# Submit Experiment Results

> Post material interim assignment updates, then commit and submit a terminal experiment result through the typed Senpai GitHub tools.

- Skill: `wandb/submit-experiment-results` (Agent Skill)
- Install (CLI): `npx skillmds@latest add wandb/submit-experiment-results`
- Raw SKILL.md: https://api.skillmd.com/api/skills/wandb/submit-experiment-results/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: wandb (https://skillmd.com/u/wandb)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/wandb/submit-experiment-results

---


# Submit experiment results

## Interim assignment comments

Use `post_assignment_comment` when the advisor asked for a response or the
experiment is blocked or materially delayed. Use a fresh `comment_id` for each
new message and reuse that ID only for an exact retry. Keep the PR concise
rather than mirroring routine internal narration.

First make the repository worktree clean by committing only the assigned change.
Collect the current local commit SHA and the current remote assignment-branch
SHA. Build the strict `ExperimentResult` required by
`submit_experiment_result`:

- the assignment repository, PR, assignment ID, revision ID, student, and
  current expected head SHA;
- terminal status, hypothesis, and bounded summary;
- every W&B run ID, URL, and terminal state;
- the primary metric comparison; and
- the same local commit SHA.

Call `submit_experiment_result` with exactly this operation-specific payload. Do not add PR-body
text, per-run metrics, hyperparameters, or aliases such as `head_sha`,
`previous_head_sha`, `success`, or `min`; put those details in the bounded
`summary` instead.

```json
{
  "branch": "student/experiment",
  "remote_branch_sha_before_push": "REMOTE_SHA_BEFORE_PUSH",
  "result": {
    "assignment": {
      "repo": "owner/repo",
      "pr_number": 123,
      "assignment_id": "assignment-id",
      "revision_id": "LATEST_REVISION_ID",
      "expected_head_sha": "LOCAL_COMMIT_SHA",
      "student": "student-name"
    },
    "status": "succeeded",
    "hypothesis": "The falsifiable hypothesis tested.",
    "summary": "The conclusion, evidence, caveats, and important per-run metrics (maximum 4,000 characters).",
    "runs": [
      {
        "run_id": "wandb-run-id",
        "url": "https://wandb.ai/entity/project/runs/wandb-run-id",
        "state": "finished"
      }
    ],
    "primary_metric": {
      "name": "validation/metric",
      "direction": "minimize",
      "baseline": 1.23,
      "candidate": 1.10,
      "delta": -0.13
    },
    "commit_sha": "LOCAL_COMMIT_SHA"
  }
}
```

Use one of `succeeded`, `failed`, `inconclusive`, or `cancelled` for result
status; `minimize` or `maximize` for metric direction; and `finished`,
`failed`, `crashed`, or `killed` for each run. `primary_metric` may be omitted
when no finite comparison exists. Refresh the PR first and use its latest
assignment `revision_id`; a mid-turn advisor revision supersedes an earlier
one.

That single tool call derives the PR and local result head from `result`,
lease-pushes the clean assignment branch, verifies the new PR head, upserts the
authenticated structured result, marks the PR ready,
and reconciles `status:review`. That label is the durable advisor notification.
Exact replay of the same result is safe. Once result evidence is published for
that revision and commit, changed evidence requires a new commit or assignment
revision; the tool will not rewrite the reviewed result in place.
Do not run `git push`, edit labels, write result markers, or call `gh pr ready`
yourself.

If any run is still active or could change the conclusion, keep the assignment
in progress and register it with `monitor_training`; do not submit a terminal
result.