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Get Started Free →Verifies math-heavy code for algorithmic correctness and numerical stability. Use when reviewing scientific algorithms, ML models, or numerical code.
.claude/skills/athola-math-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 129% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-13 | ✗→✓ | ▲ Improved | 107% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 1% | 0% |
Intensive analysis ensuring numerical stability and alignment with standards.
bash/math-review
Verification: Run the command with --help flag to verify availability.
use architecture-review
math-review:context-syncedmath-review:requirements-mappedmath-review:derivations-verifiedmath-review:stability-assessedmath-review:evidence-loggedmath-review:findings-verifiedbashpwd && git status -sb && git diff --stat origin/main..HEAD
Verification: Run git status to confirm working tree state. Enumerate math-heavy files (source, tests, docs, notebooks). Classify risk: safety-critical, financial, ML fairness.
Translate requirements → mathematical invariants. Document pre/post conditions, conservation laws, bounds. Load: modules/requirements-mapping.md
Re-derive formulas using CAS. Challenge approximations. Cite authoritative standards (NASA-STD-7009, ASME VVUQ). Load: modules/derivation-verification.md
Evaluate conditioning, precision, scaling, randomness. Compare complexity. Quantify uncertainty. Load: modules/numerical-stability.md
bashpytest tests/math/ --benchmark jupyter nbconvert --execute derivation.ipynb
Verification: Run pytest -v tests/math/ to verify. Log deviations, recommend: Approve / Approve with actions / Block. Load: modules/testing-strategies.md
math-review:findings-verified)Every issue must cite a real location and a verbatim anchor. Write findings to .review/findings.json and confirm each citation resolves:
bashpython plugins/imbue/scripts/citation_verifier.py \ --findings .review/findings.json --repo-root .
Drop or label UNVERIFIED any finding the verifier fails (exit 1); only verified findings enter the report. See Skill(imbue:review-core) Step 5 for the protocol and Skill(imbue:structured-output) for the finding schema.
Default (200 tokens): Core workflow, checklists +Requirements (+300 tokens): Invariants, pre/post conditions, coverage analysis +Derivation (+350 tokens): CAS verification, standards, citations +Stability (+400 tokens): Numerical properties, precision, complexity +Testing (+350 tokens): Edge cases, benchmarks, reproducibility
Total with all modules: ~1600 tokens
Correctness: Formulas match spec | Edge cases handled | Units consistent | Domain enforced Stability: Condition number OK | Precision sufficient | No cancellation | Overflow prevented Verification: Derivations documented | References cited | Tests cover invariants | Benchmarks reproducible Documentation: Assumptions stated | Limitations documented | Error bounds specified | References linked
markdown## Summary [Brief findings] ## Context Files | Risk classification | Standards ## Requirements Analysis | Invariant | Verified | Evidence | ## Derivation Review [Status and conflicts] ## Stability Analysis Condition number | Precision | Risks ## Issues [M1] [Title] - Location: file.py:123 - Anchor: `verbatim source text at line 123` - Issue: [what is wrong] | Fix: [remediation] | Evidence: [E1] ## Recommendation Approve / Approve with actions / Block
Every issue's Anchor is the exact source text at Location; it is what citation_verifier.py re-reads to prove the finding is real. Verification: Run the command with --help flag to verify availability.
Location + verbatim Anchor, and citation_verifier.py confirmed all citations (exit 0) or unverified issues were dropped or labeled UNVERIFIED| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-10 | pass→pass | 21,444 | 17,435 | -19% | 1 | 1 | 0% | 3,771 | 3,605 | -4% | 0 | 0 | — |
case-01 | fail→fail | 33,037 | 22,188 | -33% | 1 | 1 | 0% | 6,250 | 5,028 | -20% | 0 | 0 | — |
case-02 | fail→fail | 11,951 | 17,020 | +42% | 1 | 1 | 0% | 357 | 1,587 | +345% | 0 | 0 | — |
case-03 | fail→fail | 33,851 | 11,458 | -66% | 1 | 1 | 0% | 5,963 | 1,580 | -74% | 0 | 0 | — |
case-04 | pass→pass | 15,520 | 29,403 | +89% | 1 | 1 | 0% | 2,540 | 5,328 | +110% | 0 | 0 | — |
case-05 | pass→pass | 15,313 | 12,428 | -19% | 1 | 1 | 0% | 2,466 | 3,402 | +38% | 0 | 0 | — |
case-06 | pass→pass | 15,539 | 14,302 | -8% | 1 | 1 | 0% | 2,626 | 3,816 | +45% | 0 | 0 | — |
case-07 | fail→pass | 8,280 | 3,557 | -57% | 1 | 1 | 0% | 1,355 | 1,812 | +34% | 0 | 0 | — |
case-08 | fail→fail | 7,083 | 3,429 | -52% | 1 | 1 | 0% | 1,075 | 1,814 | +69% | 0 | 0 | — |
case-09 | fail→pass | 4,683 | 2,196 | -53% | 1 | 1 | 0% | 706 | 1,620 | +129% | 0 | 0 | — |
case-11 | pass→pass | 15,563 | 9,169 | -41% | 1 | 1 | 0% | 2,486 | 2,855 | +15% | 0 | 0 | — |
case-12 | fail→pass | 7,339 | 2,931 | -60% | 1 | 1 | 0% | 1,242 | 1,626 | +31% | 0 | 0 | — |
case-13 | fail→pass | 4,867 | 2,721 | -44% | 1 | 1 | 0% | 786 | 1,624 | +107% | 0 | 0 | — |
case-14 | pass→pass | 17,230 | 10,742 | -38% | 1 | 1 | 0% | 2,686 | 2,947 | +10% | 0 | 0 | — |
case-15 | fail→pass | 15,852 | 6,461 | -59% | 1 | 1 | 0% | 2,375 | 2,405 | +1% | 0 | 0 | — |
case-16 | pass→pass | 11,114 | 5,910 | -47% | 1 | 1 | 0% | 1,750 | 2,160 | +23% | 0 | 0 | — |
case-17 | fail→pass | 7,556 | 2,032 | -73% | 1 | 1 | 0% | 1,180 | 1,586 | +34% | 0 | 0 | — |
case-18 | fail→fail | 12,170 | 2,310 | -81% | 1 | 1 | 0% | 1,960 | 1,614 | -18% | 0 | 0 | — |
case-19 | pass→pass | 10,153 | 10,455 | +3% | 1 | 1 | 0% | 1,590 | 3,001 | +89% | 0 | 0 | — |
case-20 | fail→fail | 6,484 | 2,600 | -60% | 1 | 1 | 0% | 1,105 | 1,716 | +55% | 0 | 0 | — |
case-21 | fail→pass | 13,461 | 1,619 | -88% | 1 | 1 | 0% | 1,951 | 1,488 | -24% | 0 | 0 | — |
case-22 | fail→pass | 13,591 | 4,965 | -63% | 1 | 1 | 0% | 2,143 | 2,138 | -0% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 20 counted toward the lift figure. The other 2 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +36 percentage points is the difference between those two pass rates over the 20 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.