actor simulate

Author: RCFans

.github/workflows/pylint.yml

@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.12"]
+        python-version: ["3.12", "3.13"]
     steps:
     - uses: actions/checkout@v5
     - name: Set up Python ${{ matrix.python-version }}

config/prompts/base.yaml

@@ -33,6 +33,9 @@ prompt_meta:
   scenario_enhance_prompt:
     id: base.scenario_enhance_prompt
     version: 1.0.0
+  scenario_prior_prompt:
+    id: base.scenario_prior_prompt
+    version: 1.0.0
 prompts:
   system_prompt: |
     You are a strategic ontology engineer.
@@ -329,7 +332,7 @@ prompts:
     [[source_text]]
 
   situation_decompose_prompt: |
-    You are a strategy analyst. Decompose situation artifact into deterministic scenarios A/B/C.
+    You are a strategy planner. Decompose situation artifact into deterministic scenarios A/B/C.
     Return ONLY valid JSON object with keys:
     pack_id, pack_version, derived_from_situation_id, ontology_version, scenarios.
 
@@ -427,7 +430,7 @@ prompts:
     [[source_text]]
 
   scenario_enhance_prompt: |
-    You are a strategy analyst. Derive StrategicActor strategic_style from widely known historical similar cases.
+    You are a organizational behavior analyst. Derive StrategicActor strategic_style from widely known historical similar cases.
     Return ONE valid JSON object only.
 
     Required output keys:
@@ -451,3 +454,31 @@ prompts:
     strategic_actor_role: [[strategic_actor_role]]
     current_case_id_to_exclude: [[current_case_id_to_exclude]]
     actor_ontology_json: [[actor_ontology_json]]
+
+  scenario_prior_prompt: |
+    You are a organizational behavior analyst. Score the probability of selecting scenario A/B/C based on StrategicActor style.
+    Return ONE valid JSON object only.
+
+    Required output:
+    {
+      "raw_prior_scores": [
+        {"scenario_key": "A", "score": 0.00-1.00, "explain": "..."},
+        {"scenario_key": "B", "score": 0.00-1.00, "explain": "..."},
+        {"scenario_key": "C", "score": 0.00-1.00, "explain": "..."}
+      ]
+    }
+
+    Rules:
+    - Use only StrategicActor style and scenario candidate definitions.
+    - Evaluate behavioral fit, not textual overlap.
+    - Treat this as a probability distribution problem. If one scenario is highly likely due to actor style, the others must be discounted accordingly to maintain the sum.
+    - Provide A/B/C exactly once.
+    - The sum of scores for A, B, and C MUST equal exactly 1.0 (A + B + C = 1).
+    - score must be numeric and non-negative.
+    - explain must be concise and decision-relevant.
+    - Do not output markdown or extra keys.
+
+    actor_ref: [[actor_ref]]
+    actor_style_json: [[actor_style_json]]
+    scenario_candidates_json: [[scenario_candidates_json]]
+    fallback_similarity_json: [[fallback_similarity_json]]

pyproject.toml

@@ -4,10 +4,10 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "omenai"
-version = "0.1.2"
+version = "0.1.3"
 description = "The AI-Powered open-source strategic reasoning engine."
 readme = "README.md"
-requires-python = ">=3.12,<3.13"
+requires-python = ">=3.12"
 authors = [{ name = "StrategyLogic" }]
 license = "AGPL-3.0-or-later"
 license-files = ["LICENSE", "NOTICE.md"]

specs

@@ -1,4 +1,4 @@
 """Omen strategic reasoning engine package."""
 
 __all__ = ["__version__"]
-__version__ = "0.1.2"
+__version__ = "0.1.3"

src/omen/__init__.py

@@ -150,6 +150,7 @@ def _append_scenario_decomposition_trace(
     scenario_artifact_path: Path,
     situation_ref: Path,
     decomposition_quality: dict[str, Any] | None,
+    planner_trace: dict[str, Any] | None = None,
 ) -> None:
     payload: dict[str, Any] = {}
     if trace_path.exists():
@@ -171,6 +172,11 @@ def _append_scenario_decomposition_trace(
         "validation_issues": list((decomposition_quality or {}).get("validation_issues") or []),
         "logic_issues": list((decomposition_quality or {}).get("logic_issues") or []),
     }
+    if isinstance(planner_trace, dict):
+        payload["scenario_planner"] = {
+            "actor_style_enhancement": dict(planner_trace.get("actor_style_enhancement") or {}),
+            "prior_scoring": dict(planner_trace.get("prior_scoring") or {}),
+        }
     save_auxiliary_json(trace_path, payload)
 
 
@@ -455,6 +461,8 @@ def handle_scenario_command(args: Any) -> int:
             config_path=str(args.config),
             traces_dir=output_path_arg.parent / "traces",
         )
+        planner_trace = dict(ontology.pop("_planner_trace", {}) or {})
+
         output_path = save_scenario_ontology_slice(output_path_arg, ontology)
         markdown_path = output_path.with_suffix(".md")
         save_scenario_ontology_markdown(markdown_path, ontology)
@@ -464,6 +472,7 @@ def handle_scenario_command(args: Any) -> int:
             scenario_artifact_path=output_path,
             situation_ref=situation_path,
             decomposition_quality=ontology.get("decomposition_quality"),
+            planner_trace=planner_trace,
         )
         print(f"Saved scenario planning artifact to {output_path}")
         print(f"Saved scenario planning summary to {markdown_path}")