Hermes Dojo

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:

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=== 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:

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🥋 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