Loop AGENTS.md

A set of instructions for an AI coding agent working on the Loop repository. It defines how to plan changes, review architecture and code, and follow code-quality and type-safety preferences.

In plain words
What is it for?
Use it when planning, reviewing, implementing, or testing changes in Loop, especially when you want explicit trade-offs, thorough tests, and careful handling of edge cases.
Why use it?
It gives the agent consistent engineering expectations before it changes code, helping avoid duplicated logic, weak tests, unclear designs, and unnecessary complexity.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/kevin-liu-01/loop/agents-md
Clone the repo
git clone --depth 1 https://github.com/Kevin-Liu-01/Loop

Made for: Codex, OpenCode.

Per session 1,953 This file is loaded in full into every session.
When invoked 1,953 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.01953 $0.01953
Opus 5 $0.00977 $0.00977
Sonnet 5 $0.00391 $0.00391
Haiku 4.5 $0.00195 $0.00195

Measured 3d ago against content hash 851a49d2347b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Loop AGENTS.md 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 3d 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.

AGENTS.md · 210 lines

How it starts

The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Claude Code Prompt for Plan Mode

#prompts

Review this plan thoroughly before making any code changes. For every issue or recommendation, explain the concrete tradeoffs, give me an opinionated recommendation, and ask for my input before assuming a direction. My engineering preferences (use these to guide your recommendations):

  • DRY is important--flag repetition aggressively.
  • Well-tested code is non-negotiable; I'd rather have too many tests than too few.
  • I want code that's "engineered enough" -- not under-engineered (fragile, hacky) and not over-engineered (premature abstraction, unnecessary complexity).
  • I err on the side of handling more edge cases, not fewer; thoughtfulness > speed.
  • Bias toward explicit over clever.
  1. Architecture review Evaluate:
  • Overall system design and component boundaries.
  • Dependency graph and coupling concerns.
  • Data flow patterns and potential bottlenecks.
  • Scaling characteristics and single points of failure.
  • Security architecture (auth, data access, API boundaries).
  1. Code quality review Evaluate:
  • Code organization and module structure.
  • DRY violations--be aggressive here.
  • Error handling patterns and missing edge cases (call these out explicitly).
  • Technical debt hotspots.
  • Areas that are over-engineered or under-engineered relative to my preferences.
  1. Test review Evaluate:
  • Test coverage gaps (unit, integration, e2e).
  • Test quality and assertion strength.
  • Missing edge case coverage--be thorough.
  • Untested failure modes and error paths.
  1. Performance review Evaluate:
  • N+1 queries and database access patterns.
  • Memory-usage concerns.
  • Caching opportunities.
  • Slow or high-complexity code paths.

For each issue you find For every specific issue (bug, smell, design concern, or risk):

  • Describe the problem concretely, with file and line references.
  • Present 2-3 options, including "do nothing" where that's reasonable.
  • For each option, specify: implementation effort, risk, impact on other code, and maintenance burden.
  • Give me your recommended option and why, mapped to my preferences above.
  • Then explicitly ask whether I agree or want to choose a different direction before proceeding.

Workflow and interaction

  • Do not assume my priorities on timeline or scale.
  • After each section, pause and ask for my feedback before moving on.

BEFORE YOU START, Ask if I want one of two options: 1/ BIG CHANGE: Work through this interactively, one section at a time (Architecture -> Code Quality -> Tests -> Performance) with at most 4 top issues in each section 2/ SMALL CHANGE: Work through interactively ONE question per review section

FOR EACH STAGE OF REVIEW: output the explanation and pros and cons of each stage's questions AND your opinionated recommendation and why, and then use AskUserQuestion. Also NUMBER issues and then give LETTERS for options and when using AskUserQuestion make sure each option clearly labels the issue NUMBER and option LETTER so the user doesn't get confused. Make the recommended option always the 1st option.

Read the full file on GitHub · 210 lines

Changes

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.

  1. 3d ago First seen · 210 lines · 1,953 tokens per session scan A 851a49d2347b

Subscribe to this mod's changes

Loop AGENTS.md is an instructions file published in the GitHub repository Kevin-Liu-01/Loop (18 stars, last pushed 1mo ago), licensed MIT. It adds 1,953 tokens to every session, about $0.0098 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.