Agent skill

Bdi Mental States

by sickn33 in sickn33/agentic-awesome-skills

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…

MITAuto-check passedKnowledge Management

Install Bdi Mental States

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill bdi-mental-states -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills bdi-mental-states --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bdi-mental-states .claude/skills/bdi-mental-states && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bdi-mental-states
GitHub stars
47k
Used in
2 other repos
Token cost
~2.6k tokens
SKILL.md length
701 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

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…

  • Works in 10 steps: Model world states as configurations… → Distinguish endurants (persistent mental… → Treat goals as descriptions rather than… → …
  • Asks to model agent mental states
  • SKILL.md covers When to Use, Core Concepts, T2B2T Paradigm and Notation Selection by Level, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Bdi Mental States is an agent skill from sickn33/agentic-awesome-skills. 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.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

When your agent uses it

  • Asks to model agent mental states
  • Implement BDI architecture
  • Create belief-desire-intention models
  • Transform RDF to beliefs

Example prompts

  • “model agent mental states”
  • “implement BDI architecture”
  • “create belief-desire-intention models”
  • “/bdi-mental-states”

Requirements

  • Python 3

Workflow steps

10 steps, taken from the first numbered list in SKILL.md.

  1. Model world states as configurations independent of agent perspectives, providing referential substrate for mental states.
  2. Distinguish endurants (persistent mental states) from perdurants (temporal mental processes), aligning with DOLCE ontology.
  3. Treat goals as descriptions rather than mental states, maintaining separation between cognitive and planning layers.
  4. Use hasPart relations for meronymic structures enabling selective belief updates.
  5. Associate every mental entity with temporal constructs via atTime or hasValidity.
  6. Use bidirectional property pairs (motivates/isMotivatedBy, generates/isGeneratedBy) for flexible querying.
  7. Link mental entities to Justification instances for explainability and trust.
  8. Implement T2B2T through: (1) translate RDF to beliefs, (2) execute BDI reasoning, (3) project mental states back to RDF.
  9. Define existential restrictions on mental processes (e.g., BeliefProcess ⊑ ∃generates.Belief).
  10. Reuse established ODPs (EventCore, Situation, TimeIndexedSituation, BasicPlan, Provenance) for interoperability.

What it can do on your machine

Read from SKILL.md and the folder at commit 1c7bdea. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are turtle, sparql, python and prolog).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bdi Mental States loads about 2.6k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 701 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sickn33/agentic-awesome-skills at commit 1c7bdea, republished under its MIT licence (© sickn33). 701 words, ~2,614 tokens.

Download SKILL.mdSave it as .claude/skills/bdi-mental-states/SKILL.md (or your agent's skills folder).
name
bdi-mental-states
description
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.
risk
critical
source
community
date_added
2026-09-04

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 world
  • Desire: What the agent wishes to bring about
  • Intention: What the agent commits to achieving

Mental Processes (Perdurants): Events that modify mental states

  • BeliefProcess: Forming/updating beliefs from perception
  • DesireProcess: Generating desires from beliefs
  • IntentionProcess: Committing to desires as actionable intentions
Cognitive Chain Pattern
turtle
: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:

turtle
: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:

turtle
: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

turtle
# 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

turtle
# 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 LevelNotationMental State Representation
L1 ContextArchiMateAgent boundaries, external perception sources
L2 ContainerArchiMateBDI reasoning engine, belief store, plan executor
L3 ComponentUMLMental state managers, process handlers
L4 CodeUML/RDFBelief/Desire/Intention classes, ontology instances

Justification and Explainability

Mental entities link to supporting evidence for traceable reasoning:

turtle
: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:

turtle
:Belief_B1 a bdi:Belief ;
    bdi:hasValidity :TimeInterval_TI1 .

:TimeInterval_TI1 a bdi:TimeInterval ;
    bdi:hasStartTime :TimeInstant_9am ;
    bdi:hasEndTime :TimeInstant_11am .

Query mental states active at specific moments:

sparql
SELECT ?mentalState WHERE {
    ?mentalState bdi:hasValidity ?interval .
    ?interval bdi:hasStartTime ?start ;
              bdi:hasEndTime ?end .
    FILTER(?start <= "2025-01-04T10:00:00"^^xsd:dateTime && 
           ?end >= "2025-01-04T10:00:00"^^xsd:dateTime)
}

Compositional Mental Entities

Complex mental entities decompose into constituent parts for selective updates:

turtle
:Belief_meeting a bdi:Belief ;
    rdfs:comment "Meeting at 10am in Room 5" ;
    bdi:hasPart :Belief_meeting_time , :Belief_meeting_location .

# Update only location component
:BeliefProcess_update a bdi:BeliefProcess ;
    bdi:modifies :Belief_meeting_location .

Integration Patterns

Logic Augmented Generation (LAG)

Augment LLM outputs with ontological constraints:

python
def augment_llm_with_bdi_ontology(prompt, ontology_graph):
    ontology_context = serialize_ontology(ontology_graph, format='turtle')
    augmented_prompt = f"{ontology_context}\n\n{prompt}"
    
    response = llm.generate(augmented_prompt)
    triples = extract_rdf_triples(response)
    
    is_consistent = validate_triples(triples, ontology_graph)
    return triples if is_consistent else retry_with_feedback()
SEMAS Rule Translation

Map BDI ontology to executable production rules:

prolog
% Belief triggers desire formation
[HEAD: belief(agent_a, store_open)] / 
[CONDITIONALS: time(weekday_afternoon)] » 
[TAIL: generate_desire(agent_a, buy_groceries)].

% Desire triggers intention commitment
[HEAD: desire(agent_a, buy_groceries)] / 
[CONDITIONALS: belief(agent_a, has_shopping_list)] » 
[TAIL: commit_intention(agent_a, buy_groceries)].

Guidelines

  1. Model world states as configurations independent of agent perspectives, providing referential substrate for mental states.

  2. Distinguish endurants (persistent mental states) from perdurants (temporal mental processes), aligning with DOLCE ontology.

  3. Treat goals as descriptions rather than mental states, maintaining separation between cognitive and planning layers.

  4. Use hasPart relations for meronymic structures enabling selective belief updates.

  5. Associate every mental entity with temporal constructs via atTime or hasValidity.

  6. Use bidirectional property pairs (motivates/isMotivatedBy, generates/isGeneratedBy) for flexible querying.

  7. Link mental entities to Justification instances for explainability and trust.

  8. Implement T2B2T through: (1) translate RDF to beliefs, (2) execute BDI reasoning, (3) project mental states back to RDF.

  9. Define existential restrictions on mental processes (e.g., BeliefProcess ⊑ ∃generates.Belief).

  10. Reuse established ODPs (EventCore, Situation, TimeIndexedSituation, BasicPlan, Provenance) for interoperability.

Show full SKILL.md (247 more words)Show less

Competency Questions

Validate implementation against these SPARQL queries:

sparql
# CQ1: What beliefs motivated formation of a given desire?
SELECT ?belief WHERE {
    :Desire_D1 bdi:isMotivatedBy ?belief .
}

# CQ2: Which desire does a particular intention fulfill?
SELECT ?desire WHERE {
    :Intention_I1 bdi:fulfils ?desire .
}

# CQ3: Which mental process generated a belief?
SELECT ?process WHERE {
    ?process bdi:generates :Belief_B1 .
}

# CQ4: What is the ordered sequence of tasks in a plan?
SELECT ?task ?nextTask WHERE {
    :Plan_P1 bdi:hasComponent ?task .
    OPTIONAL { ?task bdi:precedes ?nextTask }
} ORDER BY ?task

Anti-Patterns

  1. Conflating mental states with world states: Mental states reference world states, they are not world states themselves.

  2. Missing temporal bounds: Every mental state should have validity intervals for diachronic reasoning.

  3. Flat belief structures: Use compositional modeling with hasPart for complex beliefs.

  4. Implicit justifications: Always link mental entities to explicit justification instances.

  5. Direct intention-to-action mapping: Intentions specify plans which contain tasks; actions execute tasks.

Integration

  • RDF Processing: Apply after parsing external RDF context to construct cognitive representations
  • Semantic Reasoning: Combine with ontology reasoning to infer implicit mental state relationships
  • Multi-Agent Communication: Integrate with FIPA ACL for cross-platform belief sharing
  • Temporal Context: Coordinate with temporal reasoning for mental state evolution
  • Explainable AI: Feed into explanation systems tracing perception through deliberation to action
  • Neuro-Symbolic AI: Apply in LAG pipelines to constrain LLM outputs with cognitive structures

References

See references/ folder for detailed documentation:

  • bdi-ontology-core.md - Core ontology patterns and class definitions
  • rdf-examples.md - Complete RDF/Turtle examples
  • sparql-competency.md - Full competency question SPARQL queries
  • framework-integration.md - SEMAS, JADE, LAG integration patterns

Primary sources:

  • Zuppiroli et al. "The Belief-Desire-Intention Ontology" (2025)
  • Rao & Georgeff "BDI agents: From theory to practice" (1995)
  • Bratman "Intention, plans, and practical reason" (1987)

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/bdi-mental-states of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit 1c7bdea

Used in 2 other repositories

We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 9, 2026.

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Questions about Bdi Mental States

What does Bdi Mental States do?

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…. Bdi Mental States is an agent skill from sickn33/agentic-awesome-skills. 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.

When should I use Bdi Mental States?

Bdi Mental States fits situations like: asks to model agent mental states; implement BDI architecture; create belief-desire-intention models; transform RDF to beliefs.

How do I install Bdi Mental States in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill bdi-mental-states -a claude-code`. Or copy the skill folder (skills/bdi-mental-states in sickn33/agentic-awesome-skills) into .claude/skills/bdi-mental-states in your project. Claude Code loads it when a task matches its description.

How do I install Bdi Mental States in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill bdi-mental-states -a codex`. Or copy the skill folder (skills/bdi-mental-states in sickn33/agentic-awesome-skills) into .agents/skills/bdi-mental-states in your project. Codex loads it when a task matches its description.

Can I use Bdi Mental States in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill bdi-mental-states -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bdi-mental-states, .gemini/skills/bdi-mental-states, .github/skills/bdi-mental-states and .opencode/skills/bdi-mental-states in your project.

What does Bdi Mental States need to run?

SKILL.md names no scripts, command-line tools or credentials: Bdi Mental States is instructions for the agent only. Our summary lists: Python 3.

Does Bdi Mental States access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bdi Mental States safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bdi Mental States use?

Bdi Mental States is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bdi Mental States use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bdi Mental States?

Skills that share tags, products or a category with Bdi Mental States: Logseq Review Workflow Eval (logseq/logseq, 45k stars), Baoyu URL To Markdown (sdyckjq-lab/llm-wiki-skill, 2.5k stars), Obsidian CLI (Atmosphere/atmosphere, 3.8k stars) and Esm Cjs Risk Scan (logseq/logseq, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bdi Mental States?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,443 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 10, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.