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research-ops

以證據為優先的現狀研究工作流程,適用於 ECC。當使用者需要最新事實、比較、資訊補充或根據當前公開證據與提供的本地脈絡所做出的建議時使用。

research-ops 是什麼?

Research Ops 是圍繞 ECC 儲存庫研究工作流程的操作包裝器。它並非取代 `deep-research`、`exa-search` 或 `market-research` 等特定技能,而是指導何時以及如何組合使用它們。此技能的核心是確保研究行動始終以證據為優先:它會從使用者提供的材料開始,將其標準化為已確證的事實、待驗證的事項與待解決的問題,並在整個過程中明確區分來源事實、使用者提供的脈絡、推論與建議。支援的平台包括 ECC 生態系統,透過與 `exa-search`、`deep-research`、`market-research`、`lead-intelligence` 及 `knowledge-ops` 等技能協作,來處理快速事實查詢、比較與決策備忘錄、潛在客戶資訊補充以及重複監控任務。此技能對於需要新鮮公開資訊來回答問題、進行選項比較或將重複查詢轉變為持續監控工作流程的情境特別有用。它內建了防範機制,避免從過時的記憶回答目前問題、將推論混入來源事實中,以及忽略使用者提供的證據。

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

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

研究營運 (Research Ops)

當使用者要求研究當前資訊、比較選項、補充人員或公司資料,或將重複查詢轉變為受監控的工作流程時,請使用此技能。

這是圍繞儲存庫研究工作流程的操作包裝器。它並非取代 deep-research、exa-search 或 market-research;它會告訴您何時以及如何組合使用它們。

技能堆疊 (Skill Stack)

在相關時機,將這些 ECC 原生技能拉入工作流程:

  • exa-search:用於快速發現最新網路資訊
  • deep-research:用於多來源綜合分析並附上引用
  • market-research:當最終結果應為建議或排名決策時使用
  • lead-intelligence:當任務重點是針對人員/公司而非一般性研究時使用
  • knowledge-ops:當結果應儲存於持久性脈絡中以供後續使用時使用

使用時機 (When to Use)

  • 使用者說「研究」、「查詢」、「比較」、「我該跟誰談」或「最新情況是什麼」
  • 答案取決於當前的公開資訊
  • 使用者已提供證據,並希望將其納入新的建議中
  • 該任務可能重複發生,應轉變為監控 (monitor) 而非一次性查詢

防護措施 (Guardrails)

  • 若可輕鬆進行新鮮搜尋,請勿以過時的記憶回答目前問題
  • 區分以下項目:
    • 來源事實 (sourced fact)
    • 使用者提供的證據 (user-provided evidence)
    • 推論 (inference)
    • 建議 (recommendation)
  • 若答案已存在於本地程式碼或文件中,請勿啟動耗時的研究程序

工作流程 (Workflow)

1. 從使用者已提供的內容開始

將任何提供的資料標準化為:

  • 已確證的事實
  • 需要驗證的事項
  • 待解決的問題

如果使用者已建立部分模型,請勿從零開始重新分析。

2. 分類請求

在搜尋之前,選擇正確的處理路線:

  • 快速事實查詢
  • 比較或決策備忘錄
  • 潛在客戶/資訊補充
  • 重複監控候選事項

3. 首先採用最輕量的有用證據路徑

  • 使用 exa-search 進行快速發現
  • 當需要綜合分析或多個來源時,升級至 deep-research
  • 當結果應以建議結束時,使用 market-research
  • 當真正的問題是目標排名或溫和路徑發現時,交給 lead-intelligence

4. 報告時明確標示證據邊界

對於重要主張,說明其屬於:

  • 來源事實
  • 使用者提供的脈絡
  • 推論
  • 建議

對時效性敏感的答案應包含具體日期。

5. 決定任務是否應保持手動

如果使用者可能重複詢問相同的研究問題,請明確指出,並建議採用監控或工作流程層,而不是永遠重複相同的手動搜尋。

輸出格式 (Output Format)

問題類型
- 事實性 / 比較 / 資訊補充 / 監控

證據
- 來源事實
- 使用者提供的脈絡

推論
- 從證據得出的結論

建議
- 答案或後續行動
- 這是否應成為監控事項

常見陷阱 (Pitfalls)

  • 請勿將推論混入來源事實中而不標明
  • 請勿忽略使用者提供的證據
  • 若問題可由本地儲存庫脈絡解答,請勿使用耗時的研究路線
  • 請勿提供對時效性敏感的答案而沒有日期

驗證 (Verification)

  • 重要主張已按證據類型標示
  • 對時效性敏感的輸出包含日期
  • 最終建議與實際使用的研究模式相符

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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