Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/axgord/claude-workflow/mathnpx skills add AxGord/claude-workflow --skill mathgit clone --depth 1 https://github.com/AxGord/claude-workflowWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/axgord/claude-workflow/math)<a href="https://agentmods.dev/skills/axgord/claude-workflow/math"><img src="https://agentmods.dev/badge/skills/axgord/claude-workflow/math.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00026 | $0.01132 |
| Opus 5 | $0.00013 | $0.00566 |
| Sonnet 5 | $0.00005 | $0.00226 |
| Haiku 4.5 | $0.00003 | $0.00113 |
Grade A, and why
math scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Math — Verified Gotchas
Central Binomial Coefficient C(n, n/2) overflow
| Type | Last fits | First overflow |
|---|---|---|
| Int64 | C(66, 33) = 7,219,428,434,016,265,740 | C(67, 33) |
| UInt64 | C(67, 33) = 14,226,520,737,620,288,370 | C(68, 34) |
Models consistently underestimate this boundary (a common wrong answer is n=62; correct is n=66 for Int64).
Result vs computation: the table bounds the RESULT. Intermediate products in the usual res = res * (n-k+i) / i loop overflow earlier (the multiply happens before the divide) — the last few safe n need 128-bit intermediates or a divide-first formulation.
Cross-runtime trig: V8 Math.tan ≠ CPython math.tan by 1 ULP
When building bit-exact parity between TS/JS and Python implementations of
the same math, tan is the single biggest offender. V8 and CPython do
not share a single implementation (V8 ships its own fdlibm-derived routines;
CPython calls platform libm), and tan's range reduction produces a
different LSB from naive sin(x)/cos(x) for some angles — which layer is
responsible varies by platform.
Fix: compute tan(x) as sin(x) / cos(x) in BOTH languages. sin and
cos round-tripped bit-identically across the tested runtimes.
| Op | V8 Math.X vs CPython math.X |
Use? |
|---|---|---|
sin, cos |
bit-identical (observed) | yes |
sqrt |
bit-identical (IEEE-754 mandates correctly-rounded) | yes |
tan |
1 ULP drift possible | avoid — use sin/cos |
** (pow) |
1-2 ULP drift possible | accept tolerance |
atan, atan2, exp, log |
1-2 ULP drift possible | accept tolerance |
Even after sin/cos swap, pow(x, y) (e.g. step_mult = floor + norm**exp)
can still produce 1-ULP-different doubles between V8 and CPython for some
inputs. If the math identity is preserved (same algorithm, same constants,
sin/cos for tan), math.isclose(rel_tol=1e-12, abs_tol=1e-9) covers ~2 ULP
at typical magnitudes and is the right tolerance for "bit-exact in spirit".
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 77 lines · 26 tokens per session scan A c8e3061e569e
math is a skill published in the GitHub repository AxGord/claude-workflow (5 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,132 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
nature-statistics
Audit, revise, or draft manuscript statistical reporting for Nature / high-impact journal submissions. Use when the user asks to check statistical analysis sections, p values, confidence intervals, sample size, biological versus technical replicates, randomization, blinding, multiple-comparison correction, model…
evaluating-with-leakage-gates
Evaluate an OpenMed de-identification or clinical NER model against the leakage-first release gates G1a through G8, which gate releases on residual PHI leakage rather than on F1. Use when the user wants to run the OpenMed eval harness on a synthetic golden set, decide whether a de-id model is RELEASABLE or…
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…
mixed-precision
Use FP16/BF16 mixed precision to accelerate training and reduce memory. Use when optimizing GPU performance.
indication-dossier
Build a source-backed biomedical indication dossier. Use when a research task asks for disease biology, target rationale, patient segmentation, biomarkers, trials, drugs, competitive landscape, or translational evidence.