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agentfootprint: Instructions file for Codex

AGENTS.md

agentfootprint AGENTS.md is an instructions file for Codex, OpenCode from footprintjs/agentfootprint. It costs 5,652 tokens per session, scanned A, original, MIT.

A JavaScript framework for building generative-AI applications by controlling what information enters each language-model call and when. It provides calls, agents, reusable flow compositions, injections, and memory definitions.

In plain words
What is it for?
It helps build sequential, parallel, conditional, and looping agent workflows, add typed context or memory, and implement patterns such as Reflexion, Tree of Thought, and Swarm from shared building blocks.
Why use it?
It makes context engineering—the work of deciding which instructions, conversation, facts, and memory the model receives—explicit in the application’s control flow.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions CLAUDE.md; positional $N argument; mentions Codex.

This is footprintjs/agentfootprint's own configuration. It tells Codex and OpenCode how to work on agentfootprint itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything agentfootprint configures →

not rated 21repo changed yesterday A scan 5,652 tokens original MIT
View source ↗ footprintjs/agentfootprint agentfootprint.dev
Reuse

Borrowing it

Nothing to install: this file belongs to footprintjs/agentfootprint. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/footprintjs/agentfootprint/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/footprintjs/agentfootprint

Made for: Codex, OpenCode.

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Per session 5,652 This file is loaded in full into every session.
When invoked 5,652 The same file — it is already loaded in full.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.05652 $0.05652
Opus 5.5 $0.02261 $0.02261
Sonnet 5.5 $0.01130 $0.01130
Haiku 4.5 $0.00565 $0.00565

Measured yesterday against content hash 5673e23797e0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

agentfootprint 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 yesterday.

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 · 384 lines

How it starts

The opening of the file, as written. The whole thing — 384 lines — stays where its author put it; the contents beside it link to each section on GitHub.

agentfootprint — Agent Instructions (OpenAI Codex)

GENERATED from ai-instructions/claude-code/SKILL.md by npm run docs:regen — edit that file. Search CAPABILITIES.md before building anything — it may already ship. Contributing to agentfootprint itself? Read its CLAUDE.md (the definition of done).

agentfootprint structures AI agents as composable flowcharts, so every injection, read, write, decision and tool call becomes connected evidence as the run happens. Every concept takes an LLMProvider — swap mock({...}) for anthropic({...}) with zero code changes.

Core principles:

  • Adapter-swap testing ($0 test runs, deterministic assertions)
  • The ladder: mock → ollama (free, local, real model) → a paid provider
  • Declare context (facts, steering, skills); the framework decides WHEN it fires and WHICH slot it lands in
  • Collect during traversal, never post-process (inherited from footprintjs)
npm install agentfootprint footprintjs

Read this first — what does NOT exist

These are not hypothetical. A capable author, working from a correct mental model of this library, invented all four in one document. Treat them as the things you are most likely to get wrong here.

You will reach for The reality
startRun(...) No such function. The door is agent.run(input, options?), where AgentInput = { message: string; identity?; continueFrom? } and AgentOutput = string. run() returns AgentOutput | RunnerPauseOutcome — a run paused for a human returns a checkpoint; discriminate with isPaused(result).
RunStep as skill/route history RunStep is real and it is something else — the footprintjs flowchart TOPOLOGY slider, exported from agentfootprint/observe. Its kind is 'sequential' | 'fork' | 'merge' | 'decide' | 'iteration' | 'iteration-exit' | 'react'. Nothing in it concerns skills. Importing it succeeds, which is exactly why it is dangerous. For route history use routeRecorder() from the same door.
the LLM classifier as routing "tier 3" It is a tier-2 strategy. Tier 1 = declared start rules. Tier 2 = the configured scorer — llmClassifier(provider) OR keywordScorer() OR embeddingScorer(e) OR the entry scorer; near-ties fall through rather than argmax. Tier 3 = a menu the model resolves in-band through read_skill's own description, reached only when tier 2 was NOT decisive.
a skill's tools being gated to that skill automatically They are not, by default. defineSkill({ tools }) puts them in the agent's static tool list at build time — visible from iteration 1 whether the skill ever activates or not. Ask for the gate: .toolsFromActiveSkill() (agent-wide), skillGraph({ scopeTools: true }) (graph-wide), or autoActivate: 'currentSkill' (per skill). .tree() leaves are the one shape scoped by default.

Read the full file on GitHub · 384 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. yesterday Changed 5673e23797e0
  2. 7d ago Changed 957a2ab68756
  3. 13d ago Changed 646067c8d9db
  4. 14d ago Changed · +10 lines · +107 tokens per session 9b7c5e5313d3
  5. 22d ago Changed · +99 tokens per session 406a08d17261
  6. 26d ago Changed 2f06c97c59c0
  7. 27d ago Changed · -2 lines · +12 tokens per session c9688cb247a1
  8. 1mo ago Changed · +11 tokens per session c27283f88961
  9. 1mo ago Changed · +65 tokens per session 5f526a89ccb5
  10. 1mo ago First seen · 376 lines · 5,358 tokens per session scan A da27f8c48d70

Subscribe to this mod's changes

agentfootprint AGENTS.md is an instructions file published in the GitHub repository footprintjs/agentfootprint (21 stars, last pushed yesterday), licensed MIT. It adds 5,652 tokens to every session, about $0.0226 per session on Opus 5.5. 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.

AI SafetyBuilding agentsLLMagent-frameworkai-agentshuman-in-the-loopobservability
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