Refactor system prompt variables for clarity and consistency across agent setup and runner modules

app/api/routes/agent_setup.py

@@ -88,7 +88,7 @@ def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
 
 # ── System prompt builder ─────────────────────────────────────────────────
 
-_SYSTEM_PROMPT_TEMPLATE = """\
+_JOURNEY_SYSTEM_PROMPT = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
 Your job is to understand exactly what data the user wants to extract from their
 local directory and produce a detailed prompt_template that a separate AI will use
@@ -158,7 +158,7 @@ def _build_system_prompt(
         else ""
     )
     template, prompt_obj = get_prompt_or_fallback(
-        "journey_system", _SYSTEM_PROMPT_TEMPLATE
+        "journey_system", _JOURNEY_SYSTEM_PROMPT
     )
     compiled = template.format(
         directory=directory,

app/core/agent_runner.py

@@ -102,7 +102,7 @@ _DOMAIN_DESCRIPTIONS: dict[str, str] = {
     ),
 }
 
-_STEP1_SYSTEM_PROMPT = """\
+_BATCH_FILE_CLASSIFIER_PROMPT = """\
 You are a file classifier for a freelance project management tool.
 
 Your job is to match a file to an existing project and identify which data domains to extract.
@@ -133,7 +133,7 @@ Respond ONLY with a JSON object — no markdown, no explanation:
 
 # ── Step 2: Processing prompt ─────────────────────────────────────────────
 
-_PROCESSING_SYSTEM_PROMPT = """\
+_BATCH_PROCESSING_PROMPT = """\
 You are a data extraction assistant for a freelance project management tool.
 
 Your task: extract structured data from the file content and persist it using the available tools.
@@ -162,7 +162,7 @@ Domains to extract: {data_types}
 
 # ── Cloud processing prompt (kept separate for cloud agent) ───────────────
 
-_CLOUD_PROCESSING_PROMPT = """\
+_BATCH_CLOUD_PROCESSING_PROMPT = """\
 You are a data extraction and management assistant for a freelance project
 management tool.
 
@@ -560,7 +560,7 @@ async def _classify_file(
     )
 
     step1_template, step1_prompt_obj = get_prompt_or_fallback(
-        "batch_file_classifier", _STEP1_SYSTEM_PROMPT
+        "batch_file_classifier", _BATCH_FILE_CLASSIFIER_PROMPT
     )
     system = step1_template.format(
         domain_definitions=domain_definitions,
@@ -774,7 +774,7 @@ async def run_local_agent(
                 existing_context = "\n\n".join(existing_blocks)
 
                 step2_template, step2_prompt_obj = get_prompt_or_fallback(
-                    "batch_processing", _PROCESSING_SYSTEM_PROMPT
+                    "batch_processing", _BATCH_PROCESSING_PROMPT
                 )
                 system_prompt = step2_template.format(
                     existing_context=existing_context,
@@ -995,7 +995,7 @@ async def run_cloud_agent(
             items_processed += 1
 
             cloud_template, cloud_prompt_obj = get_prompt_or_fallback(
-                "batch_cloud_processing", _CLOUD_PROCESSING_PROMPT
+                "batch_cloud_processing", _BATCH_CLOUD_PROCESSING_PROMPT
             )
             processing_prompt = cloud_template.format(
                 data_types=", ".join(config.data_types),

app/core/deep_agent.py

@@ -28,7 +28,7 @@ logger = logging.getLogger(__name__)
 FloatingDomainType = Literal["task", "timeline", "project", "node"]
 FloatingDomainSection = Literal["task", "timeline", "note"]
 
-_HOME_SINGLE_AGENT_SYSTEM = (
+_HOME_SYSTEM_PROMPT = (
     "You are the home assistant with direct access to all tools: tasks, projects, notes, timelines, and memory tools. "
     "Always use tools for factual data retrieval before answering. "
     "When the user asks to remember, forget, or update what you know about them, use memory tools. "
@@ -41,7 +41,7 @@ _HOME_SINGLE_AGENT_SYSTEM = (
     "For upcoming tasks, after tag lines add a short recommendation based on due date and priority."
 )
 
-_FLOATING_SINGLE_AGENT_SYSTEM = (
+_FLOATING_SYSTEM_PROMPT = (
     "You are the floating assistant with direct access to all tools: tasks, projects, notes, timelines, and memory tools. "
     "Stay focused on the floating scope in context.scope and answer concisely. "
     "Return plain text only. Do not output XML/HTML-like tags such as <task>, <project>, <note>, <timeline>, or any bracketed id tag wrappers. "
@@ -50,7 +50,7 @@ _FLOATING_SINGLE_AGENT_SYSTEM = (
     "If context.context.resolved_project_id exists, use it as project_id for scoped list calls. "
 )
 
-_FLOATING_DOMAIN_CLASSIFIER_SYSTEM = (
+_FLOATING_DOMAIN_CLASSIFIER_PROMPT = (
     "You are a strict domain classifier for websocket floating requests. "
     "Return ONLY a JSON object with keys: type, id, section. "
     "Allowed type values: task, timeline, project, node. "
@@ -539,7 +539,7 @@ async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[
     try:
         llm = get_llm()
         classifier_messages = [
-            SystemMessage(content=_FLOATING_DOMAIN_CLASSIFIER_SYSTEM),
+            SystemMessage(content=_FLOATING_DOMAIN_CLASSIFIER_PROMPT),
             HumanMessage(
                 content=(
                     f"Message:\n{message}\n\n"
@@ -549,7 +549,7 @@ async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[
         ]
         lf = get_langfuse()
         _, classifier_prompt_obj = get_prompt_or_fallback(
-            "floating_domain_classifier", _FLOATING_DOMAIN_CLASSIFIER_SYSTEM
+            "floating_domain_classifier", _FLOATING_DOMAIN_CLASSIFIER_PROMPT
         )
         if lf:
             with lf.start_as_current_observation(
@@ -851,7 +851,7 @@ async def _run_single_agent_stream(
 async def run_home(user_id: str, message: str, context: dict[str, Any]) -> str:
     prepared_context = await _prepare_context(message, context)
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
-        "home_system", _HOME_SINGLE_AGENT_SYSTEM
+        "home_system", _HOME_SYSTEM_PROMPT
     )
     response = await _run_single_agent(
         user_id=user_id,
@@ -868,7 +868,7 @@ async def run_floating(user_id: str, message: str, context: dict[str, Any]) -> t
     prepared_context = await _prepare_context(message, context)
     domain = await _infer_floating_domain(message, prepared_context)
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
-        "floating_system", _FLOATING_SINGLE_AGENT_SYSTEM
+        "floating_system", _FLOATING_SYSTEM_PROMPT
     )
     response = await _run_single_agent(
         user_id=user_id,
@@ -891,7 +891,7 @@ async def run_home_stream(
 ) -> AsyncGenerator[tuple[str, Any], None]:
     prepared_context = await _prepare_context(message, context)
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
-        "home_system", _HOME_SINGLE_AGENT_SYSTEM
+        "home_system", _HOME_SYSTEM_PROMPT
     )
     text_chunks: list[str] = []
     async for event in _run_single_agent_stream(
@@ -923,7 +923,7 @@ async def run_floating_stream(
     yield "floating_domain", domain
 
     system_prompt, langfuse_prompt = get_prompt_or_fallback(
-        "floating_system", _FLOATING_SINGLE_AGENT_SYSTEM
+        "floating_system", _FLOATING_SYSTEM_PROMPT
     )
     sanitizer = _FloatingStreamSanitizer()
     emitted_sanitized = False