sensei AGENTS.md

A guide for curating DojoCode learning resources through its connected tools. It covers coding challenges, contests, learning paths, projects, and assignments, including the files and process needed to create them.

In plain words
What is it for?
Use it when creating or managing coding exercises, tests, starter code, solutions, contests, learning paths, sandbox projects, or assignments for students and groups.
Why use it?
It provides a consistent creation process for educational resources and clarifies how individual challenges fit into courses, competitions, projects, and student assignments.

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/dojo-coder/sensei/agents-md
Clone the repo
git clone --depth 1 https://github.com/dojo-coder/sensei

Made for: Codex, OpenCode.

Per session 35,969 This file is loaded in full into every session.
When invoked 35,969 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 $0.35969 $0.35969
Opus 5 $0.17985 $0.17985
Sonnet 5 $0.07194 $0.07194
Haiku 4.5 $0.03597 $0.03597

Measured 2d ago against content hash 4c839d27251a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sensei 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 2d 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 · 2,513 lines

How it starts

The opening of the file, as written. The whole thing — 2,513 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DojoCode Resource Curator - Agent Guidelines

This file provides comprehensive context for AI curators on how to create, structure, and manage all DojoCode resource types by calling the dojocode MCP tools: coding challenges, contests, learning paths, projects, and assignments.

The document is organized in parts. Read the Session start section first (it applies to every workflow), then jump to the part for the resource you are creating:

  • Part 1 — Challenges: full file-based authoring (starter code, solutions, tests), packaging, upload, and test execution. This is the largest part and the foundation the other resources build on. Starts at "Challenge Creation Process" below.
  • Part 2 — Contests: assemble existing challenges into a timed competition (create_contest).
  • Part 3 — Learning Paths: assemble existing challenges into a guided, lesson-by-lesson path (create_learning_path).
  • Part 4 — Projects: create a free-form sandbox project from a template and a file tree (create_project).
  • Part 5 — Assignments: assign a learning path or a challenge to students / groups (create_assignment).

Organizations / workspace. Business work (contests, assignments, student groups, and the content that belongs to them) is scoped to an organization workspace. Establish the active workspace at session start with get_my_organizationsselect_organization before any org-scoped operation — see Organization workspace under Session start.

Each resource type has its own top-level folder in this repo (challenges/, contests/, learning-paths/, projects/, assignments/) with a committed *-example descriptor inside it, while the full library of challenge template samples lives under challenges/challenge-samples/. Contests, learning paths, and assignments reference existing challenges (and learning paths) by their platform _id — always resolve those IDs first via get_my_challenges / get_challenges / get_my_learning_paths before calling a create tool. Parts 2–5 and their MCP workflows live at the end of this file, after the challenge workflows.

Read the full file on GitHub · 2,513 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. 2d ago First seen · 2,513 lines · 35,969 tokens per session scan A 4c839d27251a

Subscribe to this mod's changes

sensei AGENTS.md is an instructions file published in the GitHub repository dojo-coder/sensei (2 stars, last pushed 1mo ago), licensed MIT. It adds 35,969 tokens to every session, about $0.1798 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 2026-08-31.

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