A neural-inspired conflict detection and error monitoring module that acts as a cognitive filter for AI agents.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install anterior-cingulate-memory
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 anterior-cingulate-memory 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 Anterior Cingulate Memory?
The anterior cingulate memory skill serves as a sophisticated monitoring layer for autonomous agents, drawing inspiration from the neurobiology of the human brain's anterior cingulate cortex. As a critical component of the AI Brain series within the Openclaw Skills ecosystem, this module focuses on detecting contradictions in information and providing pre-conscious warning signals when logical states appear inconsistent.
By integrating this skill, developers can equip their agents with the ability to recognize uncertainty and track errors over time. It functions as a high-level cognitive monitor, ensuring that agents do not blindly follow conflicting instructions or process hallucinated data without first evaluating the logical integrity of their current workflow.
Anterior Cingulate Memory Use Cases
- Identifying logical contradictions within complex, multi-turn AI conversations.
- Detecting when an agent's planned action conflicts with established constraints or safety protocols.
- Triggering uncertainty alerts when high-confidence predictions are met with contradictory evidence.
- Monitoring agent performance to track and learn from repetitive mistakes in reasoning tasks.
How Anterior Cingulate Memory Works
- The skill continuously monitors the stream of incoming data and the agent's internal reasoning steps.
- It cross-references new information against existing memory buffers to identify potential conflicts or logical gaps.
- Upon detecting a discrepancy, the skill generates a conflict signal that serves as a warning for the agent's core controller.
- The agent utilizes this feedback to adjust its confidence levels or request human intervention before proceeding.
- Conflict data and error logs are stored to refine future detection accuracy and facilitate long-term learning.
Anterior Cingulate Memory Setup
The anterior cingulate memory skill is designed for environments running Darwin or Linux. As part of the Openclaw Skills framework, it can be initialized via the CLI.
# Install the skill from the OpenClaw registry
openclaw install anterior-cingulate-memory
Ensure that you have other components of the AI Brain series installed if you wish to enable cross-functional memory reinforcement.
Anterior Cingulate Memory Data Schema & Taxonomy
This skill organizes conflict metadata and error logs to provide a clear audit trail of agent uncertainty. It utilizes the following data structure to interface with other Openclaw Skills:
| Attribute | Description | Data Type |
|---|---|---|
| conflict_id | Unique identifier for the detected error or inconsistency | String |
| source_ref | Reference link to related hippocampus or amygdala memory entries | UUID |
| confidence_score | Quantified degree of certainty regarding the detected conflict | Float |
| conflict_type | Categorization of the error (e.g., logical, constraint, or factual) | Enum |
Anterior Cingulate Memory Advanced Features
- Seamless integration with the hippocampus skill for context-aware error monitoring.
- Configurable uncertainty thresholds allowing developers to fine-tune the agent's cautiousness.
- Pre-conscious warning signals that flag potential failures before the agent commits to an action.
- Advanced error tracking and mistake analysis to support reinforcement learning workflows.
SKILL.md
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