diff --git a/.claude-plugin/marketplace.json b/.claude-plugin/marketplace.json index a6210b24..abce34bf 100644 --- a/.claude-plugin/marketplace.json +++ b/.claude-plugin/marketplace.json @@ -11,7 +11,7 @@ { "name": "ecc", "source": "./", - "description": "Harness-native ECC operator layer - 64 agents, 261 skills, 84 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses", + "description": "Harness-native ECC operator layer - 64 agents, 262 skills, 84 legacy command shims, reusable hooks, rules, selective install profiles, and production-ready workflows for Claude Code, Codex, OpenCode, Cursor, and related agent harnesses", "version": "2.0.0-rc.1", "author": { "name": "Affaan Mustafa", diff --git a/.claude-plugin/plugin.json b/.claude-plugin/plugin.json index be1a95cf..0d2b5465 100644 --- a/.claude-plugin/plugin.json +++ b/.claude-plugin/plugin.json @@ -1,7 +1,7 @@ { "name": "ecc", "version": "2.0.0-rc.1", - "description": "Harness-native ECC plugin for engineering teams - 64 agents, 261 skills, 84 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses", + "description": "Harness-native ECC plugin for engineering teams - 64 agents, 262 skills, 84 legacy command shims, reusable hooks, rules, MCP conventions, and operator workflows for Claude Code plus adjacent agent harnesses", "author": { "name": "Affaan Mustafa", "url": "https://x.com/affaanmustafa" diff --git a/AGENTS.md b/AGENTS.md index f791d13a..c890bd9e 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -1,6 +1,6 @@ # Everything Claude Code (ECC) — Agent Instructions -This is a **production-ready AI coding plugin** providing 64 specialized agents, 261 skills, 84 commands, and automated hook workflows for software development. +This is a **production-ready AI coding plugin** providing 64 specialized agents, 262 skills, 84 commands, and automated hook workflows for software development. **Version:** 2.0.0-rc.1 @@ -150,7 +150,7 @@ Troubleshoot failures: check test isolation → verify mocks → fix implementat ``` agents/ — 64 specialized subagents -skills/ — 261 workflow skills and domain knowledge +skills/ — 262 workflow skills and domain knowledge commands/ — 84 slash commands hooks/ — Trigger-based automations rules/ — Always-follow guidelines (common + per-language) diff --git a/README.md b/README.md index 0b924a0f..ed2537ef 100644 --- a/README.md +++ b/README.md @@ -123,7 +123,7 @@ This repo is the raw code only. The guides explain everything. ### v2.0.0-rc.1 — Surface Refresh, Operator Workflows, and ECC 2.0 Alpha (Apr 2026) - **Dashboard GUI** — New Tkinter-based desktop application (`ecc_dashboard.py` or `npm run dashboard`) with dark/light theme toggle, font customization, and project logo in header and taskbar. -- **Public surface synced to the live repo** — metadata, catalog counts, plugin manifests, and install-facing docs now match the actual OSS surface: 64 agents, 261 skills, and 84 legacy command shims. +- **Public surface synced to the live repo** — metadata, catalog counts, plugin manifests, and install-facing docs now match the actual OSS surface: 64 agents, 262 skills, and 84 legacy command shims. - **Operator and outbound workflow expansion** — `brand-voice`, `social-graph-ranker`, `connections-optimizer`, `customer-billing-ops`, `ecc-tools-cost-audit`, `google-workspace-ops`, `project-flow-ops`, and `workspace-surface-audit` round out the operator lane. - **Media and launch tooling** — `manim-video`, `remotion-video-creation`, and upgraded social publishing surfaces make technical explainers and launch content part of the same system. - **Framework and product surface growth** — `nestjs-patterns`, richer Codex/OpenCode install surfaces, and expanded cross-harness packaging keep the repo usable beyond Claude Code alone. @@ -394,7 +394,7 @@ If you stacked methods, clean up in this order: /plugin list ecc@ecc ``` -**That's it!** You now have access to 64 agents, 261 skills, and 84 legacy command shims. +**That's it!** You now have access to 64 agents, 262 skills, and 84 legacy command shims. ### Dashboard GUI @@ -1473,7 +1473,7 @@ The configuration is automatically detected from `.opencode/opencode.json`. |---------|---------------------|----------|--------| | Agents | PASS: 64 agents | PASS: 12 agents | **Claude Code leads** | | Commands | PASS: 84 commands | PASS: 35 commands | **Claude Code leads** | -| Skills | PASS: 261 skills | PASS: 37 skills | **Claude Code leads** | +| Skills | PASS: 262 skills | PASS: 37 skills | **Claude Code leads** | | Hooks | PASS: 8 event types | PASS: 11 events | **OpenCode has more!** | | Rules | PASS: 29 rules | PASS: 13 instructions | **Claude Code leads** | | MCP Servers | PASS: 14 servers | PASS: Full | **Full parity** | @@ -1635,7 +1635,7 @@ ECC is the **first plugin to maximize every major AI coding tool**. Here's how e |---------|-----------------------|------------|-----------|----------|----------------| | **Agents** | 64 | Shared (AGENTS.md) | Shared (AGENTS.md) | 12 | N/A | | **Commands** | 84 | Shared | Instruction-based | 35 | 6 prompts | -| **Skills** | 261 | Shared | 10 (native format) | 37 | Via instructions | +| **Skills** | 262 | Shared | 10 (native format) | 37 | Via instructions | | **Hook Events** | 8 types | 15 types | None yet | 11 types | None | | **Hook Scripts** | 20+ scripts | 16 scripts (DRY adapter) | N/A | Plugin hooks | N/A | | **Rules** | 34 (common + lang) | 34 (YAML frontmatter) | Instruction-based | 13 instructions | 1 always-on file | diff --git a/README.zh-CN.md b/README.zh-CN.md index d532eb9b..239cc0b9 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -160,7 +160,7 @@ Copy-Item -Recurse rules/typescript "$HOME/.claude/rules/" /plugin list ecc@ecc ``` -**完成！** 你现在可以使用 64 个代理、261 个技能和 84 个命令。 +**完成！** 你现在可以使用 64 个代理、262 个技能和 84 个命令。 ### multi-* 命令需要额外配置 diff --git a/docs/zh-CN/AGENTS.md b/docs/zh-CN/AGENTS.md index 54f7f93b..20e224ca 100644 --- a/docs/zh-CN/AGENTS.md +++ b/docs/zh-CN/AGENTS.md @@ -1,6 +1,6 @@ # Everything Claude Code (ECC) — 智能体指令 -这是一个**生产就绪的 AI 编码插件**，提供 64 个专业代理、261 项技能、84 条命令以及自动化钩子工作流，用于软件开发。 +这是一个**生产就绪的 AI 编码插件**，提供 64 个专业代理、262 项技能、84 条命令以及自动化钩子工作流，用于软件开发。 **版本:** 2.0.0-rc.1 @@ -147,7 +147,7 @@ ``` agents/ — 64 个专业子代理 -skills/ — 261 个工作流技能和领域知识 +skills/ — 262 个工作流技能和领域知识 commands/ — 84 个斜杠命令 hooks/ — 基于触发的自动化 rules/ — 始终遵循的指导方针（通用 + 每种语言） diff --git a/docs/zh-CN/README.md b/docs/zh-CN/README.md index a0a3fcfa..1d037495 100644 --- a/docs/zh-CN/README.md +++ b/docs/zh-CN/README.md @@ -224,7 +224,7 @@ Copy-Item -Recurse rules/typescript "$HOME/.claude/rules/" /plugin list ecc@ecc ``` -**搞定！** 你现在可以使用 64 个智能体、261 项技能和 84 个命令了。 +**搞定！** 你现在可以使用 64 个智能体、262 项技能和 84 个命令了。 *** @@ -1138,7 +1138,7 @@ opencode |---------|---------------|----------|--------| | 智能体 | PASS: 64 个 | PASS: 12 个 | **Claude Code 领先** | | 命令 | PASS: 84 个 | PASS: 35 个 | **Claude Code 领先** | -| 技能 | PASS: 261 项 | PASS: 37 项 | **Claude Code 领先** | +| 技能 | PASS: 262 项 | PASS: 37 项 | **Claude Code 领先** | | 钩子 | PASS: 8 种事件类型 | PASS: 11 种事件 | **OpenCode 更多！** | | 规则 | PASS: 29 条 | PASS: 13 条指令 | **Claude Code 领先** | | MCP 服务器 | PASS: 14 个 | PASS: 完整 | **完全对等** | @@ -1246,7 +1246,7 @@ ECC 是**第一个最大化利用每个主要 AI 编码工具的插件**。以 |---------|-----------------------|------------|-----------|----------| | **智能体** | 64 | 共享 (AGENTS.md) | 共享 (AGENTS.md) | 12 | | **命令** | 84 | 共享 | 基于指令 | 35 | -| **技能** | 261 | 共享 | 10 (原生格式) | 37 | +| **技能** | 262 | 共享 | 10 (原生格式) | 37 | | **钩子事件** | 8 种类型 | 15 种类型 | 暂无 | 11 种类型 | | **钩子脚本** | 20+ 个脚本 | 16 个脚本 (DRY 适配器) | N/A | 插件钩子 | | **规则** | 34 (通用 + 语言) | 34 (YAML 前页) | 基于指令 | 13 条指令 | diff --git a/package.json b/package.json index bddfb7f8..9ff4b937 100644 --- a/package.json +++ b/package.json @@ -229,6 +229,7 @@ "skills/mcp-server-patterns/", "skills/messages-ops/", "skills/mle-workflow/", + "skills/ml-adoption-playbook/", "skills/motion-ui/", "skills/mysql-patterns/", "skills/nanoclaw-repl/", diff --git a/skills/ml-adoption-playbook/SKILL.md b/skills/ml-adoption-playbook/SKILL.md new file mode 100644 index 00000000..45f0cc6b --- /dev/null +++ b/skills/ml-adoption-playbook/SKILL.md @@ -0,0 +1,57 @@ +--- +name: ml-adoption-playbook +description: End-to-end methodology for AI agents and software engineers to add machine learning algorithms to existing non-ML codebases. Covers problem framing, data readiness, architectural decoupling, and baseline model integration. +origin: ECC +--- + +# ML Adoption Playbook + +This skill provides an adaptive methodology for implementing machine learning models into existing software engineering projects. It bridges the gap between traditional SWE and MLOps by structuring how ML should be researched, decoupled, trained, and integrated. + +## When to Activate + +- A user asks to "add ML" or "add an algorithm" to their existing codebase. +- Planning the integration of a new model (e.g., recommendation, classification, forecasting) into a non-ML application. +- Structuring a workflow for an agent to build, train, and deploy an ML component adaptively. + +## Phase 1: Problem Framing & Feasibility + +Before writing model code, establish the "why" and "how". +- **Heuristic Check:** Ask the user if a simple heuristic (e.g., regex, rule-based sorting) could solve the problem faster. If yes, start there. +- **Metric Definition:** Define what business metric the ML model is trying to improve (e.g., click-through rate, reduced latency). +- **Mistake Budget:** Define what a "bad" prediction looks like and how the system should handle it. + +## Phase 2: Data Readiness + +ML is useless without clean, accessible data. +- **Audit Data Sources:** Identify where the training data lives. Is it a live database, a static CSV, or an API? +- **Data Contract:** Establish a schema for the input data. What features are required? What happens if a feature is missing? +- **Leakage Prevention:** Ensure the user's proposed data split does not accidentally leak future information into the training set (e.g., chronological splitting for time-series data). + +## Phase 3: Architectural Integration & Decoupling + +Do not tightly couple model inference to core business logic. +- **API Boundary:** Suggest placing the model behind an API endpoint (e.g., using `fastapi-patterns` or `django-patterns`) or a dedicated service class. +- **Fallback Mechanisms:** Design a default state. If the model takes too long to respond or throws an error, the system must gracefully fall back to a hardcoded rule. +- **Feature Flags:** Wrap the new ML inference call in a feature flag so it can be rolled out (or rolled back) safely. + +## Phase 4: Model Implementation & Training + +Structure the code for reproducibility and iteration. +- **Start Simple:** Build a baseline model first (e.g., a simple scikit-learn Logistic Regression or a barebones PyTorch linear layer). +- **Reproducibility:** Apply `pytorch-patterns` or similar best practices: fix random seeds, make code device-agnostic, and explicitly document tensor/array shapes. +- **Automated Evidence:** Require tests for the data transforms and inference schema. Do not accept a model without an evaluation script comparing it against the baseline. + +## Phase 5: Handoff to MLOps + +Once the baseline model is integrated, shift focus to continuous operations. +- **Refer to `mle-workflow`:** Guide the user toward setting up experiment tracking, model registries, and drift detection. +- **CI/CD:** Add the model evaluation step to the existing CI pipeline to ensure future commits do not degrade model performance. + +## Iterative Agent Workflow + +When assisting a user via this playbook, agents should: +1. **Ask clarifying questions** to complete Phase 1 before proposing architectures. +2. **Draft a data contract** in Phase 2 for user approval. +3. **Write the decoupling interface** (API/Service) in Phase 3 *before* writing the training loop. +4. **Deliver a reproducible script** in Phase 4 that trains the model and saves the artifact. \ No newline at end of file