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.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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还没有评价。欢迎分享你的使用感受。
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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.
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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.
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Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
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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.
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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.
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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.
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