fix skill install link and enforce audits

Co-Authored-By: PI[openai-codex] <288921227+claudypoo@users.noreply.github.com>

.github/workflows/audit.yml

@@ -0,0 +1,15 @@
+name: Audit
+
+on:
+  pull_request:
+  push:
+
+jobs:
+  audit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+      - run: python scripts/audit.py --self-test .

README.md

@@ -5,7 +5,7 @@ In an attempt to upskill the machine learning debugging on AI coding assistants
 ## Use as a Claude skill
 
 ```
-/skills add https://github.com/wassname/ml_debug
+/skills add https://github.com/wassname/ml-debug
 ```
 
 Or paste `SKILL.md` into your system prompt / context when debugging.
@@ -712,6 +712,6 @@ That is the difference of two arm means over 12 answers each, not a paired diffe
   title = {ML Debugging Folklore: A Practitioner Debugging Skill for LLM Agents},
   author = {Michael J. Clark},
   year = {2026},
-  url = {https://github.com/wassname/ml_debug/}
+  url = {https://github.com/wassname/ml-debug/}
 }
 ```

references/llm_judges.md

@@ -42,7 +42,7 @@ From Wang's calibration framework and verdict's best-practices page:
 - Score both orderings and aggregate (Wang's Balanced Position Calibration); at minimum, randomize position and check the flip rate.
 - Use a different model family for the judge (and for any verifier-of-the-judge) than the one being evaluated. Same-model verification produces a positive skew "that may not discriminate faithfully".[^verdict]
 - Inspect the raw score distribution before trusting means: mode collapse or skew means the scale isn't being used.
-- Spot-check judge verdicts against your own reading of ~20 transcripts (the [Ng error-analysis move](../SKILL.md#inspect-the-data-first), applied to the judge).
+- Spot-check judge verdicts against your own reading of ~20 transcripts (the [Ng error-analysis move](../README.md#inspect-the-data-first), applied to the judge).
 - Judge quality is benchmarkable: [JudgeBench](https://huggingface.co/spaces/ScalerLab/JudgeBench) ranks judges on objective-correctness pairs.
 
 ## Choosing the judge model
@@ -68,9 +68,9 @@ Earn the rubric's ink:
 
 Read a whole trace, not the aggregate:
 
-- Read one complete judge trace end to end: system prompt, user prompt, the exact chat template and special tokens, the judge's saved reasoning, and its reply. Formatting bugs corrupt a judge the way they corrupt any model (see the [template/BOS-mismatch failure](../SKILL.md#chat-template-and-bos-handling-must-match-across-train-and-deploy-unsloth)). Hamel Husain: "You cannot write a good judge prompt until you've seen the data."[^hamel]
+- Read one complete judge trace end to end: system prompt, user prompt, the exact chat template and special tokens, the judge's saved reasoning, and its reply. Formatting bugs corrupt a judge the way they corrupt any model (see the [template/BOS-mismatch failure](../README.md#chat-template-and-bos-handling-must-match-across-train-and-deploy-unsloth)). Hamel Husain: "You cannot write a good judge prompt until you've seen the data."[^hamel]
 - Read both compared outputs for every scenario, not just the winner or aggregate. Verify A and B are not accidentally identical and that both are coherent, on-task, non-refusing, complete, and untruncated.
-- Could you reproduce the verdict from only what the judge sees? If you can't judge it, neither can the model. This is the [Ng error-analysis move](../SKILL.md#inspect-the-data-first) applied to the judge.
+- Could you reproduce the verdict from only what the judge sees? If you can't judge it, neither can the model. This is the [Ng error-analysis move](../README.md#inspect-the-data-first) applied to the judge.
 
 Setup-repair principle: confusion is evidence against the evaluation setup before it is evidence against the model. Use this checklist:
 
@@ -102,7 +102,7 @@ Before plotting or ranking, classify every missing score. A model refusal or tas
 Check stability across order and repeats:
 
 - Position: score both orderings, map back to arm identity, report strict reversals (mechanics in the mitigation checklist above). Watch for a judge that always picks A, sometimes a model does this in protest.
-- Repeat variance: run N>=3-4 identical judgements and check the spread. If repeats disagree wildly the signal is noise, the same warning as [seed variance](../SKILL.md#seed-variance-you-cant-tell-a-bug-from-bad-luck): "Instability to random seed is like a canary in a coal mine."
+- Repeat variance: run N>=3-4 identical judgements and check the spread. If repeats disagree wildly the signal is noise, the same warning as [seed variance](../README.md#seed-variance-you-cant-tell-a-bug-from-bad-luck): "Instability to random seed is like a canary in a coal mine."
 
 ## Repeat draws, temperature, and paired differences
 

scripts/audit.py

@@ -26,7 +26,12 @@ def authored_markdown(root: Path) -> list[Path]:
     return [
         path
         for path in sorted(root.rglob("*.md"))
-        if ".git" not in path.parts and not is_frozen_evidence(path, root)
+        if (
+            ".git" not in path.parts
+            and "slop" not in path.parts
+            and path.relative_to(root).parts[:2] != ("docs", "spec")
+            and not is_frozen_evidence(path, root)
+        )
     ]
 
 
@@ -254,6 +259,10 @@ def self_test() -> None:
     with tempfile.TemporaryDirectory() as directory:
         clean = Path(directory) / "clean"
         write_fixture(clean)
+        (clean / "slop").mkdir()
+        (clean / "slop" / "scratch.md").write_text("[broken](missing.md)\n")
+        (clean / "docs" / "spec").mkdir(parents=True)
+        (clean / "docs" / "spec" / "scratch.md").write_text("[broken](missing.md)\n")
         assert not audit(clean), audit(clean)
     for expected, mutate in mutations:
         with tempfile.TemporaryDirectory() as directory: