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概要と使いどころ
A workplace-safety compliance review for a manufacturing regulator — findings, the evidence chain, and the corrective mandate. Built as a decision-grade policy briefing deck for regulator, plant leadership.
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OpenDesign's enterprise AI-adoption brief: local-first agents at work, the risk controls, the ROI, and the rollout plan. Built as a decision-grade AI literacy deck for leadership, IT, security.
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Build a complete design system from scratch with creative risks and realistic product mockups. Useful for kickoff workshops and brand-from-zero 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。
Turn messages, meeting notes, and documents into sourced decisions, actions, dependencies, and risks
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。
Retrieve disease outbreak alerts and PM2.5 air-quality health warnings. Use when the user asks about current public-health risks in a country, city, or region.
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Read current stock research, technical backtests, saved analyses or saved backtests. Use for a named ticker or watchlist research history; preserve source dates, model limitations and risk factors.
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Use when you need to analyze git diffs or pull requests to understand what changed, affected components, and risks
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Evaluate and carry out non-trivial software-system changes from first principles. Use when assessing RFCs, issues, designs, features, refactors, migrations, dependency changes, or proposed fields, events, APIs, modules, and services whose need, consumers, system fit, validation, or rollback require scrutiny. Read the actual system, identify the concrete problem and named semantic consumers, choose the smallest sufficient solution, reject pseudo-requirements and speculative abstractions, and require evidence proportional to risk. Do not use for mechanical edits, source-code explanation, or a dedicated review of an already-complete diff.
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Use when a DeerFlow maintainer needs comment-only GitHub issue or PR handling: resolve issue/PR scopes with gh, analyze issues, post or draft issue comments, perform PR review comments, review PR or issue batches, compare competing PRs that target the same issue, give fix strategy, risk classification, and validation guidance. Intended for maintainers and trusted local agents, not general contributors.
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。
Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan
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。
7-section repo readiness report from `metaharness genome {path}`. Returns repo_type / agent_topology / risk_score / mcp_surface / test_confidence / publish_readiness. Pure-read; degrades gracefully (ADR-150).
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 reviewing image assets, markup, and CDN or build transforms related to Set explicit width and height on images. Check encoded size, rendered size, loading strategy, and above-the-fold impact together. Separate real CLS risks from tiny decorative SVGs or ornament-only assets that are unlikely to move surrounding content.
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When the user wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.
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。
Turns implementation work into working code, reviewed and verified. Use when the user delegates a feature, story, bug fix, or meaningful change; a bare story or issue link counts. Skip obvious, low-risk mechanical maintenance such as small ignore-file, typo-only, formatting-only, or configuration-hygiene edits. Explicit BMAD requests always qualify. Do not volunteer for user-directed interactive edits or version-control operations that only record existing 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。
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題, 심층 연구, 문헌 조사, 체계적 문헌고찰, 메타분석, 사실 확인, 연구 방향을 잡아줘, 연구 주제 정하는 것을 도와줘, revisión de literatura, metaanálisis
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Builds, evaluates, and audits right-censored or competing-risk survival workflows with scikit-survival, including leakage-safe preprocessing, model selection, probability prediction, and censoring-aware metrics.
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Evaluates scientific claims and evidence quality. Applies to experimental design validity, biases and confounders, statistical interpretation, evidence grading frameworks (GRADE, Cochrane Risk of Bias), and teaching critical analysis. Supports evidence appraisal and identifying flaws; formal peer review writing belongs to peer-review.
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Supports multivariate severity assessment and exploratory endpoint-time score forecasting for laboratory animal studies using the RELSA (RELative Severity Assessment) score and ARIMA-based foRcast forecasting. Use when combining welfare readouts — body weight or weight loss, body temperature, clinical or nesting scores, biomarkers, activity, heart rate, burrowing, wheel running — into one severity score per animal per day, when asking which animals are at risk of reaching a humane endpoint at a specified future observation time, when defining attention/danger zones or thresholds on a severity scale by kernel density estimation, or when reporting severity for a 3Rs, refinement, animal-welfare, or EU Directive 2010/63/EU severity-assessment context. Covers directionality ("turned" variables), baseline normalization, reference sets, RELSA weights, ARIMA prediction intervals, and RMSE/PICP/MPIW evaluation.
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 code changes with GitNexus from a GitHub PR URL or number, a branch/ref or commit range, or local staged, unstaged, and untracked changes. Use when the user asks for a code review, merge-risk assessment, regression hunt, missing-test analysis, or a verdict on whether a PR, branch, commit range, or local diff is safe.
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Expert security auditor for AI Skills and Bundles. Performs non-intrusive static analysis to identify malicious patterns, data leaks, system stability risks, and obfuscated payloads across Windows, macOS, Linux/Unix, and Mobile (Android/iOS).
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Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
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Review repository architecture using Ontoly Software Graph and MCP capabilities. Use when asked to explain architecture, module boundaries, package topology, service ownership, or architectural risk.
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 structured AI red team exercises for jailbreak resistance, data exfiltration risk, harmful output controls, and agent tool abuse resilience.
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。
Runs a bounded spec-build-review development loop with explicit scope, stop conditions, and human approval gates for risky or ambiguous 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。