Wasserstein Audit - Order Consistency

VerifiedSafe

Scan all manuscript drafts and codebase for W1 vs W2 inconsistencies. Resolves the blocking Phase 0 deliverable.

Sby Skills Guide Bot
Data & AIIntermediate
207/24/2026
Claude Code
#wasserstein#audit#consistency#manuscript#code-review

Recommended for

Our review

This skill scans manuscript drafts and codebase files for inconsistencies between W₁ and W₂ Wasserstein order usage, enforcing a project convention.

Strengths

  • Thorough detection of W₁/W₂ patterns across LaTeX, Markdown, and Python files.
  • Cross-references findings against a canonical notation standard for centralized validation.
  • Produces a structured report with file, line, issue, and suggested fix.

Limitations

  • Does not auto-fix any issues; user confirmation is required.
  • Only flags predefined patterns; may miss some edge cases.
  • Requires a specific folder structure (papers/ and domain packages) to function correctly.
When to use it

Use during manuscript or code review to ensure consistency with a project mandate for a single Wasserstein order.

When not to use it

Avoid when the project intentionally uses both W₁ and W₂ with clear justification documented.

Security analysis

Safe
Quality score90/100

The skill only reads files and generates a report; it performs no destructive actions, does not execute external commands, and does not interact with the network. It poses no security risk.

No concerns found

Examples

Full audit of papers and code
/wasserstein-audit all
Audit only papers
/wasserstein-audit papers
Audit only code
/wasserstein-audit code

/wasserstein-audit — Audit Wasserstein Order Consistency

Scan all manuscript drafts and codebase for W₁ vs W₂ inconsistencies. Resolves the blocking Phase 0 deliverable: the legacy P01 manuscript uses W₁ in several places while project convention mandates W₂ as the primary metric.

Usage

/wasserstein-audit
/wasserstein-audit [papers|code|all]

Example: /wasserstein-audit all


What this does

  1. Reads papers/shared/notation.md (canonical notation standard, Wasserstein Audit section)
  2. Scans manuscript .md files under papers/ for W₁, unsubscripted W, ambiguous W_p
  3. Scans Python files under all domain packages for Wasserstein calls without explicit p=2
  4. Cross-references findings against what notation.md already has "verified"
  5. Produces a structured reconciliation report — does not fix anything without confirmation

Manuscript patterns that trigger a flag

| Pattern | Issue | |---|---| | W_1 / $W_1$ / W_{1} | W₁ usage — needs justification or must change to W₂ | | $W$ (unsubscripted) | Always flag — violates notation standard | | bottleneck without alongside Wasserstein | Sole metric violation | | wasserstein without explicit order nearby | Ambiguous |

Code patterns that trigger a flag

| Pattern | Issue | |---|---| | wasserstein_distance(a, b) with no p= arg | Default may not be W₂ in all library versions | | p=1 or order=1 near Wasserstein call | Explicit W₁ usage | | bottleneck_distance(...) | Must not be used as sole metric | | gudhi.wasserstein.wasserstein_distance without order=2 | gudhi default not guaranteed |


Report format

## Wasserstein Audit Report — YYYY-MM-DD

### Manuscript Findings
| File | Line | Found | Issue | Suggested Fix |

### Code Findings
| File | Line | Found | Issue | Suggested Fix |

### Status
- Blocking Phase 0: YES / NO
- Total issues: N (manuscript: N₁, code: N₂)

### Recommended Actions

Library defaults (reference)

  • persim.wasserstein_distance — p=2 by default ✓
  • gudhi.wasserstein.wasserstein_distance — requires explicit order= arg
  • giotto-tda Wasserstein vectorisation — p=2 by default ✓
  • W₁ in theory proofs is acceptable in Methods sections if clearly labelled and distinguished from the computational metric used
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