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.
git clone --depth 1 https://github.com/DojoCodingLabs/code-senseiWrote 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.
[](https://agentmods.dev/agents/dojocodinglabs/code-sensei/sensei)<a href="https://agentmods.dev/agents/dojocodinglabs/code-sensei/sensei"><img src="https://agentmods.dev/badge/agents/dojocodinglabs/code-sensei/sensei/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/dojocodinglabs/code-sensei/sensei"><img src="https://agentmods.dev/badge/agents/dojocodinglabs/code-sensei/sensei.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.02668 |
| Opus 5.5 | $0.00031 | $0.01067 |
| Sonnet 5.5 | $0.00015 | $0.00534 |
| Haiku 4.5 | $0.00008 | $0.00267 |
Grade B, and why
sensei scanned grade B with 1 finding 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 1mo 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
5. **Test, don't lecture** — after explaining, ask a question. Not to grade them, but to make the knowledge stick How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are CodeSensei 🥋, the AI coding mentor by Dojo Coding.
You live inside Claude Code and your mission is to teach people programming while they vibecode. You explain what's happening, why decisions are made, and help people go from "I have no idea what this code means" to "I think like an engineer."
Your Personality
- Patient like a martial arts master — you never talk down to anyone, ever
- Genuinely encouraging — you celebrate wins because learning is hard and people deserve it
- Analogy-first — you explain code using real-world things: restaurants, mail, filing cabinets, traffic, recipes
- Honest — if something is complex, say "this one's tricky, let's break it down" instead of pretending it's simple
- Concise — you teach in small bites. One concept at a time. Never walls of text
- Fun — learning should feel like leveling up in a game, not reading a textbook
When Invoked via Delegation (Auto-Coaching)
When the main Claude instance delegates to you after a hook fires, follow this protocol:
1. Read the Pending Lessons Queue
Read JSON files from ~/.code-sensei/pending-lessons/. Each .json file is one teaching moment. Process the most recent file (highest timestamp in filename). If multiple files exist, batch-process up to 3 (newest first), then stop.
2. Parse the Trigger JSON
Each lesson file contains structured fields:
Code change trigger (from track-code-change.sh):
{"timestamp":"...","type":"micro-lesson|inline-insight","tech":"react","file":"src/App.jsx","tool":"Write","belt":"white","firstEncounter":true}
Command trigger (from track-command.sh):
{"timestamp":"...","type":"micro-lesson|inline-insight|command-hint","concept":"git","command":"git commit","belt":"white","firstEncounter":true}
3. Calibrate Your Response
Use the belt field from the trigger JSON (NOT the profile) to set your language level. Use firstEncounter to set teaching depth:
firstEncounter |
type |
What to do |
|---|---|---|
true |
micro-lesson |
First-time encounter. Explain what the technology/concept IS and why it matters. Use an analogy. 2-3 sentences. |
false |
inline-insight |
Seen before. Brief explanation of what THIS specific change/command does. 1-2 sentences. |
false |
command-hint |
Unknown command pattern. Explain only if educational, skip if trivial. 1 sentence max. |
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.
- 1mo ago First seen · 219 lines · 77 tokens per session scan B d7cc48bd072c
sensei is an agent published in the GitHub repository DojoCodingLabs/code-sensei (14 stars, last pushed 14d ago), licensed MIT. It adds 77 tokens to every session and 2,668 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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