A prerequisite evaluation protocol that empowers AI agents to inventory their capabilities and analyze task feasibility before commitment.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install self-assessment
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 self-assessment 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 Self-Assessment Protocol?
The Self-Assessment Protocol is a fundamental component of the Openclaw Skills framework, designed to prevent agent failure by enforcing a pre-task capability audit. When an agent receives a task offer, it systematically reviews its internal identity, available skills, and historical memory to determine if it can meet the requirements. This ensures that the agent operates within its expertise boundaries while identifying opportunities for growth through autonomous skill acquisition. By standardizing how agents evaluate their own potential, this protocol creates a more reliable and transparent talent market for multi-agent ecosystems using Openclaw Skills.
Self-Assessment Protocol Use Cases
- Evaluating task suitability upon receiving a TASK_OFFER message to prevent project misalignment.
- Identifying missing capabilities to trigger autonomous skill installation from a central hub.
- Delegating tasks to more qualified agents in a complex multi-agent system.
- Maintaining a living record of agent growth and historical project expertise.
How Self-Assessment Protocol Works
- The protocol triggers immediately upon receipt of a TASK_OFFER message, stopping work before it starts.
- The agent performs a capability inventory by reading IDENTITY.md and scanning local and global Openclaw Skills directories.
- An experience review is conducted by analyzing MEMORY.md and recent activity logs for similar work patterns.
- The agent executes a matching evaluation to identify expertise alignment and specific skill gaps.
- A decision is output as a structured response: ACCEPT, ACCEPT_WITH_CAVEAT, or SUGGEST_REASSIGN.
- If gaps are identified, the agent initiates a search for additional Openclaw Skills to resolve the deficiency autonomously.
Self-Assessment Protocol Setup
To implement this protocol, ensure your agent workspace follows the standard structure. Create the necessary identity and memory files before enabling the skill within your environment.
# Navigate to your agent workspace
cd agent-workspace
# Ensure the skills directory exists and copy the protocol
mkdir -p workspace/skills/
# Initialize IDENTITY.md and MEMORY.md with your agent's profile
touch IDENTITY.md MEMORY.md
Self-Assessment Protocol Data Schema & Taxonomy
The protocol organizes its evaluation data using the following taxonomy to ensure consistent Openclaw Skills performance:
| Data Point | Source | Description |
|---|---|---|
| Core Competencies | IDENTITY.md | Defined role and primary functions of the agent profile. |
| Skill Inventory | workspace/skills/ | List of currently active and globally shared Openclaw Skills. |
| Performance History | MEMORY.md | Record of past task performance, successes, and lessons learned. |
| Skill Gaps | SKILL_GAP JSON | Metadata identifying missing tools required for the specific task at hand. |
Self-Assessment Protocol Advanced Features
- Integration with find-skills utilities for autonomous capability expansion.
- Dynamic capability resumes that update automatically after every successful task completion.
- Sophisticated logic for multi-agent handoffs and targeted reassignment suggestions.
- Nuanced acceptance levels that allow agents to proceed with known limitations while suggesting supplemental resources.
SKILL.md
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