「json」の検索結果

Generate abstract Control Flow Graph (CFG) representations of programs showing loops, branches, and function calls for static analysis or verification. Use when users need to: (1) Visualize program control flow structure, (2) Generate CFGs for static analysis tools, (3) Create control flow abstractions for formal verification, (4) Analyze program paths and reachability, (5) Document program structure. Supports both function-level (intraprocedural) and program-level (interprocedural) analysis with multiple output formats (textual, DOT/Graphviz, JSON).

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Generate prioritized CVE watchlists and actionable security recommendations for repositories. Use when analyzing CVE scan results, creating security reports, prioritizing vulnerability remediation, or generating security gate reports for CI/CD. Takes CVE scan results (JSON/SARIF from npm audit, pip-audit, Snyk), reachability analysis, and cutoff date as input. Combines severity, reachability, exploitability, and dependency criticality to rank CVEs by practical risk. Outputs markdown reports with concrete next-step guidance (immediate upgrade, monitor, ignore with justification, apply mitigation) suitable for issue trackers, security reviews, and CI security gates.

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Automatically analyzes configuration files to detect inconsistencies, conflicts, missing keys, and divergent values across environments, versions, or modules. Use when managing multi-environment configurations, detecting config drift, validating configuration changes, or ensuring consistency across microservices. Supports JSON, YAML, TOML, INI, XML, .env, and properties files. Identifies security issues like hardcoded secrets and provides actionable resolution guidance.

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Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.

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Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.

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Manages free AI models from OpenRouter for OpenClaw. Automatically ranks models by quality, configures fallbacks for rate-limit handling, and updates openclaw.json. Use when the user mentions free AI, OpenRouter, model switching, rate limits, or wants to reduce AI costs.

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Structured diagnostic code library for JavaScript/TypeScript. Turns errors and other conditions into typed, machine-readable `Diagnostic` instances with stable codes, docs URLs, and actionable fields. Use this skill whenever the project imports `nostics`, or works with `defineDiagnostics`/`defineProdDiagnostics`, the `Diagnostic` class, diagnostic code registries, or structured error handling. Also covers reporters (`createConsoleReporter`, `createFetchReporter` from nostics/reporters/fetch, `createFileReporter` from nostics/reporters/node, `createDevReporter` from nostics/reporters/dev), formatters (`formatDiagnostic`, `ansiFormatter`, `jsonFormatter`), and Vite plugins (`nosticsStrip` from @nostics/unplugin/strip-transform, `nosticsCollector` from @nostics/unplugin/dev-server-collector). Also use when migrating a library's existing `console.warn`/`console.error`/`warn()` helpers or thrown `Error`s to diagnostics: follow `references/migration.md`.

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Render Markdown, HTML, or PDF paid-advertising reports from a validated Claude Ads JSON run bundle. Use for ads report, client report, audit PDF, executive audience reporting, or exporting prior audit and plan results.

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把 CSV/Excel/JSON 数据转成漂亮的可视化报告页

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Build applications that programmatically interact with GitHub Copilot. The SDK wraps the Copilot CLI via JSON-RPC, providing session management, custom tools, hooks, MCP server integration, and streaming across Node.js, Python, Go, and .NET.

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IOTA blockchain MCP server providing wallet management with human-in-the-loop signing, Move smart contract build/test/publish, and on-chain queries via JSON-RPC and GraphQL. Use when interacting with IOTA network from AI coding agents, managing IOTA wallets, building or publishing Move packages, querying on-chain objects or transactions, or checking epoch and network statistics.

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TypeScript-first schema validation and type inference. Use for validating API requests/responses, form data, env vars, configs, defining type-safe schemas with runtime validation, transforming data, generating JSON Schema for OpenAPI/AI, or encountering missing validation errors, type inference issues, validation error handling problems. Zero dependencies, compact core (~5kb gzipped; zod/mini ~1.9kb).

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| Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

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Cloudflare Workers local development with Wrangler, Miniflare, hot reload, debugging. Use for project setup, wrangler.jsonc configuration, or encountering local dev, HMR, binding simulation errors.

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Use when choosing between engines or models for an agent pipeline and the answer must come from measurement on your own data, not from marketing pages: speech recognition for meeting recordings, the model that turns a transcript into notes, the critic model that checks it. Runs every variant in an isolated container, measures time, cost per hour of input (USD and RUB, with price source and date) and quality (WER/CER and course terms for ASR; code checks plus a judge of a different model for LLM steps), writes results.json/csv and one static analytics page. Triggers on "бенчмарк", "сравни движки", "сколько стоит час записи", "какую модель взять для пайплайна", "benchmark the pipeline", "compare ASR engines".

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Use when creating, searching, updating, or managing GitHub issues via CLI. Triggers: "issue", "create issue", "gh issue", "task tracking", "context", "handoff", "resume task", "session context", "save progress", "active tasks", "in-progress", "my tasks", "open issues". Covers: gh commands, bulk operations, JSON/jq, search filters, issue-to-PR workflow, AI session context storage, task workflow with labels.

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Робот агента Personal Corp. Агент делает себе голову робота из набора деталей, хранит её в штабе в stack/avatars.json и кладёт в штаб страницу avatar/robot.html, где робот стоит за партой своего штаба и вокруг видны отделы, без сервера и интернета. На уроке по ссылке входа с экрана урока робот входит в живой класс и горит маячком, пока агент работает. Триггеры /avatar, /avatar <ссылка входа>, сделай себе аватар, покажи своего робота, приди на урок.

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Run the weak-agent adversarial test harness against docx-cli. Spawns weak exercise agents (Haiku by default, Sonnet to probe, or a local agent harness's pre-produced runs) to perform real document tasks over six scenarios — five editing (MNDA form-fill + font fidelity, invoice table-edit/restructure + logo replace, résumé styling, contract redlining + commenting, contract finalize via accept/reject + comment reply/resolve) and one authoring (T. S. Eliot poetry journal: multi-column, verse, footnotes, links, figure) — renders every result with Word, has opus judge them against ground-truth rubrics, measures each exercise's tool economy, token cost, wall-clock, and correctness (from transcripts for Claude, the exercise.json ledger for the local harness), and synthesizes a prioritized ergonomics report. Use when the user says 'adversarial review', 'test docx-cli with weak agents', 'run the haiku harness', 'weak agent test', or wants to re-run yesterday's adversarial process.

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Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.

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Base template for Stage2 rebuttal refinement. Use when creating or extending conference-specific Stage2 skills so all venues share the same high-quality writing constraints, JSON output format, and normalization rules.

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当需要为 AI 定义工具接口、设计调用规范、实现工具发现与编排机制时调用此 skill。典型场景包括：设计 AI agent 的工具集、定义 JSON Schema/XML/TypeScript 格式的工具描述、实现工具权限控制与并行调度、设计子代理委托架构。 不适用于：定义 AI 身份（persona-design）、安全规则（safety-guardrails）、记忆存储（memory-system）。 关键 trigger 信号：AI 需要调用外部 API 或服务、工具数量超过 10 个需要发现机制、需要区分工具权限层级、需要子代理并行执行、工具调用涉及敏感操作需要审批。

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Design and validate model-facing tool definitions with clear names, action-oriented descriptions, bounded JSON Schema parameters, explicit side effects, safe defaults, idempotency, errors, and realistic tests. Use when creating function-calling tools, MCP tools, agent actions, structured tool inputs, or when a model selects the wrong tool, invents arguments, or causes unsafe side effects.

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Design Health — runs 19 deterministic quality checks on HTML output. Use when the user says /reimagine-it audit, "audit this page", "check design quality", or wants to verify craft-floor compliance before shipping. Catches blank plates, missing focus rings, non-compositor motion, off-palette accents, dead motion, fabricated content. No LLM, no API key. CI-ready with --json and exit codes. Part of the reimagine-it Content-Derived Design suite.

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Thesis report synthesis — turn a thesis's markdown artifacts into its letter-size thesis-report.html, then OPTIMIZE it against real page renders. Four-step loop — pack (deterministic Python bundle of all sources + report/metrics.json), author (the LLM writes report/content.html — narrative, KPI tiles, badges, timeline, tables and citations are LLM judgment), assemble (deterministic validation, template injection, Q50 pins, Q47 overflow gate), render + optimize (Chrome headless per-page PNGs — the LLM reads them and iterates until every page is visually clean). Spec 046 Q46–Q50.

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。