elephant.ai AGENTS.md

elephant.ai AGENTS.md is an instructions file for Codex, OpenCode from cklxx/elephant.ai. It costs 954 tokens per session, scanned A, original, MIT.

Project instructions defining how an AI coding agent should behave in elephant.ai, including priorities, coding rules, and file-scope limits.

In plain words
What is it for?
They guide responses and code changes in elephant.ai projects, including function size, abstraction choices, comments, configuration examples, and cleanup.
Why use it?
They give the agent consistent expectations for safety, correctness, maintainability, and code structure.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions Codex.

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/cklxx/elephant.ai/agents-md
Clone the repo
git clone --depth 1 https://github.com/cklxx/elephant.ai

Made for: Codex, OpenCode.

Wrote 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.

agentmods badge for elephant.ai AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/cklxx/elephant.ai/agents-md.svg)](https://agentmods.dev/instructions/cklxx/elephant.ai/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/cklxx/elephant.ai/agents-md"><img src="https://agentmods.dev/badge/instructions/cklxx/elephant.ai/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 954 This file is loaded in full into every session.
When invoked 954 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 5.1 $0.00954 $0.00954
Opus 5 $0.00477 $0.00477
Sonnet 5 $0.00191 $0.00191
Haiku 4.5 $0.00095 $0.00095

Measured 6d ago against content hash 8cab0aa5e61d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

elephant.ai 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 6d 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 · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

elephant.ai — Agent Contract

Minimize mandatory reading. Expand context only when triggered.

0. Read Policy

  1. Read §1 on every task.
  2. Read §2 only when a trigger matches.
  3. No trigger? Follow §3 and stop expanding context.

1. Mandatory Core

1.1 Identity and priority

  • Greet ckl at conversation start.
  • Priority: safety > correctness > maintainability > speed.
  • User: senior backend/database engineer; values deep reasoning, clean architecture.

1.2 Code style (non-negotiable)

  • Max function body: 15 lines. Extract or redesign if exceeded.
  • No comments that restate code. Only "why" comments for non-obvious decisions.
  • Prefer composition over inheritance. Prefer data transforms over mutation.
  • Every abstraction must justify itself: if used <2 places, inline it.
  • Delete dead code immediately. No TODOs in committed code.
  • Type signatures are documentation. Verbose names > comments.
  • Between two correct approaches, pick the one with fewer moving parts.
  • Trust type/caller invariants; no unnecessary defensive code.
  • No compatibility shims when requirements change; redesign cleanly.
  • Modify only relevant files.
  • Config examples must be YAML.

Reference density:

const authenticate = (token: string, secret: string): Result<Claims, AuthError> =>
  pipe(
    decode(token),
    chain(verify(secret)),
    mapErr(toAuthError)
  );

No wrapper classes. No builders. No config objects. Transform pipeline.

1.3 Branch safety

  • Use worktree for code changes; never edit directly on main.
  • On main, run before any edit:
    1. git diff --stat + git log --oneline -10
    2. If suspicious diffs: report to ckl before continuing.

1.4 Delivery

  • Prefer TDD for logic changes; cover edge cases.
  • Run lint + tests before delivery.
  • Code review before commit: python3 skills/code-review/run.py review.
  • Fix P0/P1 before commit; follow-up for P2.
  • Small, scoped commits. Warn before destructive ops.

2. Progressive Disclosure (trigger-gated)

Read the full file on GitHub · 109 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. 6d ago First seen · 109 lines · 954 tokens per session scan A 8cab0aa5e61d

Subscribe to this mod's changes

elephant.ai AGENTS.md is an instructions file published in the GitHub repository cklxx/elephant.ai (11 stars, last pushed 5mo ago), licensed MIT. It adds 954 tokens to every session, about $0.0048 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.

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