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skill: raw artifacts in P1, seed-noise and baseline before A-beats-B, fresh auditor, follow the job
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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@@ -33,8 +33,8 @@ one change with three names.
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## P1. A run finished or crashed
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Steps 1 to 4 are what reading a run means, so do them even when you were asked only to read a log.
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They write nothing except your reply. Steps 5 to 7 change files, so they wait for a task that asks
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Steps 1 to 5 are what reading a run means, so do them even when you were asked only to read a log.
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They write nothing except your reply. Steps 6 to 8 change files, so they wait for a task that asks
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you to audit, decide, or act.
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1. Open the log and read it end to end. State its length and confirm you read all of it. A tail
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@@ -47,16 +47,19 @@ you to audit, decide, or act.
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Read each curve at four points, because the shape carries the diagnosis and the last number does
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not. If a metric that would settle a row does not exist, say which one, and add it to the code
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before the next run. STOP here if any cell is empty.
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3. List every anomaly, including ones you would rather ignore. Each line ends explained, or being
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3. Open what the run produced, not only what it printed. Quote one complete generation, prediction
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or output per arm, chosen at random, and say how you chose them. Metrics summarise, and a
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collapsed, truncated or identical output is invisible in a mean.
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4. List every anomaly, including ones you would rather ignore. Each line ends explained, or being
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investigated now. An anomaly you found without looking for it is a big problem.
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4. Write the prediction check against the P2 table from before this run, one row per prediction,
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5. Write the prediction check against the P2 table from before this run, one row per prediction,
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marked supported, contradicted, or unresolved. Write "no predictions were recorded" if there
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were none, and then record them next time.
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5. Run `/auditlog`. It owns the full audit and writes the audit file.
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6. Open `TRAINING_GUIDE.md`, edit the stage this run touched, and write the earliest step of that
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6. Run `/auditlog`. It owns the full audit and writes the audit file.
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7. Open `TRAINING_GUIDE.md`, edit the stage this run touched, and write the earliest step of that
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stage's expected sequence that evidence does not yet support. If the file got longer, you
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appended history instead of replacing a stale claim.
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7. Say what to run next and why it beats finishing, repeating, or cancelling work already queued.
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8. Say what to run next and why it beats finishing, repeating, or cancelling work already queued.
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Cancel the queued work that no longer answers the question. Never stop applies to the research
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goal, never to one experiment, sweep or hypothesis.
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@@ -81,6 +84,9 @@ the reference code.
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4. Add any metric whose row says no. A run that cannot separate success from failure is not worth
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the GPU time.
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5. Overfit about 20 samples and paste the final loss. Near zero, or stop and fix that first.
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6. Follow the job after you queue it, one background follow per job, so the finish wakes you and
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you read that log. Use `pqf <id>` here, or `pueue wait <id>` through `pi-processes` under Pi. A
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job nobody follows is a log nobody reads.
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## P3. You are about to report a result, or to say it looks fine
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@@ -97,7 +103,12 @@ are claims, and they are the claims least likely to have been checked.
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3. Write the five most likely ways this result is invalid, each with the check that would settle it.
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4. Quote complete raw samples chosen at random, and say how you chose them. Samples picked because
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they look clean prove nothing.
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5. Only now write your conclusion.
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5. To claim A beats B, name the baseline, name the chance level, and say how the gap compares with
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the spread between seeds of one arm. One seed each means unresolved, which is a permitted answer.
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[refs/sweeps.md](refs/sweeps.md) has the paired comparison.
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6. When the result matters or surprises you, send a fresh-eyes subagent the artifact with no story
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attached, and ask what the evidence supports. Report what it says, including agreement.
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7. Only now write your conclusion.
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Start from a substantial probability that a surprising result is invalid, and lower it as checks
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rule out bugs, leakage and broken evaluation. An exciting result is more likely false than a boring
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