Claude-skill-registry-data ml-training-debugger

Diagnose and stabilize ML training runs, recover from failures, and deliver validated fixes.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry-data

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/ml-training-debugger" ~/.claude/skills/majiayu000-claude-skill-registry-data-ml-training-debugger && rm -rf "$T"

manifest: data/ml-training-debugger/SKILL.md

source content

STANDARD OPERATING PROCEDURE

Purpose

Rapidly triage ML training incidents (instability, divergence, degraded metrics) and deliver validated remediations with traceable evidence.

Triggers

Positive: Failing/unstable training runs, unexplained metric drops, NaNs/exploding gradients, data/label issues, reproducibility gaps.
Negative: New model development without incident (route to
```
ml-expert
```
) or lightweight prototyping (route to
```
ml
```
).

Guardrails

Structure-first: ensure
```
SKILL.md
```
,
```
README
```
,
```
examples/
```
,
```
tests/
```
,
```
resources/
```
, and
```
agents/
```
exist; create missing docs before work.
Constraint extraction: clarify environment (hardware/framework), data provenance, metric targets, and incident timeline.
Validation discipline: reproduce issue, isolate variables (data/model/optim), run minimal change tests; adversarially probe for leakage and nondeterminism.
Confidence ceiling enforced (inference/report 0.70; research 0.85; observation/definition 0.95) with evidence per finding.
Safety: preserve checkpoints/logs; avoid destructive changes; keep rollback path ready.

Execution Phases

Intake & Evidence Gathering
- Collect logs, metrics, configs, seeds, hardware info, and recent code changes.
- Confirm baseline vs expected behavior and incident start time.
Reproduction & Isolation
- Reproduce on smallest dataset slice; fix seeds; disable randomness.
- Binary-search variables: data batches, preprocessing, model changes, optimizer settings.
Hypothesis & Experiment Plan
- Form hypotheses (data corruption, label leakage, optimizer instability, precision issues).
- Plan targeted experiments with success/fail criteria.
Fix & Validation
- Implement minimal fixes; run controlled tests (train/val curves, gradient norms, loss stats).
- Validate against performance/latency targets; ensure no regression on baseline metrics.
Handoff & Prevention
- Document root cause, applied fixes, and remaining risks.
- Add monitors/tests to prevent recurrence; package rollback instructions.

Output Format

Incident summary and constraints.
Reproduction steps, hypotheses, and experiments run.
Fixes applied with before/after metrics.
Prevention plan and owners.
Confidence statement with ceiling.

Validation Checklist

Issue reproduced with fixed seeds and minimal data slice.
Hypotheses tested; experiments documented.
Metrics reported per split; gradients/loss inspected where relevant.
Regression checks executed; rollback path documented.
Confidence ceiling stated.

VCL COMPLIANCE APPENDIX (Internal)

[[HON:teineigo]] [[MOR:root:H-T-A]] [[COM:Hata+Teshis+Analiz]] [[CLS:ge_skill]] [[EVD:-DI<gozlem>]] [[ASP:nesov.]] [[SPC:path:/skills/specialists/ml-training-debugger]]

[[HON:teineigo]] [[MOR:root:E-P-S]] [[COM:Epistemik+Tavan]] [[CLS:ge_rule]] [[EVD:-DI<gozlem>]] [[ASP:nesov.]] [[SPC:coord:EVD-CONF]]

Confidence: 0.74 (ceiling: inference 0.70) - SOP rebuilt with prompt-architect constraint discipline and skill-forge structure/validation rules.