/inno-experiment-dev

name: inno-experiment-dev
description: Creates implementation plan, writes project code with judge feedback loop, and submits final experiment run. Use after code-survey in both Idea and Plan branches.

Inno Experiment Dev (Planning, Implementation, and Submission)

Three parts in one skill: an implementation plan, implementation with a judge feedback loop, and submission of the final experiment run.

Inputs

| Variable | Source | Description | |----------|--------|-------------| | `survey_res` | `Ideation/ideas/selected_idea.txt` (inno-idea-generation, possibly refined by inno-idea-eval) or user | The finalized selected idea (or `refined_for_downstream`) | | `references` | `Ideation/references/logs/load_instance.json` | Source papers (from inno-prepare-resources) | | `updated_prepare_res` | inno-prepare-resources | JSON with `reference_codebases` and `reference_paths` | | `code_survey_res` | inno-code-survey | Comprehensive implementation report / model survey notes | | `dataset_description` | `Ideation/references/dataset_description.md` | Description of available datasets (from inno-prepare-resources; not in instance.json) | | `core_code` | instance.json `Experiment.core_code` | Absolute path when created by Dr. Claw (e.g. `<project_path>/Experiment/core_code`); use as-is or resolve with `path.join(project_path, value)` if relative | | `code_references` | instance.json `Experiment.code_references` | Absolute path when created by Dr. Claw (e.g. `<project_path>/Experiment/code_references`); use as-is or resolve if relative | | `max_iter_times` | pipeline config | Max judge-iteration rounds (default 2) | | `context_variables` | shared state | Mutable dict carrying state across agents |

Plan mode additionally uses `ideas` and survey-specific prompt variants (`build_plan_query_with_survey`, `build_iteration_query_for_plan`, etc.).

Outputs

| Variable | Description | |----------|-------------| | `plan_res` | Detailed implementation plan with dataset, model, training, and testing sections | | `ml_dev_res` | Final ML Agent implementation result | | `judge_res` | Final Judge Agent feedback | | `judge_messages` | Full conversation thread (preserved for inno-experiment-analysis) | | `submit_res` | Experiment submission result with statistical outputs | | `context_variables` | Updated with `dataset_plan`, `training_plan`, `testing_plan`, `suggestion_dict`, `raw_error_stats` |

Cache Artifacts

| File | Agent | Content | |------|-------|---------| | `Experiment/core_code/logs/coding_plan_agent.json` | Coding Plan Agent | `context_variables` + `messages` from planning phase | | `Experiment/core_code/logs/machine_learning_agent.json` | ML Agent | Initial implementation messages (+ `_iter_{N}.json` for judge iterations) | | `Experiment/core_code/logs/judge_agent.json` | Judge Agent | Evaluation messages (+ `_iter_{N}.json` for iterations) | | `Experiment/core_code/logs/machine_learning_agent_iter_submit.json` | ML Agent | Submission run messages and results |

Instructions

Phase 1: Create Implementation Plan

Mirrors `_create_implementation_plan`.

1. **Optional pre-step (Idea mode only)**: If refining the idea for implementation clarity, call the idea refinement agent to produce `refined_for_downstream` with tensor interfaces and forward-pass sketch.

2. **Build plan query**:

• **Idea mode**: `plan_query = build_plan_query(survey_res, references, updated_prepare_res, code_survey_res, dataset_description)` (see `prompts/build_plan_query.md`)
• **Plan mode**: Use `build_plan_query_with_survey(ideas, references, prepare_res, code_survey_res, dataset_description)`

3. **Call Coding Plan Agent** with `messages = [{"role": "user", "content": plan_query}]`.

• The agent reviews codebases using `tree` / `cat`, then creates structured plans via `plan_dataset`, `plan_training`, `plan_testing`.
• Calls `case_resolved` to merge plans.
• Set `plan_res = plan_messages[-1]["content"]`.
• See `references/coding_plan_agent.md` for agent details.

4. **Verify** the plan has clear sections: dataset, model, training, evaluation, file layout.

Phase 2: Implement and Iterate

Mirrors `_implement_and_iterate`.

5. **Initial implementation**: Build `ml_dev_query = build_ml_dev_query(survey_res, prepare_res, code_survey_res, plan_res, dataset_description, core_code, code_references)` (see `prompts/build_ml_dev_query.md`). Use paths from `instance.json`: `Experiment.core_code`, `Experiment.code_references` (absolute in Dr. Claw–created projects; use as-is or resolve with project path if relative). Call **ML Agent** with `messages = [{"role": "user", "content": ml_dev_query}]`. Set `ml_dev_res = ml_messages[-1]["content"]`.

• See `references/ml_agent_instructions.md` for agent details.

6. **Initial judge evaluation**: Build `judge_query = build_judge_query(survey_res, prepare_res, plan_res, ml_dev_res)` (see `prompts/build_judge_query.md`). Call **Judge Agent** with `input_messages = [{"role": "user", "content": judge_query}]`. Set `judge_res = judge_messages[-1]["content"]`.

• See `references/judge_agent_instructions.md` for agent details.

7. **Iteration loop** (for i in 0..max_iter_times - 1): a. Build `iteration_query = build_iteration_query(survey_res, prepare_res, code_survey_res, plan_res, ml_dev_res, judge_res, core_code, code_references)` (see `prompts/build_iteration_query.md`). Use paths from instance.json (absolute in Dr. Claw–created projects; use as-is or resolve if relative). Plan mode uses `build_iteration_query_for_plan`. b. Append as user message to `judge_messages`. Call **ML Agent** with `iter_times=i+1`. Update `ml_dev_res`. c. Build `judge_simple_query = build_judge_simple_query(survey_res, prepare_res, plan_res, ml_dev_res)` (see `prompts/build_judge_simple_query.md`). Plan mode uses `build_judge_simple_query_for_plan`. d. Append as user message to `judge_messages`. Call **Judge Agent** with `iter_times=i+1`. Update `judge_res`. e. I