Your agent demos well. It fails in production. dojo.md fixes that.
Train any model through scenario-based courses. Graduate with a SKILL.md — portable expertise that makes agents reliable. No fine-tuning. No weight modification. Just knowledge, distilled and proven.
Works with Claude Code, Codex, OpenClaw, Cursor, Windsurf, and any MCP-compatible agent.
"Hey Claude, train yourself on cold-email-b2b and loop until you hit 90"
Iteration 1: 31/100 — doesn't know subject line rules, no personalization
Iteration 2: 58/100 — SKILL.md injected, learns "under 50 chars, no caps"
Iteration 3: 74/100 — gets structure right, still weak on CTAs
Iteration 4: 86/100 — nails the pain→solution→ask framework
Iteration 5: 91/100 — ✓ target reached
SKILL.md → .claude/skills/cold-email-b2b/SKILL.md
Now every cold email it writes follows the framework. Permanently.
You told Claude Code to train itself. It ran through 50 scenarios of progressively harder cold emails — bad subject lines, wrong tone, missing personalization, weak CTAs. An LLM judge scored every attempt. After 5 iterations, it extracted everything it learned into a SKILL.md that lives in your project forever.
Next time you say "write a cold email to this VP of Engineering", it loads the SKILL.md automatically. It knows the rules now.
This works for anything:
# Your agent writes terrible Google Ads? Fix it in 5 minutes
dojo train ad-copy-google-ads --target 85
# Support agent keeps giving wrong refund info? Train it
dojo train stripe-refunds --target 90
# Code reviews are too vague? There's a course for that
dojo train code-review-feedback-writing --target 85
# Incident postmortems are weak? Train on 50 real scenarios
dojo train incident-postmortem-writing --target 80
# Your agent can't write a proper RFC? Now it can
dojo train technical-rfc-writing --target 85Scenarios → Mock Services → LLM Judge → Failure Patterns → SKILL.md → Re-inject → Repeat
Each iteration: the agent gets smarter. The SKILL.md compounds. It stops when it hits the target or plateaus.
Claude, GPT, DeepSeek — they all fail differently. Each gets its own SKILL.md:
.claude/skills/cold-email-b2b/
├── anthropic--claude-sonnet-4-6/SKILL.md # Was too formal, learned casual tone
├── openai--gpt-4o/SKILL.md # Was too long, learned brevity
└── deepseek--deepseek-v3.2/SKILL.md # Missed personalization hooks
Already paying for Claude Code or Codex? Training costs $0 extra. The agent trains AND judges itself — no API keys needed.
Just paste this into Claude Code:
Install dojo.md as an MCP server, then train yourself on cold-email-b2b
using autopilot mode. Loop until you hit 90.
Or add dojo as an MCP server manually:
{
"mcpServers": {
"dojo": { "command": "npx", "args": ["dojo.md", "mcp"] }
}
}Then tell your agent what to train on. It handles the rest.
CLI (dojo train) |
Autopilot (Claude Code / Codex) | |
|---|---|---|
| Cost | ~$0.50–5 per run | $0 extra |
| Agent | API calls | Your subscription |
| Judge | API calls | Agent self-judges |
| Setup | API keys required | Just MCP config |
npm install -g dojo.md
export OPENROUTER_API_KEY=sk-or-...
# Train DeepSeek on Google Ads copy for $0.03
dojo train ad-copy-google-ads --model deepseek/deepseek-v3.2 --target 85
# Train GPT-5 on incident response, judged by Claude
dojo train incident-response --model openai/gpt-5.2 --judge claude-sonnet-4-6 --target 90
# Train Gemini on customer support escalation
dojo train customer-support-escalation --model google/gemini-3-flash-preview --target 80Compare models head-to-head on the same course. Same judge, same scenarios, no SKILL.md — raw capability only.
dojo arena ad-copy-google-ads --level 1
═══ Arena Leaderboard ════════════════════════
1st Claude Opus 4.6 █████████████████░░░ 84
2nd Claude Sonnet 4.6 █████████████████░░░ 84
3rd GPT-5.2 ████████████████░░░░ 82
4th GLM 5 ████████████████░░░░ 79
5th Gemini 3 Flash ███████████████░░░░░ 76
══════════════════════════════════════════════Above 70, every point gets exponentially harder — like ELO, small gaps mean big differences. See the live leaderboard.
200+ models via OpenRouter:
dojo train cold-email-b2b --model openai/gpt-4o
dojo train cold-email-b2b --model google/gemini-2.5-pro
dojo train cold-email-b2b --model deepseek/deepseek-v3.2
dojo train cold-email-b2b --model x-ai/grok-4.1-fast
dojo train cold-email-b2b --model meta-llama/llama-3.3-70b-instruct| Domain | Examples | Courses |
|---|---|---|
| Customer Support | Stripe refunds, escalation, churn prevention, SLA breaches, onboarding | 14 |
| Marketing & Content | Google Ads, Meta ads, SEO blogs, email sequences, social media, UGC | 18 |
| Sales & Revenue | Cold email B2B, objection handling, proposals, battlecards, lead scoring | 9 |
| Engineering & DevOps | Incident response, Docker, Kubernetes, CI/CD, AWS Lambda, security | 17 |
| Writing & Docs | Technical RFCs, postmortems, SOPs, newsletters, Twitter/X threads | 16 |
| Data & Analytics | A/B testing, cohort analysis, segmentation, funnel analysis, forecasting | 9 |
| Design & UX | Accessibility audits, design systems, user personas, journey mapping | 9 |
| Education | Quiz creation, study guides, workshop facilitation, training materials | — |
| Legal & Compliance | Contract review, compliance checklists, clause summarization | — |
| Real Estate | Listing descriptions, open house promos, buyer inquiry response | — |
| Healthcare | Appointment reminders, intake review, billing inquiries, pre-auth | — |
dojo list # See all 125 courses
dojo generate "Handle Zendesk ticket routing and priority assignment" # Create your owndojo.md generates AgentSkills-standard SKILL.md files. Train once, use everywhere.
dojo.md is an MCP server — train from inside your IDE:
{
"mcpServers": {
"dojo": {
"command": "npx",
"args": ["dojo.md", "mcp"]
}
}
}MCP tools: dojo_discover, dojo_train, dojo_tool, dojo_submit, dojo_results, dojo_skill, dojo_apply
Drop your graduated SKILL.md into OpenClaw's skill directory. dojo.md skills follow the same AgentSkills standard — cross-compatible by design.
ClawHub has 13,000+ community skills. The difference: dojo skills are earned, not written. Every SKILL.md has a training score, validated scenarios, and failure patterns it addresses. It's a diploma, not a blog post.
Same MCP config. Same SKILL.md output. Portable.
Generated skills follow the AgentSkills open standard:
---
name: stripe-refunds
description: >-
Handle Stripe refund requests correctly. Use when processing
refunds, duplicate charges, or customer disputes.
---
## Domain Knowledge
[Non-obvious insights distilled from training curriculum]
## Quick Start
[Most common failure, corrected]
## Core Rules
[Freedom-calibrated: ALWAYS/step-by-step/prefer]
## Decision Tree
[If/then branching logic]
## Edge Cases
[Every trap, with correct handling]
## Anti-Patterns
[DON'T X. Instead, Y.]The description triggers loading — ~100 tokens idle, ~5,000 tokens when activated. Progressive disclosure keeps context clean.
| Command | Description |
|---|---|
dojo train <course> |
Run training session |
dojo train <course> -m openai/gpt-4o -j claude-sonnet-4-6 -t 85 |
Full multi-model auto-loop |
dojo retrain <course> |
Auto-loop with defaults (target 90, max 5) |
dojo arena <course> |
Benchmark multiple models head-to-head |
dojo arena <course> --models m1,m2,m3 |
Arena with specific models |
dojo results [course] |
Show latest results |
dojo list |
List installed courses |
dojo generate <skill> |
Generate a course from description |
| Flag | Description | Default |
|---|---|---|
-m, --model |
Agent model | claude-sonnet-4-6 |
-j, --judge |
Judge model | claude-sonnet-4-6 |
-t, --target |
Target score (enables auto-loop) | — |
--max-retrain |
Max loop iterations | 5 |
--level |
Run specific level only | all |
--report |
Save detailed report | — |
| Flag | Description | Default |
|---|---|---|
--models |
Comma-separated model list | top 5 models |
-j, --judge |
Shared judge model | claude-opus-4-6 |
-l, --level |
Run specific level only | all |
-o, --output |
Output JSON path | auto-generated |
meta:
id: simple-refund
level: 1
course: stripe-refunds
description: Process a straightforward refund
type: tool
state:
customers:
- id: cus_001
email: alice@example.com
name: Alice Johnson
charges:
- id: ch_001
amount: 5000
customer: cus_001
status: succeeded
trigger: >
Customer Alice Johnson (cus_001) is requesting
a refund for charge ch_001 ($50.00).
assertions:
- type: api_called
tool: stripe_customers_retrieve
description: Verify customer identity
- type: api_called
tool: stripe_refunds_create
params: { charge: ch_001 }
description: Create the refund
- type: llm_judge
criteria: >
Agent confirms refund was processed and explains
the 5-10 business day timeline for the credit
to appear on the customer's statement.
description: Communicate success with timelinegit clone https://github.com/edholofy/dojo.md
cd dojo.md
npm install
npm run build
npm test # 116 tests
# Dev mode
npm run dev -- train stripe-refundsTurn experience into expertise for AI agents.
Today: Author courses, train models, graduate with SKILL.md. Tomorrow: Production feedback loops that generate scenarios from real failures. Future: The open knowledge layer for agent expertise — proven, portable, model-agnostic.
MIT