Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionnpx agentmods add skills/hajekim/agentic-design-patterns-extension/appendix-ai-cliWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/appendix-ai-cli)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/appendix-ai-cli"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/appendix-ai-cli/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/appendix-ai-cli"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/appendix-ai-cli.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00428 | $0.03560 |
| Opus 5.5 | $0.00171 | $0.01424 |
| Sonnet 5.5 | $0.00086 | $0.00712 |
| Haiku 4.5 | $0.00043 | $0.00356 |
Grade A, and why
appendix-ai-cli scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to appendix-ai-cli — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 340 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Appendix E - AI Agents on the CLI
Overview
AI CLI Agents represent a new class of tools that transform the developer's terminal from a command executor into an intelligent, collaborative workspace. These agents understand natural language, maintain context about entire codebases, and perform complex multi-step development tasks autonomously — from architectural refactoring to test generation to documentation.
The command-line interface is an ideal environment for AI agents: text-based, sandboxed, and structured. Unlike GUI agents that must interpret visual layouts, CLI agents work directly with code, files, and system commands, enabling precise and auditable automation.
Core Principle: Choose the CLI agent based on your workflow: Claude Code for architectural work, Gemini CLI for multimodal and cloud tasks, Aider for git-centric TDD, GitHub Copilot CLI for GitHub-integrated workflows.
When This Skill Applies
Activate this pattern when:
- Performing large-scale refactoring across many files
- Generating comprehensive test suites for existing code
- Generating or updating technical documentation
- Implementing features described in natural language
- Integrating with CI/CD pipelines for automated code review
- Comparing AI CLI tools for a team's development workflow
- Setting up CLAUDE.md / GEMINI.md project memory files
CLI Agent Comparison
| Agent | Provider | Core Strength | Best For |
|---|---|---|---|
| Claude Code | Anthropic | Deep codebase understanding, architectural reasoning | Large-scale refactoring, multi-file changes, architectural tasks |
| Gemini CLI | Open-source, multimodal, large context window, Google Cloud integration | Multimodal tasks, Google Cloud workflows, broad accessibility | |
| Aider | Open Source | Git-centric, auto-commit, TDD workflow, model-agnostic | Test-driven development, precise bug fixes, auditable changes |
| GitHub Copilot CLI | GitHub/Microsoft | Deep GitHub ecosystem integration, issue→PR workflow | GitHub-integrated teams, automated issue resolution |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 340 lines · 428 tokens per session scan A 0a30184cd661
appendix-ai-cli is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 6mo ago), licensed MIT. It adds 428 tokens to every session and 3,560 once invoked, about $0.0017 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to appendix-ai-cli, differing in 3 lines, and is treated as a copy.
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