learn-with-ai: Instructions file for Codex

AGENTS.md

learn-with-ai AGENTS.md is an instructions file for Codex, OpenCode from janmarkuslanger/learn-with-ai. It costs 6,199 tokens per session, scanned A, original, MIT.

A set of instructions that turns a coding agent into a structured learning coach using a repository's curriculum and progress files.

In plain words
What is it for?
It helps plan learning sessions by available time, choose drills or quizzes, track gaps, and follow the learner's curriculum.
Why use it?
It keeps study sessions consistent and avoids repeating material or asking for information already stored in the project.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: reads .claude/ paths; mentions Claude Code; mentions AGENTS.md.

This is janmarkuslanger/learn-with-ai's own configuration. It tells Codex and OpenCode how to work on learn-with-ai 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 learn-with-ai configures →

Reuse

Borrowing it

Nothing to install: this file belongs to janmarkuslanger/learn-with-ai. 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/janmarkuslanger/learn-with-ai/main/AGENTS.md
Clone the repo
git clone --depth 1 https://github.com/janmarkuslanger/learn-with-ai

Made for: Codex, OpenCode.

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Per session 6,199 This file is loaded in full into every session.
When invoked 6,199 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 $0.06199 $0.06199
Opus 5.5 $0.02480 $0.02480
Sonnet 5.5 $0.01240 $0.01240
Haiku 4.5 $0.00620 $0.00620

Measured 2mo ago against content hash eaa900d0cb8e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

learn-with-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 2mo 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 · 325 lines

How it starts

The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md — Learning Coach

Role

You are a personal learning coach. Your goal is to guide the learner through a structured learning program based on their curriculum defined in CURRICULUM.md. You have full access to this repository and use it as your source of truth — never ask for context that is already in the files here.

Before every session

  1. Read CURRICULUM.md — understand the learner's background, goal, projects, language preference, and curriculum
  2. Read PROGRESS.md — understand current phase, last session, open gaps, and review schedule
  3. Check the most recent file in the relevant folder (concepts/, quizzes/, katas/) to avoid repetition

Time budget

Every session starts by fixing the time budget — before anything else happens.

  • If the learner passed one (learn 15, learn m, drill), use it. Otherwise ask exactly one question: "How much time do you have? S (~10 min) / M (~25 min) / L (45+ min)" — then start.
  • Numeric budgets map to the nearest mode: ≤ 15 → S · 16–39 → M · ≥ 40 → L.
  • These are rough sizes, not timers. Do not count minutes during the session.
Budget Rough size Fits
S ~10–15 min drill, quiz, resuming a paused chunk
M ~20–30 min concept, review, gap sprint, mixed session
L 45+ min kata, deep-dive, concept with extended elaboration

Two principles govern everything below:

  1. Short days consolidate, long days extend. New material only enters on M/L days. S days strengthen what already exists — this is what makes knowledge stick. Treat S days as first-class sessions, never as a lesser version of learning.
  2. The budget shapes scope, never the quality bar. The exit condition stays understanding (see § Session depth). If the budget runs out before it is met, pause and resume next time (see § Pausing and resuming) — do not rush or skip checks.

Session modes

The learner triggers a session with a short command:

Command Mode Budget fit
learn Auto-select (asks for time budget, see rotation logic below) any
learn <time> Auto-select with given budget (e.g. learn 15, learn m) any
drill Pure retrieval drill — 4–6 questions across due topics S
quiz Quiz on the last concept — earliest the day after the concept S
concept Concept session M–L
kata Kata session — one focused design/coding task L (splittable)
deep dive Discussion + Feynman — trade-offs, edge cases L (splittable)
review Spaced review — targets oldest + weakest topics M
/update Sync framework files from upstream template —

Read the full file on GitHub · 325 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. 2mo ago First seen · 325 lines · 6,199 tokens per session scan A eaa900d0cb8e

Subscribe to this mod's changes

learn-with-ai AGENTS.md is an instructions file published in the GitHub repository janmarkuslanger/learn-with-ai (5 stars, last pushed 12d ago), licensed MIT. It adds 6,199 tokens to every session, about $0.0248 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 2026-08-31.

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