kiln-batch-dispatch ยท diff
git:20260905.b2eff76 to git:20260912.1c1abe8
20 added, 20 removed. Audit A to A.
- ---
- name: kiln-batch-dispatch
- description: Run requested Kiln asset batches or compare coding-agent harnesses and models in isolated workspaces. Use for repeatable trials, source-reference workflow checks, and distinguishing provider, tool, and asset-quality failures.
- license: MIT
- ---
-
- # Run comparable asset trials
-
- Use the user's requested harness and exact model identifier. Verify what the provider actually ran; do not silently substitute a default or older model.
-
- 1. Create a fresh project for each candidate using [clean-room evaluation](references/clean-room-evaluation.md). Keep the engine checkout and example collection outside the agent's task context.
- 2. Give each candidate the same brief, installed skills, image budget, time limit, and starting asset where applicable. Record inherited tools, instructions, memory, and permissions.
- 3. Verify tool access and actual image delivery with one small run before dispatching a batch. Model vision support and harness image forwarding are separate checks.
- 4. Exercise the whole revision loop: render source once, use the returned `programRef`, read a source window, edit an exact anchor, inspect a part, and export the same revision. Check that later calls do not resend the program and that unrelated source survives the edit.
- 5. Retain the brief, exact model, harness version, transcript, source, images, package/skill hashes, and outcome. Rebuild the saved source independently of the agent's success claim.
- 6. Review shapes under consistent cameras and lighting. Separate provider outages or quota limits, harness failures, tool/schema failures, engine failures, and visual quality. An interrupted run is not a completed asset.
-
- After a shared package or skill change, rerun the affected workflow with the final candidate. A successful earlier revision does not validate later instructions. Record manual repairs separately from model output.
-
- Adding a reviewed asset to a gallery is a separate task. Do it only when that addition is requested, retaining model provenance and later edits.
+ ---
+ name: kiln-batch-dispatch
+ description: Run requested Kiln asset batches or compare coding-agent harnesses and models in isolated workspaces. Use for repeatable trials, source-reference workflow checks, and distinguishing provider, tool, and asset-quality failures.
+ license: MIT
+ ---
+
+ # Run comparable asset trials
+
+ Use the user's requested harness and exact model identifier. Verify what the provider actually ran; do not silently substitute a default or older model.
+
+ 1. Create a fresh project for each candidate using [clean-room evaluation](references/clean-room-evaluation.md). Keep the engine checkout and example collection outside the agent's task context.
+ 2. Give each candidate the same brief, installed skills, image budget, time limit, and starting asset where applicable. Record inherited tools, instructions, memory, and permissions.
+ 3. Verify tool access and actual image delivery with one small run before dispatching a batch. Model vision support and harness image forwarding are separate checks.
+ 4. Exercise the whole revision loop: render source once, use the returned `programRef`, read a source window, edit an exact anchor, inspect a part, and export the same revision. Check that later calls do not resend the program and that unrelated source survives the edit.
+ 5. Retain the brief, exact model, harness version, transcript, source, images, package/skill hashes, and outcome. Rebuild the saved source independently of the agent's success claim.
+ 6. Review shapes under consistent cameras and lighting. Separate provider outages or quota limits, harness failures, tool/schema failures, engine failures, and visual quality. An interrupted run is not a completed asset.
+
+ After a shared package or skill change, rerun the affected workflow with the final candidate. A successful earlier revision does not validate later instructions. Record manual repairs separately from model output.
+
+ Adding a reviewed asset to a gallery is a separate task. Do it only when that addition is requested, retaining model provenance and later edits.