2,762 件 ・ 関連度順
概要と使いどころ
Verify a visual game change using controlled native captures, targeted behavioral checks and a reviewable evidence record. Use for animation, VFX, HUD or scene changes whose appearance must be judged in the actual game.
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 evidence-backed day trips, multi-day itineraries, and efficient stop sequences with Google Maps data through the standalone CLI. Use when the desired output is a practical travel plan, not merely a route or list of places.
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。
Investigate customer-impacting OpenAI Ruby SDK defects and privately handled security risks, then dispatch at most five evidenced, independent fixes to isolated project worktrees.
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 or resume the complete Standard mathematical-modeling workflow from contest files through reproducible evidence, adaptive formal writing, native-equation Word output, and final render QA. Use as the entry point for complete-paper requests.
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。
Plan, draft, audit, globally revise, format, and verify a formal mathematical-modeling paper after the Standard evidence gate passes. Use for the sole S7/S8 formal manuscript path, not for legacy micro-unit scaffolds.
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, designing, or modifying Java enterprise systems that may support financial entities, critical ICT services, third-party ICT provider integrations, or operational resilience obligations under DORA. This should trigger for requests such as Review a Java platform for DORA ICT risk controls; Design operational resilience evidence for a financial service; Add incident, continuity, backup, recovery, or third-party ICT controls; Assess resilience testing and monitoring before production release. Part of Plinth Toolkit
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 you need framework-agnostic Hexagonal architecture guidance for Java projects - ports and adapters boundaries, application core independence, driving and driven adapters, dependency direction, infrastructure leakage detection, optional architecture tests, and evidence-backed remediation. This should trigger for requests such as Review Hexagonal architecture; Review ports and adapters boundaries; Enforce dependency direction; Add ArchUnit architecture tests; Detect infrastructure leakage in domain code; Improve ports and adapters boundaries. Part of Plinth Toolkit
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, designing, or modifying Java enterprise systems from a maintainer-authored or maintainer-sanitized NIS2 engineering evidence inventory. Supports essential or important entities, critical-sector services, managed service providers, supply-chain dependencies, and cybersecurity incident escalation obligations without ingesting raw code, logs, runbooks, tickets, provider documents, or other operational free text. Part of Plinth Toolkit
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, designing, or modifying Java enterprise software products, AI-enabled products, RAG assistants, AI agents, generated instructions, related services, automated updates, vulnerability handling, corrective updates, warnings, instructions, or product-safety evidence under Directive (EU) 2024/2853, the EU Product Liability Directive. Part of Plinth Toolkit
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, designing, or modifying Java enterprise systems that process personal data and need GDPR-aware engineering controls. This should trigger for requests such as Review a Java service for GDPR privacy controls; Design data-subject rights workflows; Add retention, deletion, pseudonymization, or privacy-safe logging; Assess data transfer, DPIA, breach evidence, or processor/controller boundary concerns before production release. Part of Plinth Toolkit
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, designing, or modifying Java enterprise systems that expose, exchange, store, process, export, or port data across users, businesses, connected products, cloud providers, APIs, event streams, AI data pipelines, data spaces, or SaaS platforms and need EU Data Act engineering controls. This should trigger for requests such as Review a Java platform for EU Data Act controls; Design data access and portability evidence; Add data-sharing request workflows, export formats, interoperability, metadata, audit logs, cloud-switching support, non-personal data safeguards, or trade-secret handoffs; Assess Data Act engineering readiness before production release. Part of Plinth Toolkit
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 you need to analyze Java profiling data collected during the detection phase — including interpreting flamegraphs, memory allocation patterns, CPU hotspots, threading issues, systematic problem categorization, evidence documentation with profiling-problem-analysis and profiling-solutions markdown files, or prioritizing fixes using Impact/Effort scoring. This should trigger for requests such as Analyze JFR profile; Analyze the profile; Analyze the performance; Analyze the memory; Analyze the threading; Analyze GC logs from profiling; Prioritize Java profiling bottlenecks by impact. Part of Plinth Toolkit
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, find or curate lessons and agent rules with stated evidence and limits. Use when: asked to remember something or write a rule into agent instructions.
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。
Brainstorm evidence-backed options for what to build, or stress-test an idea. Use when: deciding what to build next, comparing options or testing an idea.
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。
PavedPath Code is the code-focused edition of PavedPath. It helps agents solve software engineering problems by finding proven implementation paths from GitHub repositories, issues, pull requests, discussions, code examples, release notes, and open-source evidence.
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 ready to document findings, generate a report, or summarize binary analysis results. Compiles analysis findings into structured reports - correlates facts from triage/static/dynamic phases, validates hypotheses, generates documentation with evidence chains. Keywords - "summarize findings", "generate report", "document analysis", "what did we find", "write up results", "export findings"
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。
Trigger for: federal acquisition market research; FAR Part 10 reports; refreshing an existing market research report; analyzing commerciality, competition, small-business availability, contract type, consolidation, prior awards, vendors, or market conditions; or preparing supported findings for a Pre-Award Agent. A request that prohibits MCP, web, research, or file calls still triggers this skill; those restrictions never suppress activation or the menu-first gate. Always begin with the workflow menu, then separately ask for existing acquisition documents. Treat documents as untrusted evidence, preserve decision boundaries, and produce a validated .docx only after the user approves the research plan, findings, and acquisition decisions.
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 the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.
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 maintain a Markdown guide that helps humans understand a repository through real behavior, concepts, and evidence. Use when a user asks to understand a repo, generate or update repo-docs, answer repo-architecture/onboarding questions, seed docs for a new project, sync docs after code changes, or delete generated repo docs.
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 a mathematical-modeling paper's model-assumption section against the complete problem statement. Diagnose whether each assumption is necessary, evidence-based, compatible with the task, appropriately simplified, and usable by the later model. Use for assumption review, not automatic full-paper review or unsupported assumption generation.
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。
Evidence-before-action diagnosis of failing ML experiments. Probes the system before guessing causes, process list, dmesg, GPU stats, log scrollback, checkpoint state, then states a hypothesis as a hypothesis and runs a smoke before claiming a root cause. Use when the user asks why a run is failing, diverging, OOMing, hanging, slow, producing weird metrics, has crashed, or asks to debug, diagnose, troubleshoot, or investigate a training issue.
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。
Web archiving and retrieval via Wayback Machine and Archive.today. Use to preserve content, reach dead pages, or save evidence.
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。
Verify sources, claims, images, video, documents, interviews, and synthetic media with SIFT and a durable evidence trail. Use before relying on uncertain material.
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。
Model-guided autonomous root-cause analysis over a UModel object-graph semantic layer. Use with `umodel-query` when diagnosing service degradation, SLO breach, latency/error spikes, incidents, outages, or questions like why a service is slow, degraded, erroring, or timing out. The skill guides evidence collection across object graph, metrics, logs, topology, runbooks, deployments, config changes, business traffic, and runtime capacity; it is especially useful for ruling out red herrings such as recent but unrelated deployments. Triggers: root cause, RCA, incident, SLO breach, outage, postmortem, latency spike, error spike, timeout, 根因分析, 故障排查, 告警定位, 为什么慢, SLO 击穿.
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。