AI Coding Knowledge Base Best Practices: Rebuilding a Maintainable System with a Three‑Layer Structure

After tools such as Claude Code, Copilot, and various agent‑type utilities become routine in development teams, many encounter a common problem: the tools become more powerful while the surrounding configuration, constraints, and processes grow chaotic. Teams initially dump all rules, notices, steps, templates, and experiential tips into a single master file for convenience, which works early on but soon inflates into an unreadable “stone‑tablet” document.

Why AI configuration inevitably becomes messy

Teams start by adding a little extra—an encoding rule, then a deployment note, then a troubleshooting flow—each addition seems reasonable because it appears related to the AI’s operation. Over time the file morphs into a hybrid of team standards, operation manuals, task specifications, and memoranda, losing clear boundaries. Typical symptoms include ever‑longer files, duplicated processes, debates over whether a piece belongs in a global rule or a scenario template, and increasing difficulty in understanding responsibilities.

From a software‑design perspective this is a classic responsibility‑mixing issue: configuration is treated as mere documentation instead of an engineered, evolving asset. Applying the same layering principle used for code—separate modules, define clear boundaries—leads to a three‑layer structure.

The core idea: split rules, flows, and tasks

The three layers are:

Base layer : rules and constraints that are valid for every task over the long term.

Flow layer : standardized procedures that are triggered only in specific scenarios.

Task layer : independent, clearly defined work units that can be delegated and executed in parallel.

Separating these concerns improves stability, reusability, and composability, turning AI from a “usable helper” into a “maintainable collaboration infrastructure.”

Base Layer – Identity, Boundaries, Hard Constraints

The base layer acts like the agent’s “foundation personality.” It does not describe how to perform a specific task, but defines what must never be crossed.

Typical files:

AGENT.md – execution rules, behavior directives, prohibitions, collaboration style.

rules.md – project‑level constraints, coding standards, output formats, review principles.

What belongs here?

Allowed and prohibited tools.

Mandatory pre‑read, validation, or confirmation steps.

Code style, naming conventions, security boundaries.

Output language, response style, submission requirements.

Restrictions on external systems, sensitive data, internal network operations.

Example AGENT.md

# Execution Rules

1. Modify any file only after reading the context.
2. Prohibit destructive actions without explicit confirmation.
3. Use official toolchains for keys, credentials, and permission control.
4. Disallow fragile UI automation when invoking a browser.

# Behavioral Requirements
- Respond in Chinese by default.
- Provide executable conclusions.
- Explain impact scope after code changes.

Example rules.md

# Project Rules

- Use kebab‑case for file names.
- Prohibit debug output in production code.
- Database migrations must undergo separate review.
- Security‑related changes require manual verification.

Common pitfall: stuffing step‑by‑step procedures (release, rollback, weekly reports) into these files. “Common” does not equal “globally valid.” The base layer must stay short, hard, and stable.

Flow Layer – Encapsulating Reusable Processes as Skills

The flow layer holds scenario‑specific, repeatable procedures. It is not about permanent boundaries but about actions to take when a particular intent appears.

In modern agent ecosystems, this maps naturally to Skill files because Skills are designed for clearly defined triggers, multi‑step logic, and optional script or MCP tool calls.

What a Skill typically contains

Scenario description and trigger conditions.

Pre‑conditions and completion criteria.

Multi‑step execution logic.

Reusable scripts.

MCP tool invocation details.

Related resources or templates.

A good flow definition answers four questions:

When is it triggered?

What pre‑conditions must be satisfied?

What steps compose the execution?

How is successful completion determined?

Skill definition example

---
name: deploy-service
description: Handles service deployment, including checks, release, and verification
---

## Trigger Conditions
- User requests deployment, release, or rollout

## Preconditions
- Tests have passed
- Change has been approved

## Execution Steps
1. Read change description
2. Run pre‑deployment checks
3. Deploy to staging via deploy MCP
4. Verify core link
5. Deploy to production
6. Send completion notification

## Tools
- deploy MCP
- test script
- notify script

If a Skill needs a supporting script, it can be linked directly:

# skills/deploy/check_release.py

def precheck(ctx):
    assert ctx["tests_passed"] is True
    assert ctx["approved"] is True
    return "ok"

Typical error: turning a Skill into a “catch‑all” that mixes unrelated tasks, which defeats reusability.

Task Layer – Dynamic, One‑Off Assignments

The task layer answers “who should do it and can it be executed independently?” It deals with work that has clear inputs, expected outputs, and does not rely on long‑term context.

Examples:

Review PR naming conventions.

Scan a module for security risks.

Complete documentation for a set of functions.

Generate a draft blog post from source material.

Research and summarize multiple proposals.

Key characteristics: strong boundaries, parallelizable, stateless. The task layer should keep three aspects consistent: change frequency, invocation mode, and granularity.

Task definition checklist

Goal – what the task aims to achieve.

Input – materials required.

Boundary – what is in‑scope and out‑of‑scope.

Output format – how results should be presented.

Typical technical forms:

Subagent Prompt.

create_task / task‑dispatch command.

Dynamic context, goal, and output specifications.

Task‑specific constraints.

Subagent prompt example

You are a code‑review sub‑agent.

Task goal: Check naming issues and leftover debug code in this PR.
Input materials: Git diff, excerpts from project rules.
Focus only on: naming conventions, debug output, explicit TODOs.
Do NOT: Evaluate architecture quality or comment on unchanged files.
Output format:
- file:line
- issue
- reason
- suggested fix

Dynamic create_task example

{
  "agent": "doc-writer",
  "goal": "Generate an initial release note draft from this interface change description",
  "context": {
    "language": "zh-CN",
    "audience": "internal engineers",
    "source_files": ["api_diff.md", "release_notes.txt"]
  },
  "output": {
    "format": "markdown",
    "sections": ["Change Summary", "Compatibility Impact", "Upgrade Advice"]
  }
}

How to decide which layer a piece of content belongs to

Use the following decision sequence:

Is the content required to be valid for all tasks over the long term? If yes → Base layer.

If not, does it represent a complete, fixed process triggered by a specific scenario? If yes → Flow layer.

If neither, can it be defined as an independent, delegable task with clear inputs/outputs? If yes → Task layer.

This turns “where to write it” from a gut feeling into a discussable, repeatable standard.

Typical anti‑patterns

Continuously stuffing everything into AGENT.md, turning it into a hybrid of rules, flows, and tasks.

Hard‑coding one‑off task details inside a Skill, effectively making the Skill a task.

Allowing an Agent to accumulate excessive state or historical context, which harms parallelism, reproducibility, and debuggability.

Benefits after layering

Smaller master rule file : only truly global constraints remain, improving readability and trust.

Reduced collaboration friction : discussions shift from vague “where should this go?” to concrete, structured judgments.

Less duplicated maintenance : each layer owns its domain, so changes are localized.

More stable AI behavior : clear separation of constraints, scenario flows, and tasks gives the AI a consistent decision path.

Getting started

Inventory existing AI‑related configuration files, templates, and prompts.

Classify each item into Base, Flow, or Task according to the decision sequence.

First target the most bloated part—usually the monolithic rule file—and migrate clearly global rules to the Base layer.

For each Flow, explicitly document trigger conditions and completion criteria.

For each Task, define precise input, output format, boundaries, and any temporary constraints.

Directory layout example

agent-system/
├── AGENT.md
├── rules.md
├── skills/
│   ├── deploy-service/
│   │   ├── SKILL.md
│   │   └── check_release.py
│   ├── incident-response/
│   │   └── SKILL.md
│   └── write-blog/
│       └── SKILL.md
└── tasks/
    ├── review_pr.json
    └── write_release_note.json

Conclusion

Dividing AI collaboration configuration into Base, Flow, and Task layers is not added complexity but a return to basic engineering principles: keep stable rules stable, encapsulate reusable processes, and isolate one‑off work. This separation yields a slimmer rule base, lower coordination cost, fewer duplicated efforts, and a noticeably more reliable AI assistant.