What is cross-language-check?

A verification tool that repeats a quantitative analysis in a second programming language and compares the results. For example, it can compare an R analysis with a Python, Stata, or Julia version.

Use it to cross-check existing empirical results across R, Python, Stata, and Julia implementations.

Independent implementations can reveal coding mistakes when their estimates or outputs disagree.

cross-language-check — Skill for Claude Code by flonat/flonat-research

Install

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.

Any agent

npx skills add flonat/flonat-research --skill cross-language-check

Clone the repo

git clone --depth 1 https://github.com/flonat/flonat-research

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/flonat/flonat-research/cross-language-check/github.svg)](https://agentmods.dev/skills/flonat/flonat-research/cross-language-check)

Your own site

<a href="https://agentmods.dev/skills/flonat/flonat-research/cross-language-check"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/cross-language-check/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/flonat/flonat-research/cross-language-check"><img src="https://agentmods.dev/badge/skills/flonat/flonat-research/cross-language-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,065 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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 5.1	$0.00058	$0.02065
Opus 5.5	$0.00023	$0.00826
Sonnet 5.5	$0.00012	$0.00413
Haiku 4.5	$0.00006	$0.00206

Measured 2mo ago against content hash 3656e2901a37, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cross-language-check 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 2mo 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.

skills/cross-language-check/SKILL.md · 180 lines

How it starts

The opening of the file, as written. The whole thing — 180 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cross-Language Replication Check

Level 1 of the verification hierarchy: same specification → same estimate across languages. If two independent implementations disagree, at least one has a bug.

Output Path

Per rules/review-artefact-routing.md (auto-loads in research projects (path-scoped to paper-*/ and paper/)):

Source slug: cross-language-check
Write reports to: reviews/<scope>/cross-language-check/<YYYY-MM-DD-HHMM>.md inside the project, where <scope> is the paper slug (e.g., paper-jtp) for paper-level checks or _project for project-level checks. Path is relative to the research project root, not the Task-Management repo.
Never at project root (./CRITIC-REPORT.md-style filenames are forbidden — pre-rule layout).
Idempotency: if today's file exists, append a same-day descriptor ({date}-revision.md, {date}-r2.md, {date}-pre-submission.md) — never overwrite.
Index update: if reviews/INDEX.md exists, write a one-line entry under "Latest per source" pointing at the new file. Otherwise review-recap will rebuild the index next time it runs.
Infrastructure repos (Task-Management, atlas-workspace, etc.): this section does not apply — the path-scoped rule won't load there.

When to Use

Before submitting a paper with quantitative results
When you suspect a subtle bug in estimation code
After refactoring analysis scripts
As a robustness check that reviewers increasingly expect
When switching languages for a collaborator

When NOT to Use

Pure simulation code with no statistical estimation → computational-experiments
The analysis is trivial (descriptive stats only) — not worth the overhead
The source script uses language-specific packages with no equivalent (e.g., bespoke Bayesian MCMC)

Workflow

Phase 1: Parse Source Script

Read the source script — identify language, packages, estimation calls
Extract the specification:
- Data loading and cleaning steps
- Variable construction and transformations
- Estimation command(s) with exact formula/model specification
- Standard error clustering, weights, fixed effects
- Sample restrictions and filters
Identify key outputs — point estimates, standard errors, p-values, confidence intervals, N
Flag untranslatable elements — language-specific features that may need adaptation (e.g., R formula syntax, Stata factor variables, Python sklearn pipelines)

Read the full file on GitHub · 180 lines

Changes

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.

2mo ago First seen · 180 lines · 58 tokens per session scan A 3656e2901a37

Subscribe to this mod's changes

cross-language-check is a skill published in the GitHub repository flonat/flonat-research (149 stars, last pushed 3d ago), licensed MIT. It adds 58 tokens to every session and 2,065 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-03.

Claude Code LaTeX Languages Scientific academic-research ai-tools claude phd

cross-language-check next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
cross-language-checkflonat/flonat-research	Skill	149	3d ago	MIT	A	58	Claude Code
alterlab-anndataAlterLab-IEU/AlterLab-Academic-Skills	Skill	85	yesterday	MIT	A	133	Claude Code
alterlab-rdkitAlterLab-IEU/AlterLab-Academic-Skills	Skill	85	yesterday	MIT	A	130	Claude Code
alterlab-sympyAlterLab-IEU/AlterLab-Academic-Skills	Skill	85	yesterday	MIT	A	86	Claude Code
alterlab-cirqAlterLab-IEU/AlterLab-Academic-Skills	Skill	85	yesterday	MIT	A	158	Claude Code
alterlab-pydeseq2AlterLab-IEU/AlterLab-Academic-Skills	Skill	85	yesterday	MIT	A	88	Claude Code
alterlab-pymooAlterLab-IEU/AlterLab-Academic-Skills	Skill	85	yesterday	MIT	A	83	Claude Code