diff --git a/PLAYBOOK.md b/PLAYBOOK.md
index 7d1d546..9425336 100644
--- a/PLAYBOOK.md
+++ b/PLAYBOOK.md
@@ -223,4 +223,4 @@ Folklore sources (the quotes above trace to these):
 
 For modern transformer pretraining specifically (the sources above predate it), see [Karpathy's recipe](https://karpathy.github.io/2019/04/25/recipe/) and the [nanochat deepwiki](https://deepwiki.com/karpathy/nanochat) (320+ empirical HP sweeps for a GPT-2-scale run). Most multi-source claims trace to quotes in [docs/ml_debug_folklore.argdown](docs/ml_debug_folklore.argdown) (vargdown); the full evidence set is in [docs/evidence/](docs/evidence/).
 
-Curated by [wassname](https://github.com/wassname). Companion gist: https://gist.github.com/wassname/d72da69ffe1bdb60cdb1708bd3b5c535
+Curated by [wassname](https://github.com/wassname).
diff --git a/SKILL.md b/SKILL.md
index 7ef682a..8d3a996 100644
--- a/SKILL.md
+++ b/SKILL.md
@@ -324,4 +324,4 @@ Folklore sources (the quotes above trace to these):
 
 For modern transformer pretraining specifically (most sources above predate it), see [Karpathy's recipe](https://karpathy.github.io/2019/04/25/recipe/) and the [nanochat deepwiki](https://deepwiki.com/karpathy/nanochat) (320+ empirical HP sweeps for a GPT-2-scale run). For LLM-as-judge eval debugging workflow more broadly, Hamel Husain's ["Your AI Product Needs Evals"](https://hamel.dev/blog/posts/evals/) covers the error-analysis-first approach for LLM products. Most multi-source claims trace to quotes in [docs/ml_debug_folklore.argdown](docs/ml_debug_folklore.argdown) (vargdown); the full evidence set is in [docs/evidence/](docs/evidence/).
 
-Curated by [wassname](https://github.com/wassname). Companion gist: https://gist.github.com/wassname/d72da69ffe1bdb60cdb1708bd3b5c535
+Curated by [wassname](https://github.com/wassname).
diff --git a/pinn/SKILL.md b/pinn/SKILL.md
index ed19a24..2beb8c7 100644
--- a/pinn/SKILL.md
+++ b/pinn/SKILL.md
@@ -6,9 +6,6 @@ description: "PINN (Physics-Informed Neural Network) training best practices and
 
 # PINN Training Best Practices
 
-https://gist.github.com/wassname/a68314f731002b32aa637c3d43a081c9/edit
-
-
 Consolidated from: NeuralPDE.jl tests/docs, ConFIG repo, Wang et al. 2021, Rathore et al. 2024 (ICML), ml_debug folklore, and practical experience. Heat-exchanger-specific notes in [refs/heat_exchanger.md](refs/heat_exchanger.md).
 
 Epistemic status: Patterns confirmed across multiple sources. Where sources disagree, noted. Paper claims marked with credence estimates.