Agent skill

Cross Language Check

Replicate a quantitative analysis in a second language (R↔Python↔Stata↔Julia) and compare outputs for implementation errors.

MITAuto-check passedResearch & Science

Install Cross Language Check

skills CLI

$ npx skills add flonat/flonat-research --skill cross-language-check -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install flonat/flonat-research cross-language-check --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/cross-language-check .claude/skills/cross-language-check && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "cross-language-check" agent skill from https://github.com/flonat/flonat-research/tree/main/skills/cross-language-check into .claude/skills/cross-language-check/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-language-check", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/flonat/flonat-research/tree/main/skills/cross-language-check

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add flonat/flonat-research --skill cross-language-check -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install flonat/flonat-research cross-language-check --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/cross-language-check .agents/skills/cross-language-check && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "cross-language-check" agent skill from https://github.com/flonat/flonat-research/tree/main/skills/cross-language-check into .agents/skills/cross-language-check/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-language-check", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add flonat/flonat-research --skill cross-language-check -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install flonat/flonat-research cross-language-check --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/cross-language-check .cursor/skills/cross-language-check && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "cross-language-check" agent skill from https://github.com/flonat/flonat-research/tree/main/skills/cross-language-check into .cursor/skills/cross-language-check/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-language-check", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/flonat/flonat-research.git --path skills/cross-language-check

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add flonat/flonat-research --skill cross-language-check -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install flonat/flonat-research cross-language-check --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/cross-language-check .gemini/skills/cross-language-check && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "cross-language-check" agent skill from https://github.com/flonat/flonat-research/tree/main/skills/cross-language-check into .gemini/skills/cross-language-check/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-language-check", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install flonat/flonat-research cross-language-check

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add flonat/flonat-research --skill cross-language-check -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/cross-language-check .github/skills/cross-language-check && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "cross-language-check" agent skill from https://github.com/flonat/flonat-research/tree/main/skills/cross-language-check into .github/skills/cross-language-check/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-language-check", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add flonat/flonat-research --skill cross-language-check -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install flonat/flonat-research cross-language-check --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/flonat/flonat-research.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/cross-language-check .opencode/skills/cross-language-check && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "cross-language-check" agent skill from https://github.com/flonat/flonat-research/tree/main/skills/cross-language-check into .opencode/skills/cross-language-check/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "cross-language-check", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: cross-language-check
GitHub stars: 146
Token cost: ~2.1k tokens
SKILL.md length: 772 words
Files: 1
Skills in repo: 83
Repo updated: Sep 29, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Replicate a quantitative analysis in a second language (R↔Python↔Stata↔Julia) and compare outputs for implementation errors.

Works in 6 steps: Parse Source Script → Choose Target Language → Translate → …
An existing empirical result needs independent cross-language verification
SKILL.md covers Output Path, When to Use, When NOT to Use and Workflow, plus 3 more sections
Calls bash

What it does

Cross Language Check is an agent skill from flonat/flonat-research. Replicate a quantitative analysis in a second language (R↔Python↔Stata↔Julia) and compare outputs for implementation errors. Use when an existing empirical result needs independent cross-language verification. Not for reviewing one implementation in place; use $code-suite.

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Econometrics and empirical research. It works with Python. The repository describes itself as: Shareable Claude Code + Codex infrastructure for PhD researchers — skills, agents, hooks, and rules for academic workflows. The licence is MIT.

When your agent uses it

An existing empirical result needs independent cross-language verification
Tasks that involve Econometrics and empirical research

Example prompts

“/cross-language-check”

Requirements

Python 3
Pre-approved tools (allowed-tools): Bash(uv*, Rscript*, stata*, julia*, diff*, mkdir*, ls*, cp*), Read, Write, Edit, Glob, Grep, Agent

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Parse Source Script
Choose Target Language
Translate
Run Both & Compare
Diagnose Discrepancies
Report

What it can do on your machine

Read from SKILL.md and the folder at commit da27600. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
- Bash(uv*
- Rscript*
- stata*
- julia*
- diff*
- mkdir*
- ls*
- cp*)
- Read
- Write
…and 4 more on the same allowed-tools line.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- bash
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Cross Language Check loads about 2.1k tokens when it runs. Until then it costs about 74 tokens; SKILL.md has 772 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~74
When it runs · the whole SKILL.md, loaded when a task matches: ~2.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from flonat/flonat-research at commit da27600, republished under its MIT licence (© flonat). 772 words, ~2,066 tokens.

Download SKILL.mdSave it as .claude/skills/cross-language-check/SKILL.md (or your agent's skills folder).

name: cross-language-check
description: Replicate a quantitative analysis in a second language (R↔Python↔Stata↔Julia) and compare outputs for implementation errors. Use when an existing empirical result needs independent cross-language verification. Not for reviewing one implementation in place; use $code-suite.
allowed-tools: Bash(uv*, Rscript*, stata*, julia*, diff*, mkdir*, ls*, cp*), Read, Write, Edit, Glob, Grep, Agent
argument-hint: <script-path> [--target r|python|stata|julia]

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)

Phase 2: Choose Target Language

If --target is specified, use that. Otherwise:

Source	Default target	Rationale
R	Python	Widest package overlap
Python	R	Strongest econometrics ecosystem
Stata	R	Both strong on panel/causal methods
Julia	Python	Closest syntax mapping

Ask the user to confirm if the default seems wrong for the specific analysis.

Phase 3: Translate

Write the replication script to code/replication/ (or src/replication/):

code/replication/{original_name}_{target_lang}.{ext}

Translation rules:

Mirror the specification exactly — same formula, same controls, same sample restrictions
Use equivalent packages (see shared/multi-language-conventions.md for mappings)
Match output format — both scripts should produce a CSV with columns: estimate, se, pvalue, ci_lower, ci_upper, n, model_label
Document every adaptation — comment blocks explaining where the translation required judgment calls
Use the same data file — both scripts read from the same cleaned dataset

Show full SKILL.md (346 more words)Show less

Phase 4: Run Both & Compare

Run the source script, capture output CSV
Run the replication script, capture output CSV
Comparison thresholds:

Metric	Threshold	Verdict
Point estimates	Differ by < 0.1%	PASS
Point estimates	Differ by 0.1–1%	WARN — likely rounding or optimizer differences
Point estimates	Differ by > 1%	FAIL — investigate
Standard errors	Differ by < 1%	PASS
Standard errors	Differ by 1–5%	WARN — check SE type (robust, clustered, HC1 vs HC3)
Standard errors	Differ by > 5%	FAIL — likely different SE computation
Sample size N	Must be identical	FAIL if different — data filtering diverged

Generate comparison table saved to code/replication/comparison.md:

markdown

| Model | Estimate (source) | Estimate (replica) | Diff (%) | SE (source) | SE (replica) | Diff (%) | N match | Verdict |

Phase 5: Diagnose Discrepancies

If any FAIL or WARN:

Check N first — if sample sizes differ, the data pipeline diverged (most common source of bugs)
Check SE type — HC1 vs HC3 vs clustered vs bootstrap defaults differ across languages
Check optimizer — MLE/GLM may converge to different optima with different starting values
Check missing value handling — NA dropping rules differ (R drops per-variable, Stata drops listwise, Python varies)
Check factor variable encoding — reference category defaults differ across languages

Report the root cause, not just the symptom.

Phase 6: Report

Save to code/replication/cross-language-report.md:

markdown

# Cross-Language Replication Report

**Source:** {source_path} ({source_language})
**Replica:** {replica_path} ({target_language})
**Date:** {date}

## Summary
- Models checked: N
- PASS: N | WARN: N | FAIL: N

## Comparison Table
[from Phase 4]

## Discrepancies
[from Phase 5, if any]

## Verdict
[REPLICATED | REPLICATED WITH NOTES | FAILED — action required]

Common Package Mappings

Task	R	Python	Stata	Julia
OLS + FE	`fixest::feols`	`linearmodels.PanelOLS`	`reghdfe`	`FixedEffectModels.reg`
IV	`fixest::feols` (iv syntax)	`linearmodels.IV2SLS`	`ivregress 2sls`	`FixedEffectModels.reg`
DiD	`did::att_gt`	`differences`	`csdid`	—
Clustered SE	`vcov = ~cluster`	`cov_type='clustered'`	`vce(cluster var)`	`Vcov.cluster(:var)`
Logit/Probit	`glm(family=binomial)`	`statsmodels.Logit`	`logit`	`GLM.jl`

Log to REVIEW-STATE.md (final step)

Write the comparison report to reviews/<scope>/cross-language-check/<YYYY-MM-DD-HHMM>.md (where <scope> is the paper slug for paper-level checks or _project for project-level checks; mkdir -p reviews/<scope>/cross-language-check/ first). Then append a row to the project's REVIEW-STATE.md:

bash

bash <skills-root>/_shared/review-state-log.sh \
  --check cross-language-check \
  --paper "<paper-{venue} dir, or — for project-level cross-language checks>" \
  --verdict "<MATCH|DIVERGENCE>" \
  --score "<pass-count>/<total-comparisons>" \
  --open-issues "<fail-count>/<total-comparisons>" \
  --report "reviews/<scope>/cross-language-check/<YYYY-MM-DD-HHMM>.md" \
  --notes "<one-line: e.g. 'all match within tol'; or 'IV SE differs in §4'>" \
  [--trigger "pre-submission-report|review-cluster"]

Verdict: MATCH if every comparison passes (within tolerance); DIVERGENCE if any FAIL.
Score: PASS count / total comparisons.
Open issues: FAIL count / total at run time.
Trigger: pass orchestrator name only if invoked as a sub-agent. Otherwise omit.

Schema: the installed shared resource shared/review-state-schema.md.

Cross-References

Resource	When read
`shared/multi-language-conventions.md`	Phase 3 (language-specific style)
`multi-perspective/references/computational-many-analysts.md`	Context (verification hierarchy)
the `code-review` agent	Phase 6 (optionally review both scripts)
`replication-package` skill	After (include both scripts in replication materials)

Files

Just SKILL.md in skills/cross-language-check of flonat/flonat-research.

Open the folder on GitHubat commit da27600

Compare with similar skills

Cross Language Check next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Cross Language Check compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Cross Language Check this skillflonat/flonat-research	146	—	~2.1k	Automated safety check: Pass	MIT	10 days ago
Stata C Pluginsdylantmoore/stata-skill	291	1 repos	~5.8k	Automated safety check: Pass	Custom licence	6 mo ago
Capture Environmentpedrohcgs/claude-code-my-workflow	1.7k	—	~2.8k	Automated safety check: Notes	MIT	11 days ago
Fin Data Acquisitioncsmar432/finai-research	109	—	~2k	Automated safety check: Pass	MIT	3 days ago
Empirical Research MethodsCitrus-bit/Anaxa	120	—	~1.9k	Automated safety check: Pass	CC-BY-SA-4.0	1 mo ago
Diagnosepedrohcgs/claude-code-my-workflow	1.7k	—	~4.2k	Automated safety check: Pass	MIT	11 days ago

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Works with

Python

Questions about Cross Language Check

What does Cross Language Check do?

Replicate a quantitative analysis in a second language (R↔Python↔Stata↔Julia) and compare outputs for implementation errors. Cross Language Check is an agent skill from flonat/flonat-research. Replicate a quantitative analysis in a second language (R↔Python↔Stata↔Julia) and compare outputs for implementation errors.

When should I use Cross Language Check?

Cross Language Check fits situations like: an existing empirical result needs independent cross-language verification; tasks that involve Econometrics and empirical research.

How do I install Cross Language Check in Claude Code?

Run `npx skills add flonat/flonat-research --skill cross-language-check -a claude-code`. Or copy the skill folder (skills/cross-language-check in flonat/flonat-research) into .claude/skills/cross-language-check in your project. Claude Code loads it when a task matches its description.

How do I install Cross Language Check in Codex?

Run `npx skills add flonat/flonat-research --skill cross-language-check -a codex`. Or copy the skill folder (skills/cross-language-check in flonat/flonat-research) into .agents/skills/cross-language-check in your project. Codex loads it when a task matches its description.

Can I use Cross Language Check in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add flonat/flonat-research --skill cross-language-check -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/cross-language-check, .gemini/skills/cross-language-check, .github/skills/cross-language-check and .opencode/skills/cross-language-check in your project.

What does Cross Language Check need to run?

Going by SKILL.md and its folder, Cross Language Check needs the command-line tools its instructions call (bash). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Bash(uv*, Rscript*, stata*, julia*, diff*, mkdir*, ls*, cp*), Read, Write, Edit, Glob, Grep, Agent.

Does Cross Language Check access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Cross Language Check safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Cross Language Check use?

Cross Language Check is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Cross Language Check use?

About 2.1k tokens (SKILL.md is roughly 8.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Cross Language Check?

Skills that share tags, products or a category with Cross Language Check: Stata C Plugins (dylantmoore/stata-skill, 291 stars), Capture Environment (pedrohcgs/claude-code-my-workflow, 1.7k stars), Fin Data Acquisition (csmar432/finai-research, 109 stars) and Empirical Research Methods (Citrus-bit/Anaxa, 120 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Cross Language Check?

flonat (a GitHub user) maintains it in flonat/flonat-research, which has 146 GitHub stars. The repository holds 83 skills in this directory. The repository was last updated on September 29, 2026.

Source: flonat/flonat-research on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Cross Language Check

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Cross-Language Replication Check

Output Path

When to Use

When NOT to Use

Workflow

Phase 1: Parse Source Script

Phase 2: Choose Target Language

Phase 3: Translate

Phase 4: Run Both & Compare

Phase 5: Diagnose Discrepancies

Phase 6: Report

Common Package Mappings

Log to REVIEW-STATE.md (final step)

Cross-References

Files

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