Code quality patterns and guidelines for ToolUniverse tool development. Apply when writing, fixing, or refactoring tool Python code in the ToolUniverse project. Encodes lessons from 80+ debug rounds. Use alongside devtu-fix-tool and devtu-self-evolve. Triggers: implementing tool fixes, writing new tool classes, reviewing tool code quality, checking schema correctness, looking up API-specific bug fixes.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Always run Skill(skill="simplify") after writing or modifying code.
return_schema has oneOf: [{data+metadata}, {error}]try: has except: at exact same indentation levelpython3 -c "import json; json.load(open('f.json'))")_lazy_registry_static.py and default_config.pyruff check src/tooluniverse/<file>.py passespython -c "from tooluniverse.<module> import <Class>" passespython -m tooluniverse.cli run <Tool> '<real_args_json>' returns expected dataSkill(skill="simplify") on all modified files| Category | Signal | Reference |
|---|---|---|
| Silent param ignored | API accepts but drops filter | code-patterns.md — Client-Side Filter |
| Wrong API field/endpoint | 0 results or 404 | api-fixes.md — Quick Lookup Table |
| Schema invalid | null type, missing oneOf | code-patterns.md — Schema Patterns |
| Undisclosed normalization | Auto-transform hidden from user | code-patterns.md — Normalization Disclosure |
| try/except indent | SyntaxError at runtime | code-patterns.md — try/except section |
| Truncation buried | Data count hidden in notes | code-patterns.md — Truncation |
| Hosted model API (NIM) | async 404 on poll, JSON-wrapped output, 200+inner-failure, "not found for account" | code-patterns.md — Hosted Model-API Tools |
| R subprocess tool | '\.' unrecognized escape from Rscript -e | code-patterns.md — R-subprocess Tools |
git fetch origin && git stash && git rebase origin/main && git stash pop
git push --force-with-lease origin fix/round-XX-bugs
gh pr view <N> --json mergeable # must be MERGEABLE before done
main directlymims-harvard/ToolUniverse — verify with git remote -vまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Simplest distributed training API. 4 lines to add distributed support to any PyTorch script. Unified API for DeepSpeed/FSDP/Megatron/DDP. Automatic device placement, mixed precision (FP16/BF16/FP8). Interactive config, single launch command. HuggingFace ecosystem standard.
日本語の概要は準備中です。原文の説明を表示しています。
ADMET property prediction for drug candidates. Full pharmacokinetic panel (Caco-2, PPB, clearance, CYP), toxicity (hERG, AMES, DILI), drug-likeness (Lipinski, QED), using RDKit descriptors and TDC models.
日本語の概要は準備中です。原文の説明を表示しています。
Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
日本語の概要は準備中です。原文の説明を表示しています。
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
日本語の概要は準備中です。原文の説明を表示しています。
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
日本語の概要は準備中です。原文の説明を表示しています。