io.github.affaan-m/ECC/ck

Persistent per-project memory for Claude Code. Auto-loads project context on session start, tracks sessions with git activity, and writes to native memory. Commands run deterministic Node.js scripts — behavior is consistent across model versions. Use when a project needs context to survive across Claude Code sessions instead of being re-explained each time.

io.github.affaan-m/ECC/context-budget

Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations. Use when the context window is filling up too fast and the agents, skills, MCP servers, or rules consuming it need to be identified.

io.github.affaan-m/ECC/autonomous-loops

Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems. Retained for compatibility only: when new autonomous loop guidance is needed, use continuous-agent-loop instead.

io.github.affaan-m/ECC/claude-devfleet

Orchestrate multi-agent coding tasks via Claude DevFleet — plan projects, dispatch parallel agents in isolated worktrees, monitor progress, and read structured reports. Use when dispatching parallel coding agents across isolated worktrees and tracking their reports.

io.github.affaan-m/ECC/dynamic-workflow-mode

Design task-local harnesses, eval gates, and reusable skill extraction for Claude dynamic workflow mode and other adaptive agent harnesses. Use when building a task-local harness, adding eval gates, or extracting a reusable skill from ad-hoc work.

io.github.affaan-m/ECC/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.

io.github.affaan-m/ECC/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.

io.github.affaan-m/ECC/security-scan

Scan your Claude Code configuration (.claude/ directory) for security vulnerabilities, misconfigurations, and injection risks using AgentShield. Checks CLAUDE.md, settings.json, MCP servers, hooks, and agent definitions. Use when auditing a .claude/ directory — CLAUDE.md, settings.json, MCP servers, hooks, or agent definitions.

io.github.ruvnet/ruflo/hooks-automation

Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

io.github.serverless/serverless/serverless-mcp

Host Model Context Protocol (MCP) servers on AWS with the Serverless Framework's built-in MCP support — an official-SDK server module deployed to AWS Lambda behind a streaming API Gateway REST endpoint, with gateway access control, OAuth protected-resource discovery, packaging and elicitation state handled for you. Use whenever the user wants to deploy, host, secure or debug an MCP server on AWS or Lambda, writes or edits an `mcp:` block in serverless.yml, exposes tools, resources or prompts to Claude or another AI client over HTTP, or hits an `MCP_*` configuration error (MCP_UNSUPPORTED_NODE_RUNTIME, MCP_FUNCTION_NAME_COLLISION, MCP_INVALID_STATE_ARN, and the rest). Trigger even when neither "Serverless Framework" nor "MCP" is named but the user describes putting their own tools in front of an AI client over an HTTPS endpoint.

io.github.rohitg00/ai-engineering-from-scratch/claude-certification

AI-native tutor and onboarding workflow for the four independent Claude certification tracks in AI Engineering from Scratch. Use when a learner wants to choose a Claude certification, prepare for CCAO-F, CCDV-F, CCAR-F, or CCAR-P, resume a certification path, learn the next lesson interactively, run and verify practical labs, build scored artifacts, take a diagnostic or mock exam, or remediate weak exam domains from GitHub with Claude Code, Codex, ChatGPT, Cursor, or another agent.

io.github.Jeffallan/claude-skills/devops-engineer

Creates Dockerfiles, configures CI/CD pipelines, writes Kubernetes manifests, and generates Terraform/Pulumi infrastructure templates. Handles deployment automation, GitOps configuration, incident response runbooks, and internal developer platform tooling. Use when setting up CI/CD pipelines, containerizing applications, managing infrastructure as code, deploying to Kubernetes clusters, configuring cloud platforms, automating releases, or responding to production incidents. Invoke for pipelines, Docker, Kubernetes, GitOps, Terraform, GitHub Actions, on-call, or platform engineering.

io.github.affaan-m/ECC/configure-ecc

Guide ECC installation, update, or reconfiguration from inside Claude Code, Codex, or Kimi while respecting each harness's real plugin, scope, and hook capabilities.

io.github.affaan-m/ECC/configure-ecc

在 Claude Code、Codex 或 Kimi 内引导 ECC 安装、更新或重新配置，同时严格遵守各家工具真实的插件、范围和 Hook 能力。

io.github.affaan-m/ECC/configure-ecc

Claude Code、Codex、Kimi 内で ECC のインストール、更新、再設定を案内し、各ハーネスが実際に備えるプラグイン、スコープ、フック機能を守ります。

io.github.CherryHQ/cherry-studio/claude-automation-recommender

Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.

io.github.luongnv89/claude-howto/refactor

基于 Martin Fowler 方法论的系统化代码重构 skill。适用于用户请求重构代码、改进代码结构、减少技术债、清理旧代码、消除 code smell 或提升可维护性时。这个 skill 采用分阶段、带研究与计划的安全增量实施方式。

io.github.luongnv89/claude-howto/refactor

Систематичний рефакторинг коду на основі методології Мартіна Фаулера. Використовуйте, коли користувачі просять рефакторити код, покращити структуру коду, зменшити технічний борг, очистити застарілий код, усунути запахи коду (code smells) або покращити супровідність коду. Ця навичка проводить через поетапний підхід з дослідженням, плануванням та безпечною інкрементальною реалізацією.

io.github.luongnv89/claude-howto/refactor

Systematic code refactoring based on Martin Fowler's methodology. Use when users ask to refactor code, improve code structure, reduce technical debt, clean up legacy code, eliminate code smells, or improve code maintainability. This skill guides through a phased approach with research, planning, and safe incremental implementation.

io.github.luongnv89/claude-howto/refactor

Refactor code có hệ thống dựa trên phương pháp luận của Martin Fowler. Sử dụng khi người dùng yêu cầu refactor code, cải thiện cấu trúc code, giảm nợ kỹ thuật, dọn code legacy, loại bỏ code smells, hoặc cải thiện khả năng duy trì code. Skill này hướng dẫn qua cách tiếp theo từng giai đoạn với nghiên cứu, lập kế hoạch, và triển khai tăng dần an toàn.

io.github.davila7/claude-code-templates/computer-use-agents

Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-source alternatives. Critical focus on sandboxing, security, and handling the unique challenges of vision-based control. Use when: computer use, desktop automation agent, screen control AI, vision-based agent, GUI automation.

io.github.alirezarezvani/claude-skills/senior-ml-engineer

ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost optimization. Use when the user asks about deploying ML models to production, setting up MLOps infrastructure (MLflow, Kubeflow, Kubernetes, Docker), monitoring model performance or drift, building RAG pipelines, or integrating LLM APIs with retry logic and cost controls. Focused on production and operational concerns rather than model research or initial training.

io.github.danielmiessler/LifeOS/Migrate

Intakes external content, classifies chunks against LifeOS taxonomy, commits with provenance. Sources: .md/.txt, stdin, LifeOS dirs, CLAUDE.md/Cursor/OpenAI Custom Instructions, Obsidian/Notion/Apple Notes exports. MigrateScan classifies → routing table. MigrateApprove with --approve-all/--approve-target/--review/--dry-run. Confidence ≥70% auto, 40-70% confirm, <40% walk-through. USE WHEN /migrate, migrate content, import from other LifeOS, bring in old notes, import Cursor rules, import CLAUDE.md, bulk import, Obsidian/Notion/Apple Notes import. NOT FOR single-file edits, conversational interviews, identity edits.

io.github.openclaw/openclaw/coding-agent

Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.