Agent Engineering Workflow

简体中文

Production-oriented workflow skills for AI coding agents.

v2.2.0

Organize the following pipeline into installable, reusable Coding Agent Workflows:

Requirement → Plan → Code Navigation → Impact Analysis → Implementation → Validation → Knowledge Capture → Git Commit → Delivery

This is not a plain collection of prompts, but a set of installable Coding Agent Workflow Skills.

Why

Common problems with traditional Coding Agents:

• Reading too much context
• Losing control of change scope
• Not knowing the impact range of code changes
• Overwriting the user's existing modifications
• Lacking knowledge capture in long-running projects
• Depending on chat memory across sessions
• Unstable validation and delivery processes

This project addresses them through:

Two Ways of Use

This project offers two levels of usage:

Lightweight Prompt Edition

Best for small projects, personal projects, and scenarios where you want to copy prompts directly.

Located at:

simplePrompt/

Main files:

• OptimizeGeneratePromotSkill.md
  • For new project initialization and incremental development
• OptimizeEditFunctionSkill.md
  • For feature modification, bug fixes, and local refactoring in existing projects

These prompts are verified through real usage — shorter workflow and lower context overhead, suitable for small projects that do not need a full Agent Skills infrastructure.

v2.2 adds state machine, risk control, code graph, and vector knowledge on top of the lightweight prompt edition, so it is more complete but also requires more context and a more complex execution flow.

Agent Skills Edition

Best for medium-to-large projects, long-term maintained projects, and scenarios that need persistent knowledge, code graphs, state management, and standard Skill installation.

Includes:

• project-bootstrap-workflow
• feature-change-workflow

Features:

• Standard SKILL.md structure
• State machine
• Risk levels
• Code Graph
• Vector Backend
• Progressive Discovery
• Cross-session plan state
• Complete validation and delivery workflow

Skills

New project → project-bootstrap-workflow
Existing project change → feature-change-workflow

Architecture

The two Skills share one Base Engineering Protocol. Feature Change only adds the specific flow for modifying existing projects, avoiding two duplicated rule sets.

        Shared Engineering Protocol
                 │
          ┌──────┴──────┐
          ↓             ↓
   Project Bootstrap  Feature Change
      Workflow           Workflow
          ↓                 ↓
      New Project     Existing Project

The Base Protocol is defined once, at skills/project-bootstrap-workflow/references/base-protocol.md.

Core Capabilities

Engineering Control

• Agent State Machine
• L0–L4 Risk Control
• Before Snapshot
• Decision Log

Code Intelligence

• Progressive Discovery
• L1/L2/L3 Navigation
• Code Graph
• Impact / Blast Radius

Knowledge

• Vector Backend
• MCP → Python → Markdown fallback
• Full Index
• Incremental Sync
• Health Check

Delivery

• Git Safety
• Validation Pipeline
• Acceptance Criteria
• Cross-session Plan State

Knowledge System

MCP Vector Backend
        ↓
Python Local Vector Backend
        ↓
Markdown fallback

• Reuse an existing Backend first
• When no MCP is available, try a Python project-level isolated environment
• Use Markdown Summary only as the last resort
• Not bound to any specific vector database product

Quick Start

New Project

Use project-bootstrap-workflow to initialize and develop a new project from readme.md.

Existing Project

Use feature-change-workflow to modify the existing project based on the following requirement:
<feature request>

Bug Fix

Use feature-change-workflow to analyze and fix the existing project based on the following issue:
<bug description>

Installation

Option A — Project-local installation

Best for team projects. Clone the repository, then copy the Skill directory you need into the target agent's project-level skills directory.

git clone https://github.com/cjz-wr/agent-engineering-workflow.git
cd agent-engineering-workflow/

Option B — User-level installation

Best for reusing across all your personal projects. Copy the Skill directory into the target agent's user-level skills directory.

Option C — Local Installer

A local-only install script (copies files locally, never auto-installs extra dependencies over the network):

./scripts/install-local.sh codex
./scripts/install-local.sh claude
./scripts/install-local.sh cursor

Install to the user-level directory:

./scripts/install-local.sh codex --user

The script never silently overwrites an existing Skill with the same name — it fails safely with a hint.

Skills search paths may differ between clients and versions. Always follow the target client's current official documentation.

What Happens After Installation

User Request
    ↓
Skill Selection
    ↓
Analyze
    ↓
Plan
    ↓
Code Navigation
    ↓
Implement
    ↓
Validate
    ↓
Commit
    ↓
Delivery

Example

examples/demo-project/ demonstrates a complete example and the relationships between its files:

Repository Layout

agent-engineering-workflow/
├── README.md
├── skills/      # Two Workflow Skills
├── docs/        # Architecture and protocol docs
├── examples/    # demo-project example
└── scripts/     # Validation and install scripts

Compatibility

Designed for Agent Skills-compatible coding agents.

The exact discovery path and installation behavior may differ by client and version. Always follow the target client's current documentation for skill installation and discovery.

Development

Validate the Skill structure:

python scripts/validate-skills.py

Checks: Skill structure, frontmatter, references, version, duplication.

Suggested flow when changing the protocol:

Change Base Protocol → Validate → Sync both Skills → Update version

Contributing

If you have suggestions or encounter issues, feel free to open an Issue or submit a Pull Request.

Version

Current version: v2.2.0

See git history / release notes for changes.

License

MIT