affaan-m/ECC agent skills, page 9

Deterministic method for placing signature, date, and text fields in a web e-signature composer through a browser automation session, using a fixed signature page, numeric Location panel coordinates…

affaan-m/ECC

为 OpenClaw AI Agent 锻造完整的龙虾灵魂方案。根据用户偏好或随机抽卡， 输出身份定位、灵魂描述(SOUL.md)、角色化底线规则、名字和头像生图提示词。

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Operator approval contract with internal filing notices for agent-drafted outbound messages, hashed drafts, epoch-keyed decisions, durable delivery claims and receipts, and a pre-draft baseline gate.

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克劳德代码会话的正式评估框架，实施评估驱动开发（EDD）原则

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建议在逻辑间隔处手动压缩上下文，以在任务阶段中保留上下文，而非任意的自动压缩。

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Claude Code 会话的全面验证系统。

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用于构建健壮、高效且可维护的Go应用程序的惯用Go模式、最佳实践和约定。

affaan-m/ECC

将签证申请文件（图片）翻译成英文，并创建包含原文和译文的双语PDF

affaan-m/ECC

React、Next.js、状态管理、性能优化和UI最佳实践的前端开发模式。

affaan-m/ECC

DRF ile Django mimari desenleri, REST API tasarımı, ORM en iyi uygulamaları, caching, signal'ler, middleware ve production-grade Django uygulamaları.

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构建一个全自动化的AI驱动数据收集代理，适用于任何公共来源——招聘网站、价格信息、新闻、GitHub、体育赛事等任何内容。按计划进行抓取，使用免费LLM（Gemini Flash）丰富数据，将结果存储在Notion/Sheets/Supabase中，并从用户反馈中学习。完全免费在GitHub Actions上运行。适用于用户希望自动监控、收集或跟踪任何公共数据的场景。

affaan-m/ECC

Next.js 16+ 和 Turbopack — 增量打包、文件系统缓存、开发速度，以及何时使用 Turbopack 与 webpack。

affaan-m/ECC

Expert guidance for developing with the tinystruct Java framework.

affaan-m/ECC

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

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Stop hook that blocks Claude from finishing until quality checks pass.

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Generate a design system from an existing codebase or audit one for visual consistency: extract tokens (colors, typography, spacing, shadows) into design-tokens.json and CSS custom properties with…

affaan-m/ECC

PreToolUse fact-forcing gate that denies the first Edit/Write/Bash (including MultiEdit) attempt until the agent presents concrete facts (importers, data schemas, verbatim user instruction), then…

affaan-m/ECC

Go-specific design patterns and best practices including functional options, small interfaces, dependency injection, concurrency patterns, error handling, and package organization.

affaan-m/ECC

Go testing best practices including table-driven tests, test helpers, benchmarking, race detection, coverage analysis, and integration testing patterns.

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Read a plan document, decompose it into steps, classify each step, and emit the ready-to-paste ECC slash command for that step, carrying the step identifier and scope in the command's own documented…

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Python-specific design patterns and best practices including protocols, dataclasses, context managers, decorators, async/await, type hints, and package organization.

affaan-m/ECC

Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization.

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Ruby on Rails framework patterns for Rails 7.1+ and 8.x apps.

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Research-before-coding workflow. An agent skill from affaan-m/ECC.

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通过 Context7 MCP 使用最新的库和框架文档，而非训练数据。当用户提出设置问题、API参考、代码示例或命名框架（例如 React、Next.js、Prisma）时激活。

affaan-m/ECC

Shared orchestration engine behind the orch- skill family — the gated Research-Plan-TDD-Review-Commit pipeline, size classifier, agent and command map, and two human gates (plan approval, commit…

affaan-m/ECC

Review prediction-market, basket, oracle, and trading-agent workflows for compliance, safety, data-quality, privacy, and execution risk.

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Eval-driven development (EDD) framework for AI coding sessions — define capability and regression evals before coding, grade with code-based, model-based, rule, or human graders, and track pass@k…

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Security hardening guidance for AI agent frameworks that process untrusted content, invoke tools, write workspace files, manage runtime identifiers, or handle credentials.

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Reviews staged changes and drafts a Conventional Commits-compliant commit message with the right type, scope, and length.

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Create and configure hookify rules — markdown files with YAML frontmatter that match bash, file, prompt, or stop events by regex or conditions and show warn/block messages to the agent.

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Statically review MCP configuration for mutable package references before approval or CI, without executing discovered MCP servers.

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MyBatis and MyBatis-Spring patterns for mapper design, XML and annotation SQL, result mapping, dynamic SQL safety, transactions, batching, pagination, and query performance.

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Optimize LLM prompt caching hit rate to reduce API costs and improve latency.

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Render a token-usage stat card from local Claude Code, Codex, Gemini CLI, and OpenCode session logs and commit it into the repository as a file.

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Claude Code 세션을 위한 포괄적인 검증 시스템.

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テスト駆動開発とGoコードの高品質を保証するための包括的なテスト戦略。

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tinystruct Java フレームワークで開発する際の専門ガイダンス。tinystruct コードベース、または tinystruct 上に構築されたあらゆるプロジェクトで作業する際に使用します — プロジェクトに存在しない場合の bin/dispatcher・bin/dispatcher.cmd 起動スクリプトの生成、Application クラスの作成、@Action…

affaan-m/ECC

操作并扩展NanoClaw v2，这是ECC基于claude -p构建的零依赖会话感知REPL. An agent skill from affaan-m/ECC.

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Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets.

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Quarkus 3.x LTS architecture patterns with Camel for messaging, RESTful API design, CDI services, data access with Panache, and async processing.

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Pi-hole installation, blocklist management, DNS-over-HTTPS setup, DHCP integration, local DNS records, and troubleshooting broken DNS resolution on a home network.

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A skill your agent uses when retrieving Jira tickets, analyzing requirements, updating ticket status, adding comments, or transitioning issues.

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Sparse-clue OSINT investigation methodology for extracting overlooked leads, connecting fragmented evidence, and testing explanations across sources.

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Rules and examples for safe, reversible schema changes in production: zero-downtime column and index changes, large data backfills and ORM migration workflows.

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Sets up eval-driven development for Claude Code workflows: capability and regression evals, three grader types and pass@k reliability metrics.

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Exa MCPによるウェブ、コード、企業調査のためのニューラル検索。ユーザーがウェブ検索、コード例、企業情報、人物検索、またはExaのニューラル検索エンジンを使ったAI駆動の詳細調査を必要とする場合に使用します。

affaan-m/ECC

fal.ai MCPによる統合メディア生成（画像、動画、音声）。テキストから画像（Nano Banana）、テキスト/画像から動画（Seedance、Kling、Veo 3）、テキストから音声（CSM-1B）、動画から音声（ThinkSound）をカバーします。ユーザーがAIで画像、動画、音声を生成したい場合に使用します。

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