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資料吞吐量加速器

當大量資料攝取、回填、匯出、ETL、倉儲載入、清單追趕或資料表同步需要大幅提升速度,同時確保資料正確性時使用。

資料吞吐量加速器 是什麼?

此技能專注於加速大規模資料移動、轉換或儲存的瓶頸,目標不僅是提升速度,更強調在正確的位置以可驗證的方式完成資料落地。它協助開發者與 AI 代理區分來源提取、網路傳輸、倉儲載入、轉換及服務資料表新鮮度等不同階段的效能問題,避免因即時資料增長速度超過追趕窗口而造成的延遲。技能支援多種平台(如 CLI、排程任務、工作流程或 runbook),透過將運算移近資料、利用倉儲原生掃描與合併操作、使用清單或檢查點跳過已完成檔案、進行分區與叢集化以符合讀取與附加模式、批次處理小型任務,以及透過唯一鍵或可取代暫存區確保寫入操作具冪等性等啟發式策略來實現加速。工作流程包含評估當前合約與 backlog、執行安全追趕或基準測試、比較不同變體並選擇最快且維持計數與時間戳一致性的路徑,最後將其標準化為可重複執行的程式碼。輸出需提供硬性會計區塊(如來源檔案數、處理檔案數、新增列數、執行時間等),並明確要求正確性閘門(如清單計數與資料表最大時間戳一致)。同時設有嚴格防護:不得為美化指標刪除原始資料、不得靜默跳過失敗檔案、不得混雜歷史回填狀態與即時資料新鮮度,以及對於金融、醫療、法規或影響客戶的資料須保留重播證據與核准關卡。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

資料吞吐量加速器 是做什麼的?

當瓶頸在於移動、轉換或儲存大量資料時,請使用此技能。目標不僅是速度,而是在正確的位置以更快的速度讓資料正確落地,並提供證明。

首要區分

在最佳化之前,請先區分以下環節:

  • 來源提取速度;
  • 網路傳輸速度;
  • 倉儲/載入速度;
  • 轉換速度;
  • 服務資料表的新鮮度;
  • 作業執行期間的即時資料增長。

管線可能「很快」,但如果新資料的到達速度超過最終追趕窗口,它仍然可能看起來落後。

快速路徑啟發式策略

  • 將運算移到資料所在的位置。
  • 對於大型落地檔案,偏好使用倉儲原生的掃描、合併與附加操作。
  • 使用清單或檢查點,以便跳過已完成的檔案/分割區。
  • 使用符合讀取與附加模式的分區和叢集化。
  • 批次處理小型檔案、請求和寫入操作。
  • 透過唯一鍵、清單或可取代的暫存區,使寫入操作具有幂等性。
  • 分別核算原始資料、衍生資料和服務資料表。

工作流程

  1. 讀取目前的來源、目標和清單合約。
  2. 測量 backlog:外部檔案、清單列數、原始列數、衍生列數、最小/最大時間戳記以及未處理計數。
  3. 執行安全的追趕或樣本基準測試。
  4. 比較變體:批次大小、工作者數量、倉儲 SQL、檔案分組、暫存區形狀和清單更新方法。
  5. 僅推廣能保持計數和時間戳記一致的最快路徑。
  6. 將該路徑編碼為 CLI、排程作業、工作流程或 runbook。
  7. 在編碼後的路徑執行完畢後,重新執行最終核算。

核算輸出

使用一個硬性核算區塊:

資料吞吐量結果:
- 發現的來源檔案數:294
- 本次處理的檔案數:294
- 新增的原始列數:9,683,598
- 新增的衍生列數:8,917,585
- 回讀時的剩餘尾端:24 個檔案
- 執行時間:38.7 秒
- 正確性閘門:清單計數與資料表最大時間戳記相符

防護措施

  • 請勿為了讓指標更好看而刪除原始資料。
  • 請勿靜默跳過失敗的檔案。
  • 請勿將歷史回填狀態與即時資料新鮮度混為一談。
  • 在目標資料表與清單達成一致之前,請勿宣稱管線已完成。
  • 對於金融、醫療保健、法規或影響客戶的資料,請保留重播證據和核准關卡。

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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