> ## Documentation Index
> Fetch the complete documentation index at: https://docs.withwhile.com/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Install with `uv add whileai`; import as `import whileai as wai`.
> Run the offline path first (`simulator=False`, `wai.seeded_agent`, a callable judge); no key is needed for it.
> Report every pass rate with its interval and n, as `scored.pass_at` prints it.

# Say why it moved

> Before a coding agent posts a second version, a sweep or a training run, it follows the manage-experiments skill: the question first, then per run five lines (Changed, Moved, Why, Learned, Reproduce), one chart, the rows that failed, and readback(tracked) to read the page the way the person will.

The coding agent knows the repo. The person opening the page does not have its context and reads for two reasons: to learn what a paper or an idea does when tried, and to find a behavior and fix it fast at work. Both want one look: what changed, did it move, why, what it taught, and can I run it again. On one day in September 2026 real agents posted `dapo-lr5e-05-s17-30st` nineteen times, no note on any run, no chart, a `0.75` on a page that counts points, and no question on six agents of seven. Every page was correct and said nothing.

The fix is a playbook the agent follows before the second run, tested in CI like every skill: [skills/manage-experiments](https://github.com/whilehq/whileai-sdk/blob/main/skills/manage-experiments/SKILL.md). `whileai init` installs it under `.claude/skills/` and the `AGENTS.md` block names it.

| The person asks         | The agent posts                                                                                     | Call                                                                                                              |
| ----------------------- | --------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------- |
| what are these runs for | the question, at the top of the page                                                                | `tracked.experiment(question=, hypothesis=, method=, measure=, decide=)`                                          |
| what changed            | one line: the prompt line, the data mix, the setting                                                | `run.note("Changed: ...")`                                                                                        |
| did it move             | before and after in points, with the interval                                                       | `"Moved: 75 to 96.2 points (±5.2) on 52 asks"`                                                                    |
| why                     | the asks that flipped, and whether the reward and the held-out moved together                       | `"Why: 11 asks now pass, every one an old order; 0 broke"`                                                        |
| what did it teach me    | the hypothesis closed in one sentence, with the paper when the run tried one                        | `"Learned: 800 traces taught one rule; the held-out checkpoints are the proof (rlhfbook.com, Over-Optimization)"` |
| can I run it again      | the command, the seed, the pins                                                                     | `"Reproduce: uv run python train.py --seed 17"` + `RunRecord(data=, optimizer=, provenance=)`                     |
| show me                 | one chart per run: bars with intervals for a harness, reward and held-out on one chart for training | `tracked.figure(name, {"data": [...], "layout": {...}}, run=run)`                                                 |
| what failed             | up to 20 rows under each score, failures first                                                      | `run.score(b, 96.2, ci=5.2, n=52, examples=worst)`                                                                |
| is the page ready       | the account read the way a teammate will, one line per problem with the fixing call                 | `readback(tracked)` from the skill; empty means clean                                                             |

A reward that climbs while the held-out line stays flat is the judge being gamed (rlhfbook.com, "Over-Optimization"); the note says so rather than letting the person find out. `print(tracked.brief())` and `print(tracked.verdict())` are the sentences the page shows, from the same rows; paste them in the pull request. Names follow [Naming](/platform/naming): the agent after the product, behaviors as the policy phrases them, versions as the team ships them, the test by its content.