「evidence」の検索結果

Diagnose slow or freezing VS Code-compatible editors with evidence-first, zero-hardcoded-assumption workflow. Use when the user reports editor lag, typing delay, UI freezes, extension host stalls, file watcher noise, high editor CPU/RSS, uses VS Code/Cursor as a file browser over a large folder, or wants a safe editor performance audit.

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, audit, or implement application structured logging architecture from repository evidence. Use when a user asks whether or where to add logs, how to choose or migrate a logger, how to standardize events/fields/levels/redaction, how to add HTTP access or panic logs, or why production logs cannot answer an incident question. Do not use merely to tail platform logs or to design a full metrics, tracing, SLO, and incident-management program.

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。

Diagnose excessive Codex local SQLite diagnostic log writes with read-only evidence by default. Use when a user mentions logs_2.sqlite, logs_2.sqlite-wal, block_log_inserts, SSD/TBW wear, or explicitly asks to protect, clean up, verify, or restore Codex diagnostic logging.

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, produce, or diagnose evidence-backed product demo videos and screen-recorded promotional walkthroughs. Use when the user asks to make a product demo, launch video, feature showcase, app walkthrough, demo reel, or polished recording; wants every important capability shown without a slow feature tour; asks to remove dead time or fix narration-to-action pacing; or needs a repeatable script, capture plan, and verified final media. Do not use for fictional commercials with no real product proof, general-purpose video editing, or documentation-only tutorials that do not need a promotional narrative.

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-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.

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。

Generate or update repository-specific AGENTS.md instruction files from real repo evidence. Use when asked to create, design, scaffold, split, or improve root or scoped AGENTS.md files for Codex/Claude/agent workflows, especially when a repo needs directory-specific rules, validation commands, generated-file boundaries, or a short agent onboarding router.

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。

Reverse engineer native, managed, Electron/JavaScript, packaged, firmware, and browser targets with REA. Use shipped-artifact or requested runtime evidence to explain features, compare versions, decompile code, or guide a reconstruction. Skip REA for ordinary source-repository architecture 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。

Review mathematical agent trajectories for evidence-backed Jacobian improvements; do not resume solving.

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, implement, audit, or refactor web and game-adjacent interfaces using an evidence-based Hypergryph visual language with two independent choices: product family and application depth (1 minimal, 2 moderate, 3 complex, 4 maximal). Use for Hypergryph-, Arknights-, Endfield-, Rhodes Island-, industrial sci-fi-, editorial game-UI-, or bilingual technical-interface requests; for landing pages, dashboards, menus, HUD-like panels, design systems, HTML/CSS/JS/React components, and visual QA where the result should carry this family resemblance without copying protected logos or artwork.

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。

Conduct deep web research using the openbrowser-ai agent: decompose a query, investigate sub-questions across multiple sources, and produce a cited markdown report plus structured JSON under local_docs/research/. Trigger when the user asks to: research a topic, do a deep dive, investigate, gather evidence, compare options, write a literature review, build a briefing, or produce a cited report.

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。

Summarize document text and identify its main claims, evidence, and unanswered questions.

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。

Assess an openai-guardrails-python GitHub issue or pull request as a maintainer. Use to verify the claimed need and practical impact, compare supported alternatives or competing approaches, separate code quality from repository readiness, recommend the maintainer action, and draft a copy-ready comment when evidence, changes, or closure should be requested.

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。

Migrate lane for fskill-creator; analyzes a legacy skill directory into behavior map, function split proposal, and 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。

Audit Blender scenes and assets for topology, transforms, normals, surface continuity, classified connections, intersections, modifier/node intent, procedural performance, simulation stability, protected-scene changes, and fixed-view visual evidence. Use after every major modeling or simulation stage and before render, bake, export, or claims of completion.

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。

Convert image references into spatial hypotheses, camera constraints, construction routes, material evidence, and measurable Blender reconstruction gates. Use whenever a user asks to replicate, recreate, match, clone, or reconstruct a scene, environment, object, material, lighting setup, or shot from one or more images, especially when room connectivity, hidden space, perspective, visible-frame fidelity, material identity, aging, wetness, or photographic treatment matters.

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 and maintain GRACE 4 verification entries, commands, scenarios, markers, and assertion evidence under .grace/verification.

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。

Create GRACE 4 worker and reviewer subagent presets that understand .grace artifacts, scopes, assertions, and verification 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。

Review GRACE 4 integrity across .grace artifacts, active changes, scopes, assertions, code anchors, and verification 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。

Debug and fix issues in a GRACE 4 project using .grace semantic navigation, assertions, and verification 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。

Establish explicit traceability between formal specifications (preconditions, postconditions, invariants) and verified code components with their correctness proofs. Produce structured Markdown mapping reports showing verification coverage and proof evidence. Use when auditing formal verification, documenting verified systems, establishing traceability for certification, or when the user asks to map specifications to code, generate verification reports, or analyze verification coverage in Coq, Dafny, Isabelle, or other proof assistants.

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 CVE reachability in software repositories by examining how vulnerable dependencies are imported and used. Determines whether vulnerable components, classes, or functions are reachable from project code through call chain analysis, reflection detection, dynamic loading patterns, and configuration-gated behavior. Classifies each CVE as likely reachable, possibly reachable, or likely unreachable with supporting evidence. Use when analyzing security vulnerabilities in dependencies, performing post-disclosure CVE triage, assessing vulnerability impact, or when users ask to analyze CVE reachability, check if vulnerabilities are exploitable, or evaluate dependency security 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。

Identify the precise location of bugs in source code, modules, and systems. Use this skill when debugging applications, investigating test failures, analyzing error reports, tracing runtime issues, or performing root cause analysis. Analyzes stack traces, error messages, failing tests, and code patterns to pinpoint buggy functions, classes, files, or modules with confidence rankings and supporting 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。

Forensic case investigation with evidence-graded findings, calibrated to the input. Use when the user asks to investigate a bug, trace what caused an incident, walk through unfamiliar code, or build a mental model of a code area before working on it.

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 vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.

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。