A resilient self-healing skill that empowers AI agents to diagnose, retry, and learn from execution failures autonomously.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memory-self-heal
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 memory-self-heal 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 Memory Self-Heal?
The Memory Self-Heal skill acts as a robust resilience layer for autonomous agents, specifically designed to minimize human intervention when execution stalls or fails. By implementing a systematic self-correction loop, this tool allows agents to analyze their own errors and search for historical solutions within the local environment. It is an essential component for anyone building reliable Openclaw Skills that need to operate in unpredictable environments.
This skill synthesizes core value by ensuring that agents don't just fail when encountering a blocker; instead, they classify the failure, scan historical logs for evidence, and apply a tiered recovery strategy. Whether dealing with API timeouts, authentication walls, or context window limits, Memory Self-Heal provides the logic necessary to maintain progress and provide verifiable results.
Memory Self-Heal Use Cases
- Automatically recovering from repeated tool call failures or argument mismatches during complex coding tasks.
- Resolving authentication or configuration errors by autonomously checking environment variables and memory logs.
- Preventing agent stalls when no new artifacts are produced across multiple execution cycles.
- Validating task completion to ensure that artifacts actually exist before the agent claims the job is done within Openclaw Skills.
How Memory Self-Heal Works
- Monitoring: The skill monitors the execution stream for specific failure signatures such as timeouts, syntax errors, or resource limits.
- Evidence Scanning: It performs a prioritized scan across memory directories, task queues, and runtime logs to find previous successful resolutions.
- Classification: The failure is categorized into specific classes like syntax_or_args, auth_or_config, or resource_limit to determine the best response.
- Tiered Recovery: The agent executes a 3-tier policy, starting with a direct fix based on memory, moving to a safe fallback tool, and finally escalating to the user with a specific unblock request.
- Writeback: Once resolved or blocked, the skill records the signature and the successful rule back to memory to optimize future Openclaw Skills performance.
Memory Self-Heal Setup
To integrate this resilience layer into your workflow, ensure your workspace is configured for persistent memory storage.
# Navigate to your agent workspace
cd your-agent-project
# Ensure the required directories for memory persistence exist
mkdir -p memory tasks
# Configure the agent to utilize memory-self-heal as a core resilience skill
# Note: Refer to your specific agent framework for skill registration
Memory Self-Heal Data Schema & Taxonomy
The skill organizes its recovery data and learning history using a structured format to ensure high-speed retrieval. This data is typically stored within the memory/ directory.
| Attribute | Description |
|---|---|
| Signature | The normalized error pattern or exception string encountered. |
| Class | The specific failure category (e.g., network_or_reachability, ui_login_wall). |
| Recovery Attempts | A log of the actions taken (Direct Fix, Safe Fallback) and their outcomes. |
| Reusable Rule | A concise, one-line instruction derived from the fix for future use in Openclaw Skills. |
| Artifact Ref | A path or URL to the verifiable evidence produced after a successful heal. |
Memory Self-Heal Advanced Features
- Multi-tier recovery logic that prioritizes deterministic fixes over broad speculative retries.
- Portable evidence scanning that supports both PowerShell and POSIX shell environments for cross-platform Openclaw Skills support.
- Intelligent failure classification that prevents agents from looping infinitely on unrecoverable auth or UI walls.
- Standardized completion contracts that enforce the presence of readable artifacts before a task is marked as finished.
- Memory writeback templates that turn every failure into a long-term learning opportunity for the agent.
SKILL.md
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