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diagnostics: add NaN-poisoning leakage tracer + Karpathy backprop-to-input check; README citation

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NaN poisoning: inject NaN where info must not come from (future/test/labels), run the real pipeline, assert past outputs stay finite. Documents false negatives (pandas skipna, nanmean) and false positives (softmax rows, batch stats). Backprop-to-input is its gradient dual for inside the model; quote already frozen in docs/evidence/karpathy_recipe_training_nn_2019.md.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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wassname
2026-06-11 10:18:51 +08:00
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README.md
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@@ -15,3 +15,14 @@ Or paste `SKILL.md` into your system prompt / context when debugging.
- **[SKILL.md](SKILL.md)** -- the main artifact. Load into an LLM agent's context as a debugging skill. Leads with the mindset (calibrate, mental models, general debugging tricks, and reading a working implementation when stuck), then a folklore section of sourced quotes, then an LLM-agent playbook (debugging loop, triage menu, anti-patterns). Deeper one-off tricks (loss-surface analysis, stuck-metric diagnosis, sweep reliability) live in [refs/](refs/).
- **[docs/evidence/](docs/evidence/)** -- frozen local copies of source material (blog posts, talks, papers, reddit threads). Claims in SKILL.md link back to exact quotes here.
## Citation
```bibtex
@misc{wassname2026mldebug,
title = {ML Debugging Folklore: A Practitioner Debugging Skill for LLM Agents},
author = {Michael J. Clark},
year = {2026},
url = {https://github.com/wassname/ml_debug/}
}
```