القائمة
Use when concluding an ML experiment - produce a sober conclusion that separates confirmed results from exploratory observations and states the highest verified rung.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Use when you have a written implementation plan to execute in a separate session with review checkpoints
Use when turning a rough ML idea into a runnable experiment - define the experiment card (question, hypothesis, baseline, primary metric, budget, cheapest verification rung) before writing training code.
Use when planning how to verify an ML experiment cheaply before expensive runs - design the R0-R7 ladder from local sanity checks to full study, with promotion/stop criteria.
Use when executing implementation plans with independent tasks in the current session
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
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| name | ml-result-review |
| description | Use when concluding an ML experiment - produce a sober conclusion that separates confirmed results from exploratory observations and states the highest verified rung. |
You are concluding an ML experiment, not selling it. Passing tests showed the code path runs; they never showed the method works. Your job here is a SOBER conclusion that resists over-claiming, written so your human partner can decide on real evidence.
Core principle: Confirmed, exploratory, and failed are different things. Keep them apart.
This is downstream of superpowers-ml:ml-feedback-ladder (which owns the rungs) and superpowers-ml:verification-before-completion (evidence before claims). Cite the highest rung you actually reached, never the one you hoped for.
A result is CONFIRMED only when the required full or equivalent evaluation completed under the declared primary metric and met the locked success criterion.
Never claim the method beats a baseline without the full or equivalent evaluation under the locked primary metric.
Write the conclusion under these explicit headings, in order:
What met the locked success criterion under the primary metric. One line per claim, each tied to the evaluation that proved it. If nothing is confirmed, write "None."
Interesting observations that were NOT the locked test: secondary metrics, partial runs, eyeballed curves, single seeds. Label clearly so no one mistakes these for results.
Runs that did not finish the locked evaluation, and WHY. Distinguish scientific failure (method/config wrong) from operational interruption (infra). An operational interruption belongs here only if it actually prevented the evaluation from completing.
State the top rung (R0-R7) actually reached and the artifact that proves it (log path, metric value, checkpoint, decision memo). Use the canonical wording from superpowers-ml:ml-feedback-ladder.
What remains unproven. What a skeptic would still doubt. Which rungs are not yet climbed.
The single next experiment that closes the most important gap. Name its target rung. This is what you hand your human partner to decide on.
Every conclusion must separate what the evidence supports from what it does not. Model the language exactly:
Supported: implementation trains stably through R5 pilot.
Not supported yet: variant beats baseline at full scale.
Exploratory: early loss looked better, but this was not the
locked primary metric.
If you cannot phrase a claim as "Supported:" with its evidence, it is not supported. Demote it.
Close with the standard rung statement, naming the highest GREEN rung and what is still unverified above it, e.g.:
Verified through R5 (short pilot). Not yet verified by full study.
Separate confirmed from exploratory from failed. State the highest rung you actually climbed, with its artifact. Name what is still unproven.
A sober "not supported yet" is a real result. An over-claim is a liability.