「eas」の検索結果

Prepare a release by bumping the build number, collecting user-facing changes into a changelog, and committing the 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。

Maintain CHANGELOG.md — add new version entries, keep the Unreleased section, cut a release, or restructure the whole file — following the Keep a Changelog format and Semantic Versioning.

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 legacy prompts or older framework flows reference `release-manager` and you need the modern runtime to resolve that capability cleanly.

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。

Executes authorized phishing simulation campaigns to assess an organization's susceptibility to email-based social engineering attacks. The tester designs realistic phishing scenarios, builds credential harvesting infrastructure, sends targeted phishing emails, and tracks open rates, click-through rates, and credential submission rates to measure human security awareness. Activates for requests involving phishing simulation, social engineering assessment, email security testing, or security awareness measurement.

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 measured semantic core for a brand through official keyword APIs (Yandex Wordstat for RU, Google Ads Keyword Planner / Bing Webmaster worldwide, autocomplete everywhere), commit it as core.json plus a questions.csv, and hand that straight to an open-geo visibility run. Use when the user wants demand research, a semantic core, keyword volume, or "collect the questions and then measure visibility" — no browser and no manual keyword tool.

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 an end-to-end GEO visibility measurement through a real AI interface, persist the captures, and return a portable JSON run artifact plus optional PDF/dashboard outputs. Use automatically on an explicit request to measure a brand's AI-search visibility, and as a composable data-collection step inside another agent workflow; the user should not have to launch the pipeline or dashboard manually.

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。

Agentic workflow patterns for autonomous LLM reasoning. Use when building ReAct agents, implementing reasoning loops, or creating LLMs that plan and execute multi-step tasks.

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。

Pre-release checklist for Forge — runs verify, checks version bump, changelog, docs, and docker build. Use before tagging v*.

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。

Pre-release checklist for Forge — verify, version bump, changelog, docs, docker build, smoke test. Run before tagging v*.

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。

Turn any context (blog URL, pasted article, PR diff, meeting notes, release notes, raw prompt) into a narrated explainer mp4 with slides, subtitles, and optionally a talking-head avatar. Uses a free local stack (Pillow + edge-tts + ffmpeg) with optional SadTalker for the avatar. Use when the user says "動画化", "explainer video", "解説動画作って", "ブログを動画に", "記事を動画化", "PRを動画で説明", "議事録を動画化", "context to video", or wants an mp4 from arbitrary text/source. Output is mp4 + srt under the user's chosen project workspace.

Create reproducible animated infographic GIFs from structured data or measured results, with audience-language labels, explicit timing semantics, representative-frame QA, and a static fallback. Use when asked for GIF生成, animated infographic, 動的インフォグラフィック, 結果が流れる図, animated benchmark, or a blog/SNS animation whose displayed metrics must remain traceable to source data.

"Apply Structural Equation Modeling (SEM) to test hypothesized causal structures by combining measurement models (CFA) and structural models (path analysis). Use this skill when the user needs to validate latent constructs, test mediation or moderation paths, assess model fit with CFI/TLI/RMSEA/SRMR, or when they ask 'do these variables form a causal chain', 'how do I test my theoretical model', or 'is my measurement model valid'.".

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 the SERVQUAL model (Parasuraman, Zeithaml, and Berry, 1988) to measure service quality gaps across five dimensions. Use this skill when the user needs to diagnose service quality shortfalls, benchmark customer expectations against perceptions, design service improvement programs, or when they ask 'where is our service failing', 'what do customers expect vs experience', or 'how do we measure service quality'.".

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 Partial Least Squares SEM (PLS-SEM) with reflective and formative measurement models to maximize explained variance in endogenous constructs. Use this skill when the user has small samples, formative indicators, or exploratory models, needs to assess AVE/CR/HTMT, or when they ask 'should I use PLS or CB-SEM', 'how do I handle formative constructs', or 'what is the path coefficient significance'.".

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"Apply the DeLone and McLean Information Systems Success Model to evaluate IS effectiveness through six interdependent dimensions. Use this skill when the user needs to assess system quality, information quality, or service quality of an IS, diagnose why users are dissatisfied, measure net benefits of a system investment, or when they ask 'how do we measure IS success', 'why are users unhappy with this system', or 'is our system delivering value'.".

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"Develop e-commerce strategy for Southeast Asian markets including platform selection, payment infrastructure, logistics challenges, and localization requirements. Use this skill when the user is expanding e-commerce to SEA, evaluating Shopee vs Lazada vs Tokopedia, or needs to understand SEA market differences — even if they say 'sell to Southeast Asia', 'which platform in Vietnam', 'SEA payment methods', or 'cross-border e-commerce in ASEAN'.".

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。

"Solve the newsvendor problem for single-period ordering decisions under uncertain demand. Use this skill when the user needs to determine optimal order quantity for perishable goods, seasonal products, or one-time purchase decisions — even if they say 'how much to order for this season', 'perishable inventory', or 'single-period ordering'.".

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"Apply the Balanced Scorecard (BSC) framework to translate strategy into measurable objectives across Financial, Customer, Internal Process, and Learning & Growth perspectives. Use this skill when the user needs to set strategic KPIs, create a strategy map, align organizational goals, or connect daily operations to strategic vision — even if they say 'how do we measure strategy execution' or 'our KPIs feel disconnected'.".

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 social media influence using engagement-weighted metrics beyond follower count. Use this skill when the user needs to evaluate influencer effectiveness, compare influence across accounts, or build an influence scoring system — even if they say 'who is more influential', 'influencer ranking', or 'measure social impact'.".

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 preparing or publishing a local project as a clean GitHub repository. Covers new repositories, private staging releases, public README cleanup, updates to existing remotes, and research-code packaging without datasets, checkpoints, papers, or generated artifacts.

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。

Performs a lightweight pre-release readiness review of Atlassian Forge apps across manifest/module wiring, architecture, runtime compatibility, dependency posture, tests, deploy readiness, and obvious security, cost, or reliability smells. Use when the user asks "review my Forge app", "pre-deploy check", "is this app ready to ship", "review manifest", "general app review", "release readiness", or asks for a broad quality pass. Do not use for deep security audits/SAST/exploitability review, cost optimization, or diagnosing a known broken app; route those to forge-security-review, forge-cost-optimizer, or forge-debugger respectively.

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。

Review computational protein or gene function predictions and fill PredictionReview YAML files using the COR/CNN/LSP/UNC/PLI/NPI/REP biological-validity taxonomy. Use for ProtNLM, DeepECTF, BioReason/GO-GPT, InterPro2GO, PANTHER/IBA, CLEAN, GloEC, MAPred, ProteinInfer, or other predicted EC/GO annotations, including audits of the evidence and reasoning in existing prediction reviews.

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。

Fierce autonomous internal co-founder. Bounded web research for evidence-backed improvements. Structured idea formulation with self-critique filter that rejects ideas not serving the goal. Auto-implements high-confidence aligned improvements, proposes medium-confidence ideas for approval. Evaluates viral coefficient, AI search visibility (GEO), and solo SaaS economics. Considers higher pursuits: employing disabled people, spiritual tech investigation, 99% wealth donation ethos.

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。

EAS Build, Update, Submit deployment patterns

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。