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This is the MistEye security gate skill. It is triggered by pre-installation risk checks (including Skill/MCP dependency manifests), pre-access security checks for domains or URLs, IoC malicious detection, and supply-chain risk blocking, especially for common requests such as "take a look at this address/website." It must use `https://app-api.misteye.io/functions/v1/detect`; in dependency and external-link scenarios it has the highest priority. If the API returns `status="malicious"` or any `matches`, the action must be blocked and explicitly reported as "blocked". Only after the first installation is complete should the user be reminded that daily patrols can be enabled with OpenClaw or Hermes (default once per day).

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Design, implement, or review dygo HTTP APIs and public App SDK contracts. Use for Record APIs, metadata and boot endpoints, query behavior, response envelopes, compatibility, and public Go interfaces.

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Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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Write unit and integration tests for Angular v20+ applications using Vitest or Jasmine with TestBed and modern testing patterns. Use for testing components with signals, OnPush change detection, services with inject(), and HTTP interactions. Triggers on test creation, testing signal-based components, mocking dependencies, or setting up test infrastructure. Don't use for E2E testing with Cypress or Playwright, or for testing non-Angular JavaScript/TypeScript code.

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Auto-activate for litestar_granian, GranianPlugin, litestar run Granian options, runtime threads, HTTP/2, TLS, access logs, metrics, static mounts, or worker lifecycle. Not for non-Granian servers.

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Auto-activate for Litestar, Controller, Router, @get/@post, FromPath, MsgspecDTO, OpenAPIConfig, Provide, FromDishka, Guard, ASGIMiddleware, HTTPException, ChannelsPlugin, or WebSocket. Not for standalone libs.

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Apply these opinionated C# conventions whenever writing or reviewing C# 12 / .NET 8+ code: nullable enabled with warnings as errors, records and primary constructors, pattern matching over cast chains, the Result pattern for expected failures, async all the way with CancellationToken, IOptions config, IHttpClientFactory, and never returning EF entities from an API.

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Docs 사이트 (`site/` Next.js) 의 본문/JSX/markdown 링크 audit. broken 내부 링크 탐지, build-time copy 인지, 옵트인 HTTP probe. 신규 페이지 추가 / slug 이전 / chapter 삭제 후 회귀 방지. Triggered by "broken link", "404", "docs link", "hyperlink", "링크 점검", "링크 깨짐", "audit links", "link checker" 키워드.

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Production-grade error handling for Node.js/TypeScript backends. Error classes, HTTP mapping, logging, retry strategies.

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profiling 数据统一采集：远端昇腾采集 NPU 性能数据回传本地，供 profiling-analyze 分析。 两条入口——mindiesd 自家脚本入口（collect_profile.py）与三方框架入口 （collect_patch_template.py 补丁 + torchrun，含 vLLM-Omni HTTP 服务等分场景）； warmup 在 profiler 外（默认 5 步，compile ≥10）。 只要用户想"开 profiler/采 profile/拿算子级数据"，自家脚本或 vllm、LightX2V 等框架都先 用本入口；分析/瓶颈定位/优化选型/并行/基准请求分别属 profiling-analyze、 dit-perf-opt、dit-parallel-opt、benchmark-dev，本入口不承接。 由 model-auto-optimization 的 S1（融合分析）阶段调用，亦由 profiling-analyze 与 dev-workflow 的 采集场景指引加载。

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共享 Ascend NPU 上的**性能验收标准**：判定"某优化到底有没有效、有多少效"，并规定 **只有验收态结果才能写入总览表**。分双态：**探索态**（平时特性分析：不展开、 允许少步/单次，产物必须标 `[探索]`、不得进总览表）vs **验收态**（入库：同窗 ≥N 复现、按 window-ab-protocol 口径出数）。当用户问"这个优化有多少收益 / 是否有效 / 要不要采纳 / 为什么变慢了""A-B 一下""同窗对比""测 3 次取平均""把数据对齐到同一窗口" "数字对不对""是不是我眼花了"，或需要在多卡共享机器上给出性能结论、刷新优化总览报表、 复核别人给的加速比时使用；即使用户只说"帮我确认下这个改动值不值"也应触发。 核心口径：同窗 A/B 是唯一权威（跨窗口绝对值不可比）、A/B/A 漂移校正、窗口内自带基线、 三次证明（HTTP 200 + 字节数 + 日志行）、产物 md5 与关键张量 dump 双证据、噪声地板内 不下结论、profiling 三张表统一到同一窗口；并覆盖 md5 门禁的盲区（忠实度对照）、 共享机器纪律与门控回退纪律。

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Serve a markdown file/directory as a calm, book-like reader via a local HTTP server (127.0.0.1 only); good for long-form docs like RFCs and plans. Supports Mermaid, dark/light mode, and a nav sidebar.

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Angular v20+ patterns: components, signals, services, forms, routing, HTTP, testing, SSR. Auto-activates on .ts files in Angular projects (angular.json detected).

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Serve any markdown file or directory as a calm, book-like reader via a local HTTP server (127.0.0.1 only). Best for long-form documents — RFCs, runbooks, design docs, plan files — where comfortable in-browser reading matters. Supports Mermaid diagrams, dark/light mode, plan navigation sidebar, and keyboard shortcuts. Requires explicit start and stop lifecycle.

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Use when working with Angular code — components, signals, services, forms, routing, HTTP, testing, SSR, or tooling. Targets Angular v20+ modern patterns. Auto-activates on .ts files in Angular projects (detected via angular.json).

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Use when transforming existing code into agent-consumable surfaces — CLI (npm-publishable), MCP server (stdio/SSE/Streamable HTTP), and a companion skill. Triggers on 'agentize', 'henshin', 'expose as MCP', 'wrap as CLI', 'publish to npm', 'make LLM-accessible', 'turn into agent tool', 'expose feature as tool'. Runs when the user has existing code and wants to ship it to agents. NOT for building new code from scratch (see mk:bootstrap); NOT for reviewing or verifying existing code (see mk:review).

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Local-only regression / benchmark skill for ui-clone-skills maintainers. Drives the standard ui-reverse-engineering pipeline against the canonical reference site (https://realfood.gov) and records AE/SSIM, iteration count, gate fail counts, and outcome to benchmark/history.csv so prompt / sub-doc / model-version drift surfaces as a trend. Trigger phrases: "run benchmark" / "regression benchmark" / "benchmark clone". The Makefile no longer has a `benchmark` target — setup is inline bash in this skill (Step 1 below). Internal: NOT registered in `.claude-plugin/plugin.json` `skills`. Not part of the public 3-skill marketplace surface. Maintainer tooling only.

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AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.

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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

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。

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. Credits: Original skill by @blader - https://github.com/blader/humanizer

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。

Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.

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n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.

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End-to-end smoke against the local rembric dev server (`pnpm run dev`, host Next.js dev on :3000). Apply when the user says "smoke", "probar contra docker", "dev:up", or after applying an OpenSpec change that touches HTTP (`apps/web/src/app/api/`), MCP tools (`packages/mcp/src/`), or DB migrations (`packages/db/src/migrations/`). Encodes bring-up, readiness verification, probe pattern and teardown — not the probes themselves.

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Use when CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry pipelines.

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。