bdi-mental-states
Allows the agent to model mental states like beliefs and intentions. Useful for creating cognitive agents and integrating neuro-symbolic AI.
What it does
This tool helps create 'mental state' models for AI agents. It transforms external information into beliefs, desires, and intentions, allowing the AI to 'think' and act more intelligently and explainably.
When to use it
- ✓When you want an AI to understand what it 'knows' (beliefs).
- ✓To define what the AI 'wants' to achieve (desires).
- ✓To determine what the AI 'decides' to do (intentions).
Example of what to ask your AI
“Transform this text about the weather into beliefs, desires, and intentions for an AI agent.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool uses a pattern called BDI (Belief-Desire-Intention) to organize the AI's 'thinking'. It takes real-world data and converts it into mental states that the AI can use to make decisions.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill bdi-mental-statesOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/bdi-mental-states/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
BDI Mental State Modeling
Transform external RDF context into agent mental states (beliefs, desires, intentions) using formal BDI ontology patterns. This skill enables agents to reason about context through cognitive architecture, supporting deliberative reasoning, explainability, and semantic interoperability within multi-agent systems.
When to Use
Activate this skill when:
- Processing external RDF context into agent beliefs about world states
- Modeling rational agency with perception, deliberation, and action cycles
- Enabling explainability through traceable reasoning chains
- Implementing BDI frameworks (SEMAS, JADE, JADEX)
- Augmenting LLMs with formal cognitive structures (Logic Augmented Generation)
- Coordinating mental states across multi-agent platforms
- Tracking temporal evolution of beliefs, desires, and intentions
- Linking motivational states to action plans
Core Concepts
Mental Reality Architecture
Mental States (Endurants): Persistent cognitive attributes
Belief: What the agent believes to be true about the worldDesire: What the agent wishes to bring aboutIntention: What the agent commits to achieving
Mental Processes (Perdurants): Events that modify mental states
BeliefProcess: Forming/updating beliefs from perceptionDesireProcess: Generating desires from beliefsIntentionProcess: Committing to desires as actionable intentions
Cognitive Chain Pattern
:Belief_store_open a bdi:Belief ;
rdfs:comment "Store is open" ;
bdi:motivates :Desire_buy_groceries .
:Desire_buy_groceries a bdi:Desire ;
rdfs:comment "I desire to buy groceries" ;
bdi:isMotivatedBy :Belief_store_open .
:Intention_go_shopping a bdi:Intention ;
rdfs:comment "I will buy groceries" ;
bdi:fulfils :Desire_buy_groceries ;
bdi:isSupportedBy :Belief_store_open ;
bdi:specifies :Plan_shopping .
World State Grounding
Mental states reference structured configurations of the environment:
:Agent_A a bdi:Agent ;
bdi:perceives :WorldState_WS1 ;
bdi:hasMentalState :Belief_B1 .
:WorldState_WS1 a bdi:WorldState ;
rdfs:comment "Meeting scheduled at 10am in Room 5" ;
bdi:atTime :TimeInstant_10am .
:Belief_B1 a bdi:Belief ;
bdi:refersTo :WorldState_WS1 .
Goal-Directed Planning
Intentions specify plans that address goals through task sequences:
:Intention_I1 bdi:specifies :Plan_P1 .
:Plan_P1 a bdi:Plan ;
bdi:addresses :Goal_G1 ;
bdi:beginsWith :Task_T1 ;
bdi:endsWith :Task_T3 .
:Task_T1 bdi:precedes :Task_T2 .
:Task_T2 bdi:precedes :Task_T3 .
T2B2T Paradigm
Triples-to-Beliefs-to-Triples implements bidirectional flow between RDF knowledge graphs and internal mental states:
Phase 1: Triples-to-Beliefs
# External RDF context triggers belief formation
:WorldState_notification a bdi:WorldState ;
rdfs:comment "Push notification: Payment request $250" ;
bdi:triggers :BeliefProcess_BP1 .
:BeliefProcess_BP1 a bdi:BeliefProcess ;
bdi:generates :Belief_payment_request .
Phase 2: Beliefs-to-Triples
# Mental deliberation produces new RDF output
:Intention_pay a bdi:Intention ;
bdi:specifies :Plan_payment .
:PlanExecution_PE1 a bdi:PlanExecution ;
bdi:satisfies :Plan_payment ;
bdi:bringsAbout :WorldState_payment_complete .
Notation Selection by Level
| C4 Level | Notation | Mental State Representation |
|---|---|---|
| L1 Context | ArchiMate | Agent boundaries, external perception sources |
| L2 Container | ArchiMate | BDI reasoning engine, belief store, plan executor |
| L3 Component | UML | Mental state managers, process handlers |
| L4 Code | UML/RDF | Belief/Desire/Intention classes, ontology instances |
Justification and Explainability
Mental entities link to supporting evidence for traceable reasoning:
:Belief_B1 a bdi:Belief ;
bdi:isJustifiedBy :Justification_J1 .
:Justification_J1 a bdi:Justification ;
rdfs:comment "Official announcement received via email" .
:Intention_I1 a bdi:Intention ;
bdi:isJustifiedBy :Justification_J2 .
:Justification_J2 a bdi:Justification ;
rdfs:comment "Location precondition satisfied" .
Temporal Dimensions
Mental states persist over bounded time periods:
:Belief_B1 a bdi:Belief ;
bdi:hasValidity :TimeInterval_TI1 .
:TimeInterval_TI1 a bdi:TimeInterval ;
…
FAQ
What are 'beliefs', 'desires', and 'intentions'?
Beliefs are what the AI thinks is true. Desires are what it wishes would happen. Intentions are the actions it commits to performing to satisfy its desires.
Why is this useful?
It helps the AI reason more logically, explain its decisions, and interact better with other systems or agents.
Do I need to know programming to use this?
No, this tool is designed for simple use, focusing on what you want the AI to understand and do.
Details
Original description by the author
This skill should be used when the user asks to "model agent mental states", "implement BDI architecture", "create belief-desire-intention models", "transform RDF to beliefs", "build cognitive agent", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.
Popularity
- GitHub stars
- 48k
- Ranking position
- #2407
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
agent-architecture-audit
Makes your agent check if everything is working correctly. It finds and fixes problems in AI systems, ensuring they perform as expected.
affaan-m/ECC
agent-eval
Helps your agent compare different AI coding tools. It measures which one is faster, cheaper, and more reliable for specific tasks.
affaan-m/ECC
agent-harness-construction
Makes your agent better understand how to perform tasks. It optimizes the tools and instructions the agent uses to increase its chances of success.
affaan-m/ECC