverse package by name.

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 you state a fact. End the answer with a short Sources list mapping each claim to a file, URL, or reasoning. Shared across agents.

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 you state a fact. Add a short "Sources:" list at the end of your answer with where each claim came from (file name, URL, or "reasoning"). Shared by every agent on the team.

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。

Post-ship documentation update. (gstack).

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。

"Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware with per-person contributions, praise, and growth areas. Use when asked for weekly retro, what shipped this week, or engineering retrospective.".

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 rigorous survey design principles including construct operationalization, Likert scale development, reliability and validity assessment, and common method variance control. Use this skill when the user designs questionnaires, develops measurement items, needs to evaluate Cronbach's alpha or AVE, or when they ask 'how do I operationalize this construct', 'is my scale reliable', or 'how do I control for CMV'.".

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"Apply the Technology Acceptance Model (Davis, 1989) and Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003) to predict technology adoption. Use this skill when the user needs to evaluate user acceptance of a new system, diagnose adoption barriers, design interventions to improve technology uptake, or when they ask 'why aren't users adopting this', 'what drives technology acceptance', or 'how do we increase adoption rates'.".

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"Apply the Theory of Planned Behavior to predict behavioral intentions from attitudes, subjective norms, and perceived behavioral control, and identify intervention leverage points. Use this skill when the user needs to predict adoption of a new behavior, diagnose why an intended behavior does not occur, design behavior change campaigns, or when they ask 'why do people not follow through', 'what predicts behavior change', or 'how to increase adoption rates'.".

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"Apply sustainability frameworks (triple bottom line, SDGs, ESG, circular economy) to evaluate whether strategies balance economic, social, and environmental dimensions. Use this skill when the user needs to assess ESG performance, design circular economy strategies, align business models with SDGs, or when they ask 'is this strategy truly sustainable', 'how do we measure ESG impact', 'what does a circular business model look like', or 'how do we avoid greenwashing'.".

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"Apply event study methodology to measure abnormal returns and cumulative abnormal returns (CAR) around corporate or market events. Use this skill when the user needs to quantify the market impact of announcements, design event and estimation windows, or when they ask 'did this event affect stock price', 'how do I calculate abnormal returns', or 'what is the market reaction to this announcement'.".

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"Apply Hierarchical Linear Modeling (HLM) to analyze nested data structures with random intercepts and slopes, accounting for intra-class correlation and cross-level interactions. Use this skill when the user has students nested in schools, employees in firms, or repeated measures in individuals, needs to partition variance across levels, or when they ask 'how do I handle nested data', 'what is ICC', or 'do group-level factors moderate individual-level relationships'.".

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Draft blog posts, social media, email newsletters, landing pages, press releases, and case studies with channel-specific formatting and SEO recommendations. Use when writing any marketing content, when you need headline or subject line options, or when adapting a message for a specific platform, audience, and brand voice.

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。

Draft marketing content across channels — blog posts, social media, email newsletters, landing pages, press releases, and case studies. Use when writing any marketing content, when you need channel-specific formatting, SEO-optimized copy, headline options, or calls to action.

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 compliance check on a proposed action, product feature, or business initiative, surfacing applicable regulations, required approvals, and risk areas. Use when launching a feature that touches personal data, when marketing or product proposes something with regulatory implications, or when you need to know which approvals and jurisdictional requirements apply before proceeding.

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。

"Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.".

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。