rename from checkpoint to timeline agent

AI_REFACTOR_PLAN.md

@@ -69,7 +69,7 @@ Tools must use **camelCase** field names (Drizzle maps them to snake_case intern
 |---|---|
 | `tasks` | id, projectId, title, description, status (todo\|in_progress\|done), priority (high\|medium\|low), assignee (JSON array string), dueDate (ms), isAiSuggested (0\|1), isApproved (0\|1), createdAt (ms) |
 | `projects` | id, clientId, name, status (active\|archived), aiSummary, createdAt (ms) |
-| `checkpoints` | id, projectId (required), title, date (ms), isAiSuggested (0\|1), isApproved (0\|1), createdAt (ms) |
+| `timelines` | id, projectId (required), title, date (ms), isAiSuggested (0\|1), isApproved (0\|1), createdAt (ms) |
 | `notes` | id, projectId, title, content (markdown), createdAt (ms), updatedAt (ms) |
 | `taskComments` | id, taskId, author, content, createdAt (ms) |
 | `clients` | id, parentId, name, industry, createdAt (ms) |
@@ -141,11 +141,11 @@ Tools must use **camelCase** field names (Drizzle maps them to snake_case intern
   - `update_project(project_id, ...)`: build updates → `execute_on_client(action="update", ...)` → return confirmation
   - `delete_project(project_id)`: `execute_on_client(action="delete", ...)` → return confirmation
 
-- [x] **`app/agents/checkpoint_agent.py` (4 tools):**
-  - `list_checkpoints(project_id)`: `execute_on_client(action="select", table="checkpoints", filters={projectId})` → format + return
-  - `create_checkpoint(project_id, title, date, ...)`: `execute_on_client(action="insert", table="checkpoints", data={...})` → return confirmation + id
-  - `update_checkpoint(checkpoint_id, ...)`: build updates → `execute_on_client(action="update", ...)` → return confirmation
-  - `delete_checkpoint(checkpoint_id)`: `execute_on_client(action="delete", ...)` → return confirmation
+- [x] **`app/agents/timeline_agent.py` (4 tools):**
+  - `list_timelines(project_id)`: `execute_on_client(action="select", table="timelines", filters={projectId})` → format + return
+  - `create_timeline(project_id, title, date, ...)`: `execute_on_client(action="insert", table="timelines", data={...})` → return confirmation + id
+  - `update_timeline(timeline_id, ...)`: build updates → `execute_on_client(action="update", ...)` → return confirmation
+  - `delete_timeline(timeline_id)`: `execute_on_client(action="delete", ...)` → return confirmation
 
 - [x] **`app/agents/note_agent.py` (5 tools):**
   - `list_notes(project_id)`: `execute_on_client(action="select", table="notes", filters={projectId})` → format + return
@@ -154,7 +154,7 @@ Tools must use **camelCase** field names (Drizzle maps them to snake_case intern
   - `update_note(note_id, ...)`: build updates → `execute_on_client(action="update", ...)` → then vector_upsert for updated content → return confirmation
   - `delete_note(note_id)`: `execute_on_client(action="delete", ...)` → return confirmation
 
-- **Files:** `app/agents/task_agent.py`, `app/agents/project_agent.py`, `app/agents/checkpoint_agent.py`, `app/agents/note_agent.py`
+- **Files:** `app/agents/task_agent.py`, `app/agents/project_agent.py`, `app/agents/timeline_agent.py`, `app/agents/note_agent.py`
 - **Outcome:** All 23 tools query real user data via WS. LLM sees actual rows, not action descriptors.
 
 ### Step B.3 — Bidirectional WebSocket handler
@@ -282,7 +282,7 @@ Cloud Agent:
     - `device_id` str — identifies which Electron install this config belongs to
     - `name` str
     - `directory_paths` JSON — list of absolute paths on the device
-    - `data_types` JSON — which tables to extract to: `["tasks", "notes", "checkpoints", "projects"]`
+    - `data_types` JSON — which tables to extract to: `["tasks", "notes", "timelines", "projects"]`
     - `prompt_template` text — user-configured via Chatbot Journey
     - `file_extensions` JSON — e.g. `[".eml", ".txt", ".pdf", ".md"]`
     - `schedule_cron` str — e.g. `"0 */6 * * *"` (every 6h)
@@ -429,7 +429,7 @@ Cloud Agent:
   - `POST /api/v1/agents/journey/message`:
     - Body: `{ session_id, message }`
     - AI processes user's answer, asks follow-up questions (max 5 turns)
-    - System prompt: "You are configuring a data extraction agent for a freelancer. Ask about file format, what data to extract (tasks, notes, checkpoints), naming conventions, priority rules, and any special mapping. After 3-5 questions, generate a detailed prompt_template."
+    - System prompt: "You are configuring a data extraction agent for a freelancer. Ask about file format, what data to extract (tasks, notes, timelines), naming conventions, priority rules, and any special mapping. After 3-5 questions, generate a detailed prompt_template."
     - When AI determines enough context: `{ session_id, message: "Here's your configuration...", done: true, prompt_template: "..." }`
     - The `prompt_template` is a structured instruction for the extraction LLM (e.g. "Extract tasks from email. Subject becomes task title. If body contains 'urgent' or 'ASAP', set priority to 'high'. Extract due dates if mentioned.")
     - **Electron note:** `toCamelCase` converts the response → Electron reads `promptTemplate` from the final message and auto-fills the agent config panel. User clicks "Save & apply" which calls `agent.local.update` / `agent.cloud.update` tRPC mutation.