feat: add intra-stage generation checkpoint and resume support (closes #129)

skills/meta/checkpoint-protocol.md

@@ -52,7 +52,22 @@ The checkpoint utility will:
 - Write the checkpoint JSON to disk
 - Include timestamp and stage metadata
 
-### Step 4: Human Approval (If Required)
+### Step 4: Intra-Stage Checkpointing (Resume Support)
+
+Long-running stages (like `assets` or `compose` loops) can fail midway due to API errors, rate limits, or session interruptions. To allow resuming from the exact point of failure (e.g., Scene 4):
+
+1. **Write partial progress**: Every time you successfully generate a significant item (e.g., one scene's assets, one clip), write an `in_progress` checkpoint. Because `status` is `"in_progress"`, validation will not fail even if the canonical artifact is incomplete.
+   ```python
+   write_checkpoint(
+       pipeline_dir, project_name,
+       stage="assets",
+       status="in_progress",
+       artifacts={"asset_manifest": partial_manifest_dict}
+   )
+   ```
+2. **Resume from partial progress**: When starting a stage, ALWAYS check if an `in_progress` checkpoint exists for it. See Step 7 (Resume Protocol) for how to handle it.
+
+### Step 5: Human Approval (If Required)
 
 When `human_approval_default: true`:
 
@@ -87,7 +102,7 @@ When `human_approval_default: true`:
    - `compose` — Rarely. But human may want to preview.
    - `publish` — Always. Human must approve before anything goes public.
 
-### Step 5: Determine Next Stage
+### Step 6: Determine Next Stage
 
 After checkpoint is written and approved (if needed):
 
@@ -97,7 +112,7 @@ next_stage = get_next_stage(pipeline_dir, project_name)
 
 This reads all existing checkpoints and returns the next stage that needs to run, or `None` if the pipeline is complete.
 
-### Step 6: Resume Protocol
+### Step 7: Resume Protocol
 
 At the START of any pipeline run (not just after a stage), always check for existing progress:
 
@@ -107,8 +122,13 @@ next_stage = get_next_stage(pipeline_dir, project_name)
 
 If `next_stage` is not the first stage:
 1. Inform the human: "Found existing progress. Resuming from stage: [next_stage]"
-2. Load prior artifacts from checkpoints for context
-3. Continue from that stage
+2. **Check for partial progress**: Read the checkpoint for `next_stage`:
+   ```python
+   current_cp = read_checkpoint(pipeline_dir, project_name, next_stage)
+   ```
+   If `current_cp` exists and its status is `"in_progress"`, inform the human you are resuming from the middle of the stage.
+3. **Load artifacts**: Load prior artifacts from checkpoints for context. If resuming from `"in_progress"`, load the partial artifact (e.g. `asset_manifest`) from `current_cp` and skip the sub-tasks (like scenes) that are already completed.
+4. **Continue**: Continue generation from the next successful step, appending to the partial artifact.
 
 If a checkpoint exists with status `"awaiting_human"`:
 1. Inform the human: "Stage [name] is awaiting your approval"