BDI Mental State Modeling workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.
Quelldateien prüfen
Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.
BDI Mental State Modeling workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/bdi-mental-states from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
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.
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Core Concepts, T2B2T Paradigm, Notation Selection by Level, Justification and Explainability, Temporal Dimensions, Compositional Mental Entities.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
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
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
Read the overview and provenance files before loading any copied upstream support files.
Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
Validate the result against the upstream expectations and the evidence you can point to in the copied files.
Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.
Imported Workflow Notes
Imported: 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
: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:
Use @bdi-mental-states to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @bdi-mental-states against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @bdi-mental-states for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @bdi-mental-states using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Model world states as configurations independent of agent perspectives, providing referential substrate for mental states.
Distinguish endurants (persistent mental states) from perdurants (temporal mental processes), aligning with DOLCE ontology.
Treat goals as descriptions rather than mental states, maintaining separation between cognitive and planning layers.
Use hasPart relations for meronymic structures enabling selective belief updates.
Associate every mental entity with temporal constructs via atTime or hasValidity.
Use bidirectional property pairs (motivates/isMotivatedBy, generates/isGeneratedBy) for flexible querying.
Link mental entities to Justification instances for explainability and trust.
Imported Operating Notes
Imported: Guidelines
Model world states as configurations independent of agent perspectives, providing referential substrate for mental states.
Distinguish endurants (persistent mental states) from perdurants (temporal mental processes), aligning with DOLCE ontology.
Treat goals as descriptions rather than mental states, maintaining separation between cognitive and planning layers.
Use hasPart relations for meronymic structures enabling selective belief updates.
Associate every mental entity with temporal constructs via atTime or hasValidity.
Use bidirectional property pairs (motivates/isMotivatedBy, generates/isGeneratedBy) for flexible querying.
Link mental entities to Justification instances for explainability and trust.
Implement T2B2T through: (1) translate RDF to beliefs, (2) execute BDI reasoning, (3) project mental states back to RDF.
Define existential restrictions on mental processes (e.g., BeliefProcess ⊑ ∃generates.Belief).
Reuse established ODPs (EventCore, Situation, TimeIndexedSituation, BasicPlan, Provenance) for interoperability.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/bdi-mental-states, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: 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)
Imported: T2B2T Paradigm
Triples-to-Beliefs-to-Triples implements bidirectional flow between RDF knowledge graphs and internal mental states:
Validate implementation against these SPARQL queries:
# 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
Imported: Anti-Patterns
Conflating mental states with world states: Mental states reference world states, they are not world states themselves.
Missing temporal bounds: Every mental state should have validity intervals for diachronic reasoning.
Flat belief structures: Use compositional modeling with hasPart for complex beliefs.
Implicit justifications: Always link mental entities to explicit justification instances.
Direct intention-to-action mapping: Intentions specify plans which contain tasks; actions execute tasks.
Imported: 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
Imported: 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.
