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nasiko-control-plane

使用实验性 Nasiko CLI 生命周期桥接,进行固定版本安装、只读状态查询和带明确同意的合格卸载,并遵守遥测和机密边界。

nasiko-control-plane 是什么?

该技能为 Nasiko CLI 提供了一个实验性的生命周期桥接,允许 ECC 在严格的合同约束下安全地安装、检查状态和卸载经审核的 Nasiko CLI 版本。它强制执行一系列安全合同,包括:所有安装操作必须获得用户明确同意(`--yes`),仅安装 ECC 审核的固定版本(当前为 `v0.1.0`),并在执行前通过 `--dry-run` 进行预览;状态查询(`status --json`)是只读的;卸载操作会检查所有权并与审核后的版本匹配。技能严格禁止使用未审核的引导脚本、在命令参数或日志中泄露机密,并且遥测功能必须单独选择启用。该桥接器仅覆盖安装、查看状态和卸载这些生命周期操作,其他如连接、认证、启动或关闭等动词应直接使用规范的 Nasiko CLI。技能会详细验证平台、架构、版本、清单、摘要、归档和二进制文件的完整性,任何验证失败都会停止执行,不会回退到 `latest` 版本,也不会搜索任意的 `PATH` 条目。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/nasiko-control-plane

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文档

Nasiko CLI 生命周期桥接

当用户明确要求 ECC 安装、检查或移除合格的 Nasiko CLI 时,使用此技能。此技能不操作 Nasiko 控制平面。

安全合同

  • 始终以 ecc nasiko status --json 开始。状态为只读。
  • 安装始终需要用户明确同意和 --yes。
  • 仅安装 ECC 审核的固定版本,当前为 v0.1.0。
  • 首先使用 ecc nasiko install --version v0.1.0 --dry-run --json 进行预览。
  • 仅在用户审阅版本、注册表来源、摘要和目标位置后,使用 ecc nasiko install --version v0.1.0 --yes --json 进行安装。
  • 仅使用 ecc nasiko uninstall --version v0.1.0 --yes --json 移除仍为合格的 ECC 管理的二进制文件。先使用 --dry-run 预览移除。
  • 合格来源为 https://github.com/Nasiko-Labs/nasiko,依据 Apache-2.0 许可证;工件和提取的二进制文件的 SHA-256 值已固定。
  • 切勿将合格命令替换为下载的 shell 或 PowerShell 引导脚本。
  • 切勿在命令参数、日志、技能输出、安装元数据或 ECC 状态中包含机密或凭据。
  • Nasiko 遥测以及与 Nasiko 或 Ito 的任何共享必须是选择启用并单独披露的。安装不代表同意遥测。

生命周期边界

初始 ECC 桥接支持合格安装、只读状态和所有权检查的卸载。直接使用规范的 Nasiko CLI 进行连接、认证、启动、部署或关闭,直到这些动词有经过验证的 ECC 合同。不要猜测 CLI 动词。

安装 CLI 并不证明控制平面服务器正在运行、代理受到治理、路由或 ACL 正常工作、可观测性完备、遥测已启用或 Ito 计算已连接。请分别报告每种状态。

失败行为

  • 如果平台、架构、版本、清单、摘要、归档、二进制文件或目标位置验证失败,则停止执行,不执行工件。
  • 不要回退到 latest。
  • 不要搜索任意 PATH 条目。使用 ECC 的合格位置或明确的绝对路径 ECC_NASIKO_CLI_EXECUTABLE 用于开发验证。
  • 不要将部分或不明确的安装视为成功。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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