wassname
a90624b36d
SKILL.md: 478-line PINN training best practices (complexity ladder, nondim, architecture, optimization, loss design, sampling, property mappings, ConFIG, domain decomposition). docs/evidence/: 6 files -- krishnapriyan2021, sukumar2022, wang2022 causal, wang2022+2023 expert guides, Brunton youtube transcripts. Missing evidence (to fetch): Wang 2001.04536 (gradient pathologies), Rathore 2402.01868 (ICML loss landscape). Author: wassname (https://github.com/wassname)
ML Debugging Folklore
Deep research to uplift LLMs for ML debugging. Opinionated by source selection.
Distilled from Schulman's "Nuts and Bolts" talk, Andy Jones' debugging guide, Goodfellow Ch11, CS231n, FSDL, and more. Every non-obvious claim is traced to a verbatim source quote in docs/ml_debug_folklore.argdown (vargdown format).
Author: wassname
What's here
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SKILL.md -- the main artifact. Designed to be loaded into an LLM agent's context as a debugging skill. Parts 1-5 are reference knowledge; Part 6 is a runnable triage protocol (grep patterns, diagnostic code snippets, decision tree); Part 7 is debugging mental models and practitioner priors.
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docs/ml_debug_folklore.argdown -- vargdown source map. Traces each claim to an exact quote + file in
docs/evidence/. -
docs/evidence/ -- frozen local copies of source material (blog posts, talks, papers, reddit threads).
Use as a Claude skill
/skills add https://github.com/wassname/ml_debug
Or paste SKILL.md into your system prompt / context when debugging.
Sources
Schulman (2017), Jones (2021), Rahtz (2018), Goodfellow et al. (Deep Learning book), Karpathy (CS231n), Ng (CS229), FSDL, Henderson et al. (2018), McCandlish et al. (2018), Irpan (2018), Slavv (2017), and Reddit.