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sickn33
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this,"...
Python
Updated 6/6/2026
Agent compatibility
Compatibility has not been reviewed for this listing yet. Check the publisher documentation before installing.
Installation
npx skills add https://github.com/Yonkoo11/hermes-dojo --skill rootReview source code and installation permissions before adding third-party tools to an agent.
hermes-dojo is organized in the Analytics category. Compare its source, install method, and compatibility before adding it to your workflow.
Third-party agent tools may access source code, credentials, or browser sessions. Read the source documentation and use the minimum permissions needed.
npx skills add https://github.com/Yonkoo11/hermes-dojo --skill rootSKILL.md
---
name: hermes-dojo
description: >
Continuous self-improvement system for Hermes Agent. Analyzes your past sessions
to find recurring failures and skill gaps, then automatically creates or patches
skills and runs self-evolution to fix them. Set it to run overnight and wake up
to a better agent. Use /dojo to start.
version: 1.0.0
license: MIT
metadata:
author: yonko
hermes:
tags: [self-improvement, self-evolution, analytics, meta-agent]
category: agent-improvement
requires_toolsets: [terminal]
allowed-tools: Bash(python3:*) Read Write skill_manage delegate_task session_search memory
---
## Overview
Hermes Dojo is your agent's training gym. It reads your past sessions, finds where
the agent struggles, creates or improves skills to fix those weaknesses, and tracks
improvement over time.
The core loop: **measure → identify weakness → fix → evolve → verify → report**
## Commands
- `/dojo` or `/dojo analyze` — Analyze recent sessions for failure patterns
- `/dojo improve` — Fix the top weaknesses (patch skills + run self-evolution)
- `/dojo report` — Show current performance metrics and improvement history
- `/dojo history` — Show learning curve over time
- `/dojo auto` — Set up overnight cron: analyze + improve + report at 6am
- `/dojo status` — Quick summary of agent health
## Workflow
### Step 1: Analyze
Run `python3 ~/.hermes/skills/hermes-dojo/scripts/monitor.py` to scan recent sessions.
This reads `~/.hermes/state.db` and produces a JSON report with:
- Per-tool success/failure rates
- Error patterns (grouped by tool and error type)
- User correction signals (messages containing "no,", "wrong", "I meant", "not what I")
- Skill gap detection (repeated manual tasks with no skill)
- Session-level metrics (tool calls per session, retry patterns)
Present the results as a clear dashboard:
```
=== Hermes Dojo Analysis ===
Sessions analyzed: 23 (last 7 days)
Total tool calls: 156
Overall success rate: 78%
Top Weaknesses:
1. terminal_run: 73% success (12 failures) — common error: "command not found"
2. web_extract: 81% success (4 failures) — common error: "timeout"
3. No skill for: CSV parsing (requested 4 times)
User Corrections Detected: 7
- 3x wrong file path
- 2x wrong command syntax
- 2x misunderstood request
```
### Step 2: Improve
For each identified weakness, decide the fix:
**A) Existing skill fails → patch it:**
1. Read the current skill's SKILL.md
2. Analyze the failure patterns from Step 1
3. Use `skill_manage` with action "patch" to add error handling, better instructions, or edge case coverage
4. Log the change
**B) No skill exists for a recurring need → create one:**
1. Analyze the session patterns where this capability was needed
2. Use `skill_manage` with action "create" to make a new skill
3. Include specific instructions based on what worked in past sessions
4. Log the creation
**C) Skill exists but needs deeper improvement → run self-evolution:**
1. Run: `cd ~/.hermes/hermes-agent-self-evolution && .venv/bin/python3 -m evolution.skills.evolve_skill --skill <name> --hermes-repo ~/.hermes --iterations 5 --eval-source synthetic`
2. This uses GEPA to analyze execution traces and propose targeted improvements
3. Review the evolution output — accept if score improved
4. Log before/after scores
### Step 3: Verify
After improvements:
1. Re-run `monitor.py` to compute new metrics
2. Compare before/after success rates
3. If improvement < 5%, flag for manual review
4. Store results in metrics history
### Step 4: Report
Run `python3 ~/.hermes/skills/hermes-dojo/scripts/reporter.py` to generate a report.
Format for Telegram delivery:
```
🥋 Hermes Dojo — Overnight Report
📊 Analyzed: 23 sessions, 156 tool calls
📈 Overall: 78% → 85% (+7%)
✅ Improved:
• terminal_run: 73% → 96% (added PATH check)
• web_extract: 81% → 95% (added retry + timeout)
🆕 New skill created:
• csv-handler: for CSV parsing you keep asking about
⚠️ Still working on:
• File path resolution (need more examples)
📉 Learning curve: 71% → 78% → 85% (3 days)
```
### Step 5: Track
Run `python3 ~/.hermes/skills/hermes-dojo/scripts/tracker.py save` to persist metrics.
Run `python3 ~/.hermes/skills/hermes-dojo/scripts/tracker.py history` to show the learning curve.
## Setting Up Overnight Cron
When the user says `/dojo auto`:
Set up a cron job that runs at 6:00 AM daily:
"Run /dojo analyze, then /dojo improve on the top 3 weaknesses, then send the report to my home channel."
This makes the agent literally improve while you sleep.
## Important Notes
- Never modify bundled skills (in hermes-agent/skills/). Only modify user skills in ~/.hermes/skills/
- Self-evolution requires ~/.hermes/hermes-agent-self-evolution to be cloned
- Metrics are stored in ~/.hermes/skills/hermes-dojo/data/metrics.json
- Each analysis run is timestamped so you can track improvement over days/weeks
skill
jinchenma94
>