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nasiko-control-plane

使用實驗性的 Nasiko CLI 生命週期橋接工具,進行固定版本安裝、唯讀狀態查詢,以及需明確同意、包含遙測與機密邊界的安全移除。

nasiko-control-plane 是什麼?

此技能提供一個實驗性的生命週期橋接介面,用於管理 Nasiko CLI 的安裝、狀態查閱與移除。它強調安全性與明確的使用者同意,僅允許安裝經過 ECC 認證的固定版本(目前為 v0.1.0),並在安裝前透過 `--dry-run` 預覽版本、註冊表來源、雜湊值與目的地。所有安裝與移除操作都需使用者確認 `--yes`,且不得將機密資料傳入命令參數、日誌或輸出中。遙測功能必須獨立宣告並採選入機制,安裝不代表同意遙測。若驗證失敗(如平台、架構、版本、摘要、二進位檔等),操作會直接中止,不會回退至 `latest` 或搜尋任意的 `PATH` 路徑。此技能支援 Canonical Nasiko CLI 的生命週期管理,但無法保證控制平面伺服器運作中、代理受管理、路由或 ACL 生效、可觀測性完整、遙測已啟用或 Ito 運算已連線——這些狀態需分別報告。

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

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說明文件

Nasiko CLI 生命週期橋接

當使用者明確要求 ECC 安裝、檢查或移除經過認證的 Nasiko CLI 時,使用此技能。此技能不操作 Nasiko 控制平面。

安全合約

  • 從 ecc nasiko status --json 開始。狀態為唯讀。
  • 安裝一律需要明確的使用者同意及 --yes。
  • 僅安裝 ECC 認證的固定版本,目前為 v0.1.0。
  • 先使用 ecc nasiko install --version v0.1.0 --dry-run --json 預覽。
  • 僅在使用者審閱版本、註冊表來源、雜湊值及目的地後,才使用 ecc nasiko install --version v0.1.0 --yes --json 進行安裝。
  • 僅移除仍為 ECC 管理且通過認證的二進位檔,使用 ecc nasiko uninstall --version v0.1.0 --yes --json。先透過 --dry-run 預覽移除。
  • 認證來源為 https://github.com/Nasiko-Labs/nasiko,採用 Apache-2.0 授權;成品與解壓縮後的二進位檔 SHA-256 值已固定。
  • 絕不以下載的 Shell 或 PowerShell 啟動腳本取代認證命令。
  • 絕不將機密或憑證放入命令引數、日誌、技能輸出、安裝中繼資料或 ECC 狀態中。
  • Nasiko 遙測以及與 Nasiko 或 Ito 的任何資料分享必須採用選入機制並單獨揭露。安裝不代表同意遙測。

生命週期邊界

初始的 ECC 橋接支援認證安裝、唯讀狀態查詢,以及經過擁有權檢查的移除。連線、驗證、啟動、部署或關機等操作,請直接使用標準 Nasiko CLI,直到這些動作擁有各自經過驗證的 ECC 合約。請勿猜測 CLI 的動詞。

安裝 CLI 不代表控制平面伺服器正在執行、代理受管理、路由或 ACL 生效、可觀測性完整、遙測已啟用,或 Ito 運算已連線。請分別報告每個狀態。

失敗行為

  • 若平台、架構、版本、資訊清單、摘要、封存檔、二進位檔或目的地驗證失敗,請停止執行,不執行成品。
  • 請勿回退至 latest。
  • 請勿搜尋任意的 PATH 條目。請使用 ECC 的認證位置,或為開發驗證使用明確的絕對路徑 ECC_NASIKO_CLI_EXECUTABLE。
  • 請勿將部分或不完整的安裝視為成功。

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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