AI CLI Standardization: From Tool Lock-in to Portability

Introduction: From Web Chatbots to CLI Tools

AI is a powerful tool, and for IT professionals, the most effective way to leverage it is through CLI tools like GitHub Copilot CLI, Claude Code, Gemini CLI, or similar agents. In previous articles like GitHub Spec-Kit, I explored spec-driven development and structured AI workflows, but I realized I skipped fundamental concepts: why CLI tools beat web chatbots and how to standardize your AI setup for portability.

The AI landscape evolves at the speed of light. Today, Model A delivers the best results; tomorrow, a different provider offers better cost/performance. Working through web interfaces locks you into platforms and creates painful migrations. CLI-based workflows, combined with standardized context files, make your AI setup portable, reproducible, and future-proof.

Why CLI Tools Beat Web Chatbots

Web-based AI chatbots (ChatGPT, Claude Web, Gemini Advanced) are convenient for quick questions, but they fall short for serious software development. CLI tools dominate for four critical reasons:

Direct File System Integration: CLI tools operate natively in your environment, reading and writing files directly, navigating repositories, executing commands, and applying changes across multiple files atomically. Web chatbots force constant copy-paste friction that destroys productivity and introduces transcription errors.

Context Files as Ground Rules: This is the game-changer. CLI tools automatically read project-specific context files (AGENTS.md, CLAUDE.md, gemini.md) that establish how AI should operate in your repository. These files define coding standards, security boundaries, build commands, architectural decisions, and project-specific vocabulary. The AI consumes these files automatically, ensuring consistent behavior without repeating instructions. Web chatbots have no equivalent; you’d paste the same context repeatedly.

Portability Across Providers: When you structure workflows around standardized context files rather than provider-specific interfaces, migrating between tools becomes trivial:

# Today: GitHub Copilot CLI
gh copilot suggest "implement user authentication"

# Tomorrow: Claude Code
claude "implement user authentication"

# Next week: Gemini CLI
gemini "implement user authentication"

The AI tool changes, but your context files (AGENTS.md, build scripts, documentation) remain constant. Each tool reads the same ground rules and produces aligned results.

Version Control and Team Collaboration: Context files live in your repository, versioned with Git, reviewed in pull requests, shared across teams. When developers join or AI tools update, everyone (human and AI) works from the same documented expectations. Web chatbot conversations are ephemeral and isolated to individual users.

The AGENTS.md Standard and Context Organization

AGENTS.md has emerged as the unified convention for AI-specific context, replacing the proliferation of tool-specific files. Based on analysis of over 2,500 repositories, the community converged on this single file as the source of truth for AI coding agents.

Why AGENTS.md Works

• Cross-tool compatibility: Works with GitHub Copilot, Claude, Cursor, Gemini CLI, and others
• Discovery mechanism: Tools search from current directory up to repository root, then user home directory
• Precedence rules: More specific (closer) files override general ones, enabling nested configurations in monorepos
• Separation of concerns: Keeps AI-specific instructions out of README.md, reducing clutter for human contributors

What Belongs in AGENTS.md

Based on best practices from thousands of projects, effective AGENTS.md files cover:

# Build & Test
- `npm run build` — compiles TypeScript to dist/
- `npm test` — runs Jest test suite
- `docker-compose up` — starts local dev environment

# Architecture Overview
React frontend (/frontend), Node.js API (/api), background workers (/workers).
Data flow: client → API → PostgreSQL.

# Code Conventions
- TypeScript strict mode
- Prefer async/await over callbacks
- All database mutations through DataService abstraction
- Follow airbnb-typescript eslint rules

# Git Workflow
- Branch naming: feat/*, fix/*, chore/*
- All PRs require one reviewer approval
- Conventional Commits format

# Security Boundaries
- Never modify /vendor or /secrets
- Never commit .env files or API keys
- Use environment variables for all configuration
- Test user input for injection vulnerabilities

# Project Vocabulary
- "Users" = individuals; "Accounts" = organizations
- "Sessions" are Redis-backed with 24-hour expiry
- Background jobs use BullMQ with Redis

# Useful Links
- [API Documentation](https://docs.example.com/api)
- [Architecture Diagram](https://docs.example.com/arch)

Key principles: Keep it under 200-300 lines, lead with actionable commands (build, test, lint), explicitly mark forbidden actions, use realistic examples instead of vague descriptions, and update when architectural decisions change.

Organizing Advanced Context with ./agents Folder

For complex projects, organize specialized skills and commands in an ./agents folder:

project-root/
├── AGENTS.md              # Main context file
├── .agents/
│   ├── skills/
│   │   ├── kubernetes.md  # K8s-specific operations
│   │   ├── database.md    # Database migration patterns
│   │   └── security.md    # Security scanning workflows
│   ├── commands/
│   │   ├── deploy.sh      # Custom deployment scripts
│   │   └── analyze.py     # Code analysis utilities
│   └── templates/
│       ├── api-endpoint.md
│       └── component.md

This modular approach works for:

• Specialized Skills: Domain-specific knowledge (GraphQL API patterns, Kubernetes deployments, database migrations)
• Custom Commands: Project-specific automation scripts AI can invoke or reference
• Templates: Reusable code patterns for common structures (API endpoints, React components, test files)

Reference the folder in your AGENTS.md:

# Additional Context
For specialized operations, see ./agents/skills/:
- Kubernetes: ./agents/skills/kubernetes.md
- Databases: ./agents/skills/database.md
- Security: ./agents/skills/security.md

Note: Some AI CLI tools like GitHub Copilot CLI (version 0.0.401+, released January 2026) automatically load the .agents/skills directory, eliminating the need for manual references in AGENTS.md. Check your tool’s documentation for auto-discovery capabilities.

Claude Code Workaround

While most modern AI CLI tools support AGENTS.md natively, Claude Code (as of early 2026) requires a pointer file. Create a CLAUDE.md that references AGENTS.md:

# CLAUDE.md
This project uses the AGENTS.md standard for AI context.

Please read and follow all instructions in:
- ./AGENTS.md (primary AI context)
- ./agents/skills/ (specialized operation guides)

All guidelines, security boundaries, and coding conventions 
are documented in AGENTS.md.

This hybrid approach maintains a single source of truth while ensuring compatibility. When Claude Code adds native support, simply delete the pointer file.

Security and Governance with AGENTS.md

For organizations adopting AI CLI tools at scale, security and governance are critical. The AGENTS.md specification includes enterprise features designed for DevSecOps environments, audit trails, and compliance requirements.

Signed Manifests for Integrity

AGENTS.md supports cryptographic signatures to verify context file authenticity. This prevents tampering and ensures AI tools operate only with approved instructions:

# Sign AGENTS.md with GPG
gpg --clearsign --default-key [email protected] AGENTS.md

# Produces AGENTS.md.asc with embedded signature

Enterprise AI CLI tools (GitHub Copilot Enterprise, Claude for Business) can validate signatures before loading context:

# .agents/config.yml (example enterprise config)
security:
  require_signed_manifests: true
  trusted_keys:
    - fingerprint: "0x1234567890ABCDEF"
      owner: "[email protected]"
  reject_unsigned: true

Use cases:

• Supply chain security: Prevent malicious context injection in open-source dependencies
• Compliance requirements: SOC 2, ISO 27001 environments requiring documented AI behavior
• Multi-tenant platforms: Ensure customers’ AI agents can’t access each other’s contexts

Audit Trails and Telemetry

Enterprise AI tooling provides telemetry hooks to log AI interactions for security review:

# mise.toml with audit logging
[env]
AI_AUDIT_LOG = "/var/log/ai-agents/audit.log"
AI_TELEMETRY_ENDPOINT = "https://telemetry.example.com/v1/events"

[tasks.ai-audit-check]
run = """
echo "Checking AI audit logs..."
jq 'select(.action == "file_write")' $AI_AUDIT_LOG | tail -20
"""

Audit events typically capture:

• Context files loaded (AGENTS.md, skill modules)
• Files accessed, created, or modified by AI
• Commands executed by AI agents
• Security boundary violations (forbidden file access)
• API calls to external services

Example audit log entry:

{
  "timestamp": "2026-02-03T18:15:42Z",
  "tool": "github-copilot-cli",
  "version": "0.0.401",
  "user": "[email protected]",
  "action": "context_loaded",
  "files": ["AGENTS.md", "agents/skills/kubernetes.md"],
  "checksum": "sha256:a1b2c3...",
  "signature_valid": true
}

Security Boundaries in Practice

Reference security frameworks directly in AGENTS.md for automatic enforcement:

# Security Boundaries (SOC 2 Compliance)

## Forbidden Operations
- Never read or write files in /secrets, /vendor, /.env
- Never execute commands with sudo or privilege escalation
- Never commit API keys, tokens, or credentials
- Never access production databases from local environment

## Required Validations
- All user input must pass parameterized query validation
- File uploads checked against MIME type whitelist
- Authentication required for all API endpoints except /health

## Audit Requirements
- Log all database mutations to audit_log table
- Record AI-generated code changes in git commits with "AI-Assisted:" prefix
- Weekly security scans: `npm audit && trivy scan .`

## External Service Access
Allowed domains for AI API calls:
- api.openai.com (GPT models)
- api.anthropic.com (Claude models)
- generativelanguage.googleapis.com (Gemini models)

Block all other outbound connections during AI sessions.

AI tools respecting AGENTS.md spec will refuse operations violating these boundaries and log violations to audit trails.

Environment Management with mise.toml

Beyond code context, AI workflows need reproducible environment setup. mise is a universal tool version manager ensuring everyone (developers, CI/CD, AI agents) runs identical tool versions. Without version locking, you get “works on my machine” problems where AI-generated code runs locally but fails in CI or for teammates.

Key benefits: Deterministic installations where mise install gives identical versions across all environments, automatic activation when entering directories, seamless CI/CD integration through GitHub Actions, and task definitions for standardized workflows. mise is backwards-compatible with asdf (the previous standard), supporting its plugin ecosystem while delivering 20-100x faster performance through Rust implementation.

Example: DevSecOps security scanning workflow:

[tools]
python = "3.11.7"
node = "20.11.0"
"npm:gh-copilot" = "1.0.3"

[env]
_.file = ".env"
TRIVY_VERSION = "0.50.0"
SECURITY_BASELINE = "compliance/baseline.yml"

[tasks.security-scan]
run = """
trivy fs --severity HIGH,CRITICAL .
trivy config --severity HIGH,CRITICAL .
npm audit --audit-level=high
"""

[tasks.ai-security-check]
depends = ["security-scan"]
run = """
echo "Security baseline passed. AI can proceed with changes."
gh copilot suggest "implement authentication feature"
"""

Example: Automated compliance verification:

[tasks.compliance-check]
run = """
echo "Running compliance checks..."
python scripts/check_secrets.py
python scripts/validate_licenses.py
npm run lint:security
"""

[hooks.after]
"tasks:ai-generate" = "mise run compliance-check"

This ensures AI-generated code passes security and compliance checks automatically before being committed.

Additional mise capabilities:

• Hierarchical version scoping: Override global versions per project or nested service (critical for monorepos)
• Environment variable templates: Dynamic environment construction using {{ config_root }} and {{ env.HOME }}
• Task dependencies and hooks: Chain tasks and run automated checks after AI operations
• Backend aliases: Pin specific tool backends (e.g., core:python) to avoid breaking changes
• asdf migration: Reads .tool-versions files natively for seamless migration

For detailed documentation on these features, see mise.jdx.dev.

Complementary Context Strategies

Beyond AGENTS.md and mise.toml, these patterns strengthen AI workflows:

Specification Files: As covered in my Spec-Kit article, structured specs (Spec-Kit, OpenSpec) provide high-level context about features, requirements, and architectural decisions. They complement AGENTS.md by focusing on what and why rather than how.

Architecture Decision Records (ADRs): Document key technical choices with rationale in docs/adr/:

# ADR-001: Use PostgreSQL for Primary Database

## Context
We need persistent storage for user accounts and transactional data.

## Decision
Use PostgreSQL 15 with TimescaleDB extension for time-series metrics.

## Consequences
- Requires Docker Compose for local dev
- Enables SQL-based analytics without separate warehouse
- Team needs PostgreSQL expertise for optimization

Reference ADRs in AGENTS.md so AI understands why certain patterns exist.

Configuration Templates: Provide .env.example so AI understands required environment variables:

# .env.example
DATABASE_URL=postgresql://localhost/myapp
REDIS_URL=redis://localhost:6379
OPENAI_API_KEY=sk-...
LOG_LEVEL=info

Pre-commit Hooks: Configure automated checks AI-generated code must pass:

# .pre-commit-config.yaml
repos:
  - repo: https://github.com/psf/black
    rev: 23.12.0
    hooks:
      - id: black

Reference these in AGENTS.md so AI generates code that passes linting.

Workflow Automation: Encapsulate complex workflows in Makefiles or scripts:

# Makefile
.PHONY: setup test deploy

setup:
	mise install
	npm install
	docker-compose up -d

test:
	npm run lint
	npm test
	python -m pytest

deploy:
	./scripts/deploy.sh production

Include these in AGENTS.md’s “Build & Test” section.

Practical Setup: Implementing Standardized AI Context

Here’s how to implement these patterns in a new or existing project:

Step 1: Create AGENTS.md

cd your-project
cat > AGENTS.md << 'EOF'
# Build & Test
- `npm install` — install dependencies
- `npm run build` — compile TypeScript
- `npm test` — run test suite

# Architecture
Microservices: API (Node.js), Workers (Python), Frontend (React).

# Security
- Never commit .env files
- All secrets via environment variables

# Git Workflow
- Branch: feat/*, fix/*, chore/*
- PRs require 1 reviewer

# Conventions
- TypeScript strict mode
- ESLint airbnb-typescript
- Prefer async/await

# Forbidden
- Never modify /vendor or /secrets
EOF

Step 2: Add Claude Code workaround (if needed)

cat > CLAUDE.md << 'EOF'
# CLAUDE.md
This project uses the AGENTS.md standard.

Please read and follow: ./AGENTS.md
EOF

Step 3: Create ./agents structure (optional)

mkdir -p agents/{skills,commands,templates}

cat > agents/skills/deployment.md << 'EOF'
# Deployment Skill

## Production
- `make deploy-prod` — requires AWS credentials
- Always run tests first

## Staging
- `make deploy-staging`
EOF

Step 4: Set up mise.toml (if needed)

cat > mise.toml << 'EOF'
[tools]
node = "20.11.0"
python = "3.11.7"

[env]
_.file = ".env"

[tasks.test]
run = "npm test && python -m pytest"
EOF

mise install

Step 5: Add configuration template

cat > .env.example << 'EOF'
DATABASE_URL=postgresql://localhost/db
REDIS_URL=redis://localhost:6379
API_KEY=your-key-here
EOF

Step 6: Document in README

## AI-Assisted Development

This project uses AI CLI tools with standardized context:
- Read AGENTS.md for AI-specific guidelines
- Claude Code users: CLAUDE.md points to AGENTS.md
- Run `mise install` to set up correct tool versions
- Copy `.env.example` to `.env` and configure

Step 7: Commit and share

git add AGENTS.md CLAUDE.md agents/ mise.toml .env.example README.md
git commit --signoff -m "feat: add AI context standardization"
git push

Now anyone using AI CLI tools in this repository gets consistent, predictable behavior aligned with your project’s standards.

Tool Support and Evolving Standards

The AI CLI tooling ecosystem evolves rapidly. This section reflects the state as of February 2026; always verify current support with official tool documentation.

Feature Support Matrix

Legend: ✅ Fully supported | ⚠️ Partial/Beta | ❌ Not available Note: I don’t use Cursor, I’ve provided a more extensive comparison based on web research

Wrapping Up: Why This Matters

CLI tools outperform web chatbots for development work because they access files directly, read context automatically, and execute commands without friction. When you use standardized context files like AGENTS.md instead of tool-specific configurations, switching between AI providers becomes trivial.

Think of AGENTS.md and mise.toml as executable documentation; they’re instructions AI tools consume automatically on every interaction, ensuring consistent behavior across thousands of operations. Tools like mise guarantee that AI-generated code works identically on your machine, your teammate’s laptop, and CI/CD pipelines.

Start simple with basic AGENTS.md covering build commands and security boundaries, add mise.toml when version drift causes issues, introduce specs and ADRs when compliance demands them; don’t over-engineer from day one. All these organizational patterns (AGENTS.md, ./agents folder, mise.toml, signed manifests, audit trails) remain optional for typical repositories but become essential for structured enterprise projects where AI agents need explicit guidance to produce outputs matching your expectations.

Major providers (GitHub, Anthropic, Google) are collaborating through the AI Context Working Group to standardize these patterns. By late 2026, expect universal support across CLI tools, standardized security schemas, and certified compliance templates for SOC 2 and ISO 27001.

References

• AGENTS.md Official Specification
• GitHub: How to Write a Great AGENTS.md
• Mise Documentation
• My Previous Article: GitHub Spec-Kit