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iterate-from-user

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UpdatedMay 2, 2026 at 13:59

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glemaitre/experiments

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glemaitre/experiments

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iterate-from-user

description

Source the next ML experiment proposal from the user directly, from a GitHub issue tracker the user has pointed us at, or from a spec / notes / reference repo the user has pointed us at. Hand the proposal back to `iterate-ml-experiment`, which writes it into `plan/NN_short_name.md` and seeks the user's approval. Stops at "a proposal (question, motivation, method outline) has been returned"; does not write any plan file itself, and does not author acceptance criteria — the user judges the result. TRIGGER when: `iterate-ml-experiment` is picking a sourcing strategy and the user has offered a concrete idea ("I want to try X", "let's add Y", "tweak the encoder"); the user pastes or links a GitHub issue / discussion that describes the next experiment; the user says "use the issue tracker" or "check issue #N"; the user points us at another repo / folder / set of notes ("look at the spec at <path>", "see the notes in <repo-url>", "read what's in <dir>"). SKIP when: the user is open-ended ("what's next?") with no idea in hand — try a different strategy (diagnostic / methodology / literature) first; the user is asking for a symbol lookup or pipeline mechanics (use the `*-api` skills); there is no `gh` CLI / GitHub access and the user wants the issue tracker — surface the gap and fall back to direct user input. HOW TO USE: this skill is shallow — it elicits the proposal and returns it. If the user has a verbal idea, ask the three shaping questions in the body and synthesize a proposal. If a GitHub issue is the source, fetch it with `gh issue view <N>` (or `gh api`), summarize it through the same three-question lens, and flag anything the issue doesn't answer. If a spec / notes repo is the source, read the relevant files (README, proposal docs, referenced notebooks) and summarize through the same lens. Always return: question / motivation / method outline — not a plan file, not acceptance criteria.

Iterate from user

Source: the user (directly, or via a GitHub issue they own). Output: a proposal handed back to iterate-ml-experiment.

Output contract (read this before the body)

This skill never writes plan/ files and never authors acceptance criteria. It returns a Proposal block back to iterate-ml-experiment (full shape in § What is returned at the bottom): Question, Motivation (with the user quote, issue link, or spec-repo path as Source), Method outline, Open gaps. Required:

Every Proposal must answer all three shaping questions (see § The three shaping questions). Missing → ask the user before returning.
GitHub-issue path: check gh auth status first; resolve repo per § Resolution priority; fetch comments if the issue body is short.
Spec / notes repo path: confirm the path / URL the user gave, read the relevant files only (don't crawl the whole repo), and cite specific files in the Source field.
Goal shifts (different output shape, downstream consumer, or metric class) require user confirmation of a PLAN.md Status update before returning the proposal — see § Stop conditions.

There is no "no proposal" outcome for this skill: it only fires when the user has volunteered an idea (or pointed at an issue / repo). If the user is open-ended without an idea, the dispatcher in iterate-ml-experiment asks them directly via the sourcing menu instead of invoking this skill.

Stop conditions

Don't write plan/ files. That belongs to iterate-ml-experiment. This skill returns a proposal as conversation text or structured fields; the parent skill drafts the file.
Don't infer an issue's content. If the user references an issue, fetch it (gh issue view <N> / gh api) — don't reconstruct from the issue title alone.
Don't paper over missing fields. If the user's idea (or the issue) doesn't answer one of the four shaping questions, surface the gap to the user before returning the proposal.
Check gh auth before fetching anything. Before any gh issue view / gh api call, run gh auth status (cheap, cached). If unauthenticated or on the wrong host, do not retry blindly — ask the user to either run gh auth login themselves (suggest they type ! gh auth login in the prompt so it runs in this session) or paste the issue body directly. A failed gh call surfaces a confusing error; the auth check makes the failure mode explicit.
Flag goal shifts before returning the proposal. If the user's idea (or the issue) materially changes the project goal as recorded in PLAN.md Status — different output shape (point estimate → prediction interval), different downstream consumer (offline batch → online serving), different metric class (squared error → coverage) — that's not just a method change. Surface it as a question to the user before returning the proposal: "this would update PLAN.md Status from to ; confirm or amend the goal first?" The parent skill's per-experiment plan file should not silently re-define what success means while the Status block still reflects the old goal.

The three shaping questions

Every proposal returned from this skill must answer:

What are we trying to learn? (turns "try X" into a hypothesis)
Why now? (the specific reason this idea surfaced — quote the user, link the issue, cite the spec-repo file)
What changes vs. the previous experiment? (which file in src/<pkg>/ is touched, in prose — not code)

Missing → ask the user. Don't fabricate.

No "how will we know it answered the question?" question. That's an acceptance-criteria slot, and the skill does not author those — the user reads the headline result after the run and judges. See iterate-ml-experiment § "What this skill does NOT do".

Three intake paths

Direct user input

The user has a verbal or written idea. Ask the three questions in order, in plain language. Quote them back when summarizing the proposal so the framing stays theirs. Hand the synthesis to iterate-ml-experiment.

Spec / notes repo

The user pointed us at a separate repo / folder / set of notes ("read the spec at ~/code/<repo>", "see the notes at https://github.com/<owner>/<repo>", "look at <dir> for ideas"). Treat it as a structured input source distinct from their own verbal idea — the proposal still needs to come back through the three-question lens.

Procedure:

Confirm the path / URL the user gave; if it's remote and not yet cloned, ask where to clone it (don't pick silently).
Read selectively, not exhaustively. Start with README.md / SPEC.md / NOTES.md / a top-level proposal doc; if the user named a specific file or directory, prioritize that. Don't crawl every file — that hides the actual signal.
Map to the three shaping questions. What does the spec want to learn? What's the motivation as the spec frames it? What concretely changes in src/<pkg>/? Quote the spec verbatim when answering "why now?".
Cite specifically. The Source field in the returned Proposal must reference the file paths (and line numbers if useful) — not just the repo name.
Flag gaps. If the spec doesn't answer one of the three questions, ask the user before returning — same rule as for issues.

GitHub issue tracker

The user pointed us at an issue (number, link, or "check the tracker").

Resolution priority (never silently guess). Pick the owner/repo using the first rule that matches:

Explicit URL in the user's message (https://github.com/<owner>/<repo>/issues/<N>) — wins unconditionally.
org/repo#N shorthand in the user's message (probabl-ai/skore#42) — wins over current context.
Bare #N or "issue 42" with no qualifier — fall back to the current gh context (gh repo view --json nameWithOwner to confirm). If that returns nothing, ask the user before fetching.

Then fetch:

gh issue view <N> --json title,body,labels,url for the baseline.
If the issue body is short (<200 chars) or visibly under-specified, also pull the latest comments — gh issue view <N> --json title,body,labels,url,comments or gh api repos/<owner>/<repo>/issues/<N>/comments — and read the most recent ~5. The actual proposal often lives in the thread, not the top-post.

Map the assembled issue (body + relevant comments) to the three shaping questions; flag missing fields and ask the user to clarify before returning the proposal. The proposal returned must include the issue link as Source so the per-experiment plan file can cite it.

What is returned

A short structured block, not a plan file:

Proposal (from: user | issue #<N> | spec-repo:<path>):
  Question:        <one sentence>
  Motivation:      <quote / link / spec-file path>
  Method outline:  <prose; which file in src/<pkg>/ is touched>
  Open gaps:       <anything the user / issue / spec didn't answer>

iterate-ml-experiment consumes this and drafts plan/NN_short_name.md. No Success field — the skill deliberately does not author acceptance criteria; the user judges the result post-run.

Companion skills

iterate-ml-experiment — the caller; owns the plan file.
iterate-from-literature / iterate-from-methodology / iterate-from-diagnostic — sibling strategies for when the user is open-ended.