Feat/skill builder

.env.example

@@ -1,9 +1,6 @@
-# LLM provider — set one of: openai, anthropic, google
-CONCLAVE_LLM_PROVIDER=openai
-CONCLAVE_OPENAI_API_KEY=
-CONCLAVE_OPENAI_MODEL=gpt-4o
-CONCLAVE_ANTHROPIC_API_KEY=
-CONCLAVE_GOOGLE_API_KEY=
+# NearAI API — all models served via NearAI confidential compute
+CONCLAVE_NEARAI_API_KEY=
+CONCLAVE_DEFAULT_MODEL=deepseek-ai/DeepSeek-V3.1
 
 # Supabase auth — Project Settings → API in your Supabase dashboard
 # JWT validation uses JWKS (ES256/ECC P-256) — no shared secret needed
@@ -14,3 +11,6 @@ CONCLAVE_SUPABASE_ANON_KEY=
 LANGCHAIN_TRACING_V2=true
 LANGCHAIN_API_KEY=
 LANGCHAIN_PROJECT=conclave-eval
+
+# Per-skill model config lives in skills/<skill-name>/.env
+# See skills/hackathon_novelty/.env.example for an example

api/routes.py

@@ -317,7 +317,10 @@ def get_results(submission_id: str, request: Request):
     if role == "user":
         if submission_id not in token_info["submission_ids"]:
             raise HTTPException(status_code=403, detail="Access denied: submission not owned by this token")
-        return instance_results[submission_id]
+        # Participant view: filtered to skill-declared user_output_keys
+        card = _skill_router.get_card(_instances[instance_id]["skill_name"])
+        result = instance_results[submission_id]
+        return {k: result[k] for k in card.user_output_keys if k in result}
 
     # admin: unrestricted access within the instance
     return instance_results[submission_id]

config.py

@@ -1,15 +1,14 @@
+from __future__ import annotations
 from pydantic_settings import BaseSettings
-from typing import Literal
 
 
 class Settings(BaseSettings):
-    llm_provider: Literal["openai", "anthropic", "google", "nearai"] = "openai"
-    openai_api_key: str = ""
-    openai_model: str = "gpt-4o"
-    anthropic_api_key: str = ""
-    google_api_key: str = ""
+    # NearAI API — all models served via NearAI confidential compute
     nearai_api_key: str = ""
-    nearai_model: str = "deepseek-ai/DeepSeek-V3.1"
+    nearai_base_url: str = "https://cloud-api.near.ai/v1"
+    default_model: str = "deepseek-ai/DeepSeek-V3.1"
+
+    # Embedding (unchanged)
     embedding_model: str = "all-MiniLM-L6-v2"
 
     # Supabase auth (optional — if unset, /auth/* endpoints return 503 and /register is the fallback)
@@ -22,23 +21,15 @@ class Settings(BaseSettings):
 settings = Settings()
 
 
-def get_llm():
-    """Return the configured LangChain chat model."""
-    if settings.llm_provider == "openai":
-        from langchain_openai import ChatOpenAI
-        return ChatOpenAI(model=settings.openai_model, api_key=settings.openai_api_key)
-    elif settings.llm_provider == "anthropic":
-        from langchain_anthropic import ChatAnthropic
-        return ChatAnthropic(model="claude-sonnet-4-6", api_key=settings.anthropic_api_key)
-    elif settings.llm_provider == "google":
-        from langchain_google_genai import ChatGoogleGenerativeAI
-        return ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=settings.google_api_key)
-    elif settings.llm_provider == "nearai":
-        from langchain_openai import ChatOpenAI
-        return ChatOpenAI(
-            model=settings.nearai_model,
-            api_key=settings.nearai_api_key,
-            base_url="https://cloud-api.near.ai/v1",
-        )
-    else:
-        raise ValueError(f"Unsupported LLM provider: {settings.llm_provider}")
+def get_llm(model: str | None = None):
+    """Return the configured LangChain chat model via NearAI.
+
+    model: specific model ID to use. Falls back to settings.default_model if None.
+    Skills declare their own per-node models in their own config.py.
+    """
+    from langchain_openai import ChatOpenAI
+    return ChatOpenAI(
+        model=model or settings.default_model,
+        api_key=settings.nearai_api_key,
+        base_url=settings.nearai_base_url,
+    )

core/skill_card.py

@@ -29,6 +29,7 @@ class SkillCard:
     run: Callable                        # the run_skill() entry point
     input_model: Type[BaseModel]         # Pydantic model for this skill's inputs
     output_keys: set                     # allowed output keys (mirrors ALLOWED_OUTPUT_KEYS)
+    user_output_keys: set = field(default_factory=set)  # keys visible to user role (subset of output_keys)
     config: dict = field(default_factory=dict)          # skill-specific config params
     trigger_modes: list = field(default_factory=list)   # supported trigger declarations
     roles: dict = field(default_factory=dict)           # admin + user role declarations
@@ -44,6 +45,7 @@ def metadata(self) -> dict:
             "version": self.version,
             "input_schema": self.input_model.model_json_schema(),
             "output_keys": sorted(self.output_keys),
+            "user_output_keys": sorted(self.user_output_keys),
             "config": self.config,
             "trigger_modes": self.trigger_modes,
             "roles": self.roles,