A sophisticated self-evolution framework for AI agents that transforms task execution into reusable skills and long-term experience.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install hermes-learning-loop2
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install hermes-learning-loop2 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Hermes Learning Loop?
Hermes Learning Loop is a high-level enhancement for Openclaw Skills inspired by the Nous Research Hermes Agent philosophy. It establishes a disciplined, conscious system for experience extraction, moving away from black-box automation toward a transparent 'Experience → Skill → Improvement → Precipitation' cycle. This skill ensures that every task performed serves as a stepping stone for future efficiency, allowing the agent to grow more intelligent and faster with every interaction.
By integrating this skill, users enable their agents to conduct autonomous post-task reviews, manage hierarchical memory structures, and implement error-driven self-correction. It bridges the gap between raw LLM capabilities and procedural knowledge, making the AI a truly adaptive partner in complex development and research environments.
Hermes Learning Loop Use Cases
- Automating post-task reviews after complex multi-step tool invocations to identify optimization points.
- Extracting standardized workflows from successful error recoveries or user-corrected paths.
- Managing long-term memory to prevent context bloat while retaining critical cross-session facts.
- Periodically auditing and updating obsolete skills during system idle times (Heartbeat cycles).
- Transforming repetitive operational patterns into formal, independent skill modules.
How Hermes Learning Loop Works
- Task Completion Trigger: The system monitors for complex tasks (5+ tool calls), successful error resolutions, or direct user corrections.
- Rapid Review: The agent evaluates success factors, identifies steps for standardization, and determines if the experience is worth permanent storage.
- Skill Precipitation: Key insights are distilled into structured logs within the memory directory, detailing scenarios, core steps, and potential pitfalls.
- Hierarchical Storage: Data is funneled through a three-tier architecture: Immediate (Session Context), Working (MEMORY.md), and Experience (Skills and Daily Logs).
- Self-Correction Loop: Errors trigger a back-propagation logic where the agent updates existing knowledge and notes to avoid repeating mistakes.
Hermes Learning Loop Setup
To integrate Hermes Learning Loop into your environment, ensure your workspace follows the standard memory structure and install via the CLI:
# Create necessary directories
mkdir -p memory/skills
# Add the skill to your Openclaw configuration
openclaw install hermes-learning-loop
# Initialize the memory management file if not present
touch MEMORY.md
Configure your agent to trigger the review workflow after complex sessions by adding the skill reference to your project manifest.
Hermes Learning Loop Data Schema & Taxonomy
The skill organizes data across three primary levels to optimize token usage and retrieval speed:
| Level | Component | Storage Format | Maintenance Strategy |
|---|---|---|---|
| Level 0 | Index Layer | MEMORY.md | Critical facts updated every 3-5 days |
| Level 1 | Summary Layer | memory/YYYY-MM-DD.md | Daily logs of valued operations and decisions |
| Level 2 | Full Layer | skills/*.md | Formal procedural knowledge for specific tasks |
Hermes Learning Loop Advanced Features
- Progressive Information Disclosure: Only loads full SKILL.md files when explicitly matched to a task to save tokens.
- Error-Driven Back-propagation: Automatically updates relevant notes when a universal methodology error is corrected by the user.
- Heartbeat Skill Auditing: Scans daily logs every 3 days to identify patterns that should be promoted to independent skills.
- Plan B Archive: Records successful alternative paths after tool failures as fallback strategies for future autonomous execution.
SKILL.md
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