「json」の検索結果

Per-test JSON result files with metadata, versions, and infrastructure details

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Rust の性能改善を「計測 → 仮説 → 変更 → 再計測」のループで地道に回すためのスキル。プロファイラの選び方、Cargo の最適化設定、データ構造・アロケーション・ハッシュ・分岐などの定石をフェーズ順に適用する。1 回 1 変更 1 計測の規律を厳守する。rsvelte 固有の既知ボトルネック（`serde_json::Value` 駆逐、`bumpalo` 導入、`Atom<'a>`、codegen 直書き）と OXC 対応表、NAPI 検証手順も §7 に収録。「Rust の性能改善」「ボトルネック調査」「プロファイル取って最適化」などの依頼で使用。

One-time setup for absolute: interview how you want it to behave (output style, autonomy, TDD strictness, spec dir, families) + detect the stack once, then write `.absolute.config.json` (project, committed) and `~/.absolute/config.json` (user defaults + per-project overrides). Every other absolute-* command reads it instead of re-detecting; non-blocking — commands proceed without it and soft-recommend it. Triggers on "absolute init", "set up absolute", "initialize absolute", "configure absolute", "first-time setup", "remember my conventions for this repo".

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Aggregate a retort run's findings.jsonl into a machine-readable assessment.json summary with severity counts, penalty score, requirement coverage, and top findings.

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Emit the content signals reimagine-it reads from an HTML file — title, anchors, proper nouns, dates, numbers, emails, links, source hex colors, and the derived palette — as JSON without generating a redesign. Use when the user says /reimagine-it extract, "what does the engine see in this page", "extract the palette", "pull the facts from this page", or wants content analysis for research, migration, or design-system work. CLI: npx reimagine-it extract -i page.html -o signals.json. Part of the reimagine-it Content-Derived Design suite.

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Enterprise BPMN 2.0 diagram generator — converts natural language process descriptions into OMG-compliant BPMN 2.0 XML files and SVG previews via a 4-phase pipeline: Intent Extraction (LLM → JSON Logic-Core) → Validation (deadlock detection, structural soundness) → ElkJS Auto-Layout → BPMN XML + SVG output. Supports: multi-pool collaborations, message flows, boundary events (timer/error/signal), loop/multi-instance markers, data objects, all gateway types with correct gatewayDirection, and all BPMN 2.0 task types. Use this skill whenever the user wants to create, generate, or model a BPMN diagram, process flow, or workflow — even if they say "draw a process", "model this workflow", "make a BPMN for...", "create a Prozessmodell", "visualize this process", or describe a business process in natural language. Also use for editing or extending existing BPMN Logic-Core JSON.

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cheat-on-skill 的陪跑进度 skill。用户问“今天该干嘛”“我现在做到哪了”“继续学”“打卡”“我卡住了”“下一步是什么”时触发。读取 .skill-state.json 的 active.progress 和 learning_plan，给当天任务、检查完成情况、记录进度、根据快慢调整计划。前置：已有 .skill-state.json 且 active.learning_plan 存在。

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cheat-on-skill 的首次 onboarding。盘点用户能力（现有技能/可迁移底子/学历经验/每周可学时间/能坚持几个月/学习能力自评/地区/目标薪资/转型紧迫度），判定起点档位，创建 .skill-state.json 状态文件，是 skill-scan / skill-plan 的前置。触发词："能力盘点"/"我想转AI相关工作"/"找AI时代高薪工作"/"skill init"/"职业转型初始化"。**当用户想找/规划 AI 时代工作但 .skill-state.json 不存在时，先路由到此。**

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。

CesiumJS primitives and geometry - Primitive, GeometryInstance, Appearance, BufferPrimitive collections, GeoJsonPrimitive, Billboard/Label/PointPrimitive collections, built-in geometry shapes, ground primitives, classification. Use when rendering performance-critical static or vector geometry, loading GeoJSON without entities, creating custom shapes, batching draw calls, or using low-level collections.

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CesiumJS entities and data sources - Entity, EntityCollection, DataSource, GeoJsonDataSource, KmlDataSource, CzmlDataSource, Graphics types, PathGraphics, PathMode, Visualizers. Use when adding points, labels, models, polygons, polylines, or time-segmented paths, loading GeoJSON/KML/CZML/GPX data, or working with the high-level Entity API.

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Create or modify a konsistent.json configuration file that enforces structural conventions in a TypeScript codebase. Use when the user wants to enforce consistent code structure, validate exports/imports across files, ensure directories contain required files, enforce naming patterns, add/remove/update convention rules, configure case map overrides for acronyms or special casing (kebabToPascalMap, kebabToCamelMap), or troubleshoot why konsistent is reporting violations. Triggers on: "konsistent", "konsistent.json", "enforce conventions", "structural consistency", "consistent exports", "consistent structure", "code conventions config", "add convention", "update convention", "fix konsistent errors", "case map", "case override", "acronym casing".

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"/blueprint", "blueprint", "task graph", "contract derivation", "execution plan", "plan tasks from requirements", "contract-first planning" Turn requirements.md into an executable blueprint (plan.json + contracts.md). Five phases: Contracts → Tasks → Journeys → Verify Plan → Commit. Sits between /specify and /execute. Scope-adaptive (greenfield → bugfix). Uses hoyeon-cli (plan.json only; requirements.md is read as-is via Read 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。

Generate waveform visualizations from audio files. Use when a user asks to create waveform images, build audio player visualizations, generate waveform data for web players, create podcast episode previews, build audio thumbnails, render waveform PNGs for social media, extract peak data as JSON, or integrate waveform generation into audio processing pipelines. Covers audiowaveform CLI, JSON/binary data output, and web player integration.

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementations. The LLM never sees your code - it only sees the schema and description. Use when "agent tool, function calling, tool schema, tool design, mcp server, mcp tool, tool use, build tool for agent, define function, input_schema, tool_use, tool_result, agents, tools, function-calling, mcp, json-schema, anthropic, openai, llm-tools" mentioned.

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Create and edit JSON Canvas files (.canvas) with nodes, edges, groups, and connections. Use when working with .canvas files, creating visual canvases, mind maps, flowcharts, or when the user mentions Canvas files in Obsidian.

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Generate structured data markup (JSON-LD) for rich results, AI search visibility, and enhanced SERP features. Supports Article, FAQ, HowTo, Product, Review, LocalBusiness, Event, BreadcrumbList, Person, Organization, and more. Use when the user needs schema markup, structured data, rich snippets, JSON-LD, or wants to improve search appearance.

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Citation-integrity forensics: is every reference real, correctly attributed, and used in a context the cited work actually supports? Catches hallucinated references (no paper at the claimed arXiv id/DOI/venue, fabricated authors/year), metadata drift (wrong year/venue/version), and wrong-context citations (a real paper cited for a claim it never makes — or argues against). A hot zone for machine-generated papers. Decidable at L0 (text + canonical sources). Span-anchored to the evidence ledger (claims.json); the executor gathers canonical facts (DBLP / arXiv / DOI), then one FRESH cross-model thread per cited key proposes findings; reviewer != adjudicator. Emits citation-forensics.findings.json; NEVER computes the verdict. Triggers: "citation forensics", "check the references", "hallucinated citations", "wrong-context citation", "verify references", "引用核对".

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。

Flagship intra-paper self-consistency forensics: does the paper contradict ITSELF across abstract/intro/tables/body/appendix, and does the method DESCRIBED match the method EVALUATED? Needs no external ground truth — works PDF-only (L0). Runs a deterministic arithmetic pass + a fresh cross-model semantic pass, every finding span-anchored to the evidence ledger (claims.json), reviewer≠adjudicator. Emits consistency-audit.findings.json; NEVER computes the verdict. Triggers: "consistency audit", "check the paper against itself", "self-consistency", "内部自洽".

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Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation — verdict-bearing at L1 (the LaTeX source; PDF-extracted math is unreliable, so an L0 PDF-only run surfaces info only) — never asserts 'fabricated', only that the step shown does not hold. A fresh cross-model reviewer reads the theorem/proof + an extraction-only obligation scaffold and proposes per-obligation findings, each span-anchored to the evidence ledger (claims.json); reviewer≠adjudicator. Emits proof-derivation-forensics.findings.json; NEVER computes the verdict. dimension=proof, can be critical. Triggers: "proof forensics", "check this proof", "derivation integrity", "audit the math", "证明审计", "推导有没有漏洞".

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Checkable-ish surface presentation signals a reviewer notices first — duplicate/near-identical tables, leftover pipeline/template strings, too-few or LLM-looking figures, and page-padding. AUXILIARY ONLY and weak by design: a deterministic pass (tools/check_presentation.py — dup-table + pipeline-artifact) plus a fresh cross-model GROSS-cases-only semantic pass (thin-float, LLM-figure, page-padding), every above-info finding span-anchored to the evidence ledger (claims.json). The adjudicator CAPS everything at minor (SURFACE_ONLY_SKILLS + SURFACE_PATTERNS) — these contribute at most SOFT_FLAGS, never a HARD verdict — default false_positive_risk:high. NOTE: the pure AI writing-STYLE impressions (AI-flavor prose, defensive 'not-X-but-Y' hedging, narrative-arc, jargon-stuffing, invented codenames) MOVED to the zero-verdict-weight AIS track — for those use skills/ai-style-impressions, NOT this. Emits presentation-signals.findings.json; NEVER computes the verdict. Triggers: "presentation signals", "surface check", "duplicate tables", "排版信号".

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Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/check_ai_style.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS-* style tells (broken narrative arc, LLM phrase tics, jargon-stuffing, invented codenames, clause/formula walls, gratuitous pseudocode, bullet overuse, bold-module spam, restatement loops, focus drift, single-style figures, appendix dumping). Every finding is named, LOCATED, span-anchored to the evidence ledger (claims.json), carries not_integrity_finding:true + false_positive_risk:high + an fp_case, and gets ZERO verdict weight: the adjudicator forces it to info, excludes it from overall_verdict, and renders it in a SEPARATE report section. NOT an AI-text classifier — no scores, no "this is AI-written", no authorship probability; a paper can be CLEAN_GIVEN_EVIDENCE and still list many. Emits ai-style-impressions.findings.json; computes NO verdict; if a tell is actually substantive it routes to the integrity auditor. Triggers: "AI style", "vibe check", "writing fingerprint", "AI 文风", "vibe paper".

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。

Apify Actorization workflow skill. Use this skill when the user needs Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Apify Actorization workflow skill. Use this skill when the user needs Actorization converts existing software into reusable serverless applications compatible with the Apify platform. Actors are programs packaged as Docker images that accept well-defined JSON input, perform an action, and optionally produce structured JSON output and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

ADHX - X/Twitter Post Reader workflow skill. Use this skill when the user needs Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。