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 skills add flonat/flonat-research --skill cross-language-checkgit clone --depth 1 https://github.com/flonat/flonat-researchWrote 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/flonat/flonat-research/cross-language-check)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00029 | $0.01033 |
| Sonnet 5 | $0.00012 | $0.00413 |
| Haiku 4.5 | $0.00006 | $0.00206 |
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 6d 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 — 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>.mdinside the project, where<scope>is the paper slug (e.g.,paper-jtp) for paper-level checks or_projectfor 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.mdexists, write a one-line entry under "Latest per source" pointing at the new file. Otherwisereview-recapwill 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)
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.
- 6d ago First seen · 180 lines · 58 tokens per session scan A 3656e2901a37
cross-language-check is a skill published in the GitHub repository flonat/flonat-research (132 stars, last pushed 15d ago), licensed MIT. It adds 58 tokens to every session and 2,065 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
alterlab-anndata
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the…
alterlab-rdkit
Provides the RDKit cheminformatics toolkit for low-level, fine-grained molecular primitives — SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure/SMARTS search, 2D/3D coordinate generation, similarity, and reaction handling. Use when custom sanitization, specialized fingerprint or descriptor…
alterlab-sympy
Symbolic mathematics in Python with SymPy — solve equations algebraically, perform calculus (derivatives, integrals, limits), manipulate algebraic expressions, work with symbolic matrices, and generate executable code from formulas. Use when exact symbolic results are needed rather than numerical approximations, or…
alterlab-cirq
Builds, simulates, and runs quantum circuits with Cirq, Google Quantum AI's framework for NISQ hardware, noise-aware low-level circuit design, and noise characterization. Use when targeting Google Quantum AI processors (Sycamore/Weber), designing noise-aware NISQ circuits, or running characterization experiments…
alterlab-pydeseq2
Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw…
alterlab-pymoo
Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal trade-offs, or tackling engineering design problems with competing…