// This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.
A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.
This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.
This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.
This skill should be used to explain or reason about the foundational concepts of context engineering: what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and background reading. Route operational work to the specialized skills: debugging attention failures goes to context-degradation, token-efficiency work goes to context-optimization, conversation summarization goes to context-compression, and project-shape decisions go to project-development.
This skill should be used for improving context efficiency: context budgeting, observation masking, prefix or KV-cache strategy, partitioning, token-cost reduction, retrieval scoping, and extending effective context capacity without lowering answer quality.
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.
Activate this skill when:
Do not activate this skill for adjacent work owned by other skills:
context-fundamentals.memory-systems.multi-agent-patterns.evaluation.Separate mental states into two ontological categories because BDI reasoning requires distinguishing what persists from what happens:
Mental States (Endurants) -- model these as persistent cognitive attributes that hold over time intervals:
Belief: Represent what the agent holds true about the world. Ground every belief in a world state reference.Desire: Represent what the agent wishes to bring about. Link each desire back to the beliefs that motivate it.Intention: Represent what the agent commits to achieving. An intention must fulfil a desire and specify a plan.Mental Processes (Perdurants) -- model these as events that create or modify mental states, because tracking causal transitions enables explainability:
BeliefProcess: Triggers belief formation/update from perception. Always connect to a generating world state.DesireProcess: Generates desires from existing beliefs. Preserves the motivational chain.IntentionProcess: Commits to selected desires as actionable intentions.Wire beliefs, desires, and intentions into directed chains using bidirectional properties (motivates/isMotivatedBy, fulfils/isFulfilledBy) because this enables both forward reasoning (what should the agent do?) and backward tracing (why did the agent act?):
: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 .
Always ground mental states in world state references rather than free-text descriptions, because ungrounded beliefs break semantic querying and cross-agent interoperability:
: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 .
Connect intentions to plans via bdi:specifies, and decompose plans into ordered task sequences using bdi:precedes, because this separation allows plan reuse across different intentions while keeping execution order explicit:
: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 .
Implement Triples-to-Beliefs-to-Triples as a bidirectional pipeline because agents must both consume external RDF context and produce new RDF assertions. Structure every T2B2T implementation in two explicit phases:
Phase 1: Triples-to-Beliefs -- Translate incoming RDF triples into belief instances. Use bdi:triggers to connect the external world state to a BeliefProcess, and bdi:generates to produce the resulting belief. This preserves provenance from source data through to internal cognition:
: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 -- After BDI deliberation selects an intention and executes a plan, project the results back into RDF using bdi:bringsAbout. This closes the loop so downstream systems can consume agent outputs as standard linked data:
:Intention_pay a bdi:Intention ;
bdi:specifies :Plan_payment .
:PlanExecution_PE1 a bdi:PlanExecution ;
bdi:satisfies :Plan_payment ;
bdi:bringsAbout :WorldState_payment_complete .
Choose notation based on the C4 abstraction level being modeled, because mixing notations at the wrong level obscures rather than clarifies the cognitive architecture:
| 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 |
Attach bdi:Justification instances to every mental entity using bdi:isJustifiedBy, because unjustified mental states make agent reasoning opaque and untraceable. Each justification should capture the evidence or rule that produced the mental state:
: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" .
Assign validity intervals to every mental state using bdi:hasValidity with TimeInterval instances, because beliefs without temporal bounds cannot be garbage-collected or conflict-checked during diachronic reasoning:
: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 a specific moment using SPARQL temporal filters. Use this pattern to resolve conflicts when multiple beliefs about the same world state overlap in time:
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)
}
Decompose complex beliefs into constituent parts using bdi:hasPart relations, because monolithic beliefs force full replacement on partial updates. Structure composite beliefs so that each sub-belief can be independently updated, queried, or invalidated:
: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 without touching time
:BeliefProcess_update a bdi:BeliefProcess ;
bdi:modifies :Belief_meeting_location .
Use this workflow when converting external semantic context into a BDI representation:
Start with Agent, WorldState, Belief, Desire, Intention, Plan, Task, Justification, and TimeInterval. Add specialized classes only after competency questions prove the core model cannot answer required queries. A compact ontology is easier to serialize into prompts, easier to validate, and less likely to create brittle reasoning chains.
BDI modeling is justified when the system needs explainable agency: why an agent believed something, what desire that belief created, which intention was selected, and what plan executed. If the system only needs to remember facts across sessions, use memory-systems. If it only needs to split work across agents, use multi-agent-patterns.
Use LAG to constrain LLM outputs with ontological structure, because unconstrained generation produces triples that violate BDI class restrictions. Serialize the ontology into the prompt context, then validate generated triples against it before accepting them:
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()
Translate BDI ontology patterns into executable production rules when deploying to rule-based agent platforms. Map each cognitive chain link (belief-to-desire, desire-to-intention) to a HEAD/CONDITIONALS/TAIL rule, because this preserves the deliberative semantics while enabling runtime execution:
% 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)].
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.
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
Example 1: RDF notification to BDI chain
Input world state:
:WorldState_invoice_due a bdi:WorldState ;
rdfs:comment "Invoice INV-42 is due tomorrow" ;
bdi:atTime :Time_2026_05_15 .
BDI projection:
:Belief_invoice_due a bdi:Belief ;
bdi:refersTo :WorldState_invoice_due ;
bdi:isJustifiedBy :Justification_billing_system ;
bdi:motivates :Desire_avoid_late_fee .
:Desire_avoid_late_fee a bdi:Desire ;
bdi:isMotivatedBy :Belief_invoice_due .
:Intention_pay_invoice a bdi:Intention ;
bdi:fulfils :Desire_avoid_late_fee ;
bdi:specifies :Plan_pay_invoice .
Example 2: Boundary decision
If the task is "remember that Alice prefers concise summaries," use memory-systems. If the task is "represent why the agent believes Alice needs a summary, what goal that creates, and which plan it commits to," use this skill.
Conflating mental states with world states: Mental states reference world states via bdi:refersTo, they are not world states themselves. Mixing them collapses the perception-cognition boundary and breaks SPARQL queries that filter by type.
Missing temporal bounds: Every mental state needs validity intervals for diachronic reasoning. Without them, stale beliefs persist indefinitely and conflict detection becomes impossible.
Flat belief structures: Use compositional modeling with hasPart for complex beliefs. Monolithic beliefs force full replacement when only one attribute changes.
Implicit justifications: Always link mental entities to explicit Justification instances. Unjustified mental states cannot be audited or traced.
Direct intention-to-action mapping: Intentions specify plans which contain tasks; actions execute tasks. Skipping the plan layer removes the ability to reuse, reorder, or share execution strategies.
Ontology over-complexity: Start with 5-10 core classes and properties (Belief, Desire, Intention, WorldState, Plan, plus key relations). Expanding the ontology prematurely inflates prompt context and slows SPARQL queries without improving reasoning quality.
Reasoning cost explosion: Keep belief chains to 3 levels or fewer (belief -> desire -> intention). Deeper chains become prohibitively expensive for LLM inference and rarely improve decision quality over shallower alternatives.
This skill owns formal mental-state modeling. Adjacent skills own different layers:
memory-systems: persistent facts, entity memory, and temporal knowledge graphs without BDI belief/desire/intention semantics.multi-agent-patterns: agent topology, handoff protocols, and coordination between agents.evaluation: competency questions, regression checks, and quality gates for BDI implementations.context-fundamentals: conceptual context-window and attention mechanics that inform prompt construction.tool-design: schema and tool contracts for BDI query, validation, or projection tools.Internal references:
Primary sources:
Created: 2026-01-07 Last Updated: 2026-05-15 Author: Agent Skills for Context Engineering Contributors Version: 2.1.0