synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 agentmods add skills/synthetic-sciences/openscience/bigcode-evaluation-harnessnpx skills add synthetic-sciences/openscience --skill bigcode-evaluation-harnessgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/bigcode-evaluation-harness)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/bigcode-evaluation-harness"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/bigcode-evaluation-harness.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00068 | $0.03249 |
| Opus 5 | $0.00034 | $0.01625 |
| Sonnet 5 | $0.00014 | $0.00650 |
| Haiku 4.5 | $0.00007 | $0.00325 |
Grade A, and why
evaluating-code-models 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 2d 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.
This is a copy
94% identical to evaluating-code-models — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 407 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BigCode Evaluation Harness - Code Model Benchmarking
Quick Start
BigCode Evaluation Harness evaluates code generation models across 15+ benchmarks including HumanEval, MBPP, and MultiPL-E (18 languages).
Installation:
git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
cd bigcode-evaluation-harness
pip install -e .
accelerate config
Evaluate on HumanEval:
accelerate launch main.py \
--model bigcode/starcoder2-7b \
--tasks humaneval \
--max_length_generation 512 \
--temperature 0.2 \
--n_samples 20 \
--batch_size 10 \
--allow_code_execution \
--save_generations
View available tasks:
python -c "from bigcode_eval.tasks import ALL_TASKS; print(ALL_TASKS)"
Common Workflows
Workflow 1: Standard Code Benchmark Evaluation
Evaluate model on core code benchmarks (HumanEval, MBPP, HumanEval+).
Checklist:
Code Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model and generation
- [ ] Step 3: Run evaluation with code execution
- [ ] Step 4: Analyze pass@k results
Step 1: Choose benchmark suite
Python code generation (most common):
- HumanEval: 164 handwritten problems, function completion
- HumanEval+: Same 164 problems with 80× more tests (stricter)
- MBPP: 500 crowd-sourced problems, entry-level difficulty
- MBPP+: 399 curated problems with 35× more tests
Multi-language (18 languages):
- MultiPL-E: HumanEval/MBPP translated to C++, Java, JavaScript, Go, Rust, etc.
Advanced:
- APPS: 10,000 problems (introductory/interview/competition)
- DS-1000: 1,000 data science problems across 7 libraries
Step 2: Configure model and generation
# Standard HuggingFace model
accelerate launch main.py \
--model bigcode/starcoder2-7b \
--tasks humaneval \
--max_length_generation 512 \
--temperature 0.2 \
--do_sample True \
--n_samples 200 \
--batch_size 50 \
--allow_code_execution
# Quantized model (4-bit)
accelerate launch main.py \
--model codellama/CodeLlama-34b-hf \
--tasks humaneval \
--load_in_4bit \
--max_length_generation 512 \
--allow_code_execution
# Custom/private model
accelerate launch main.py \
--model /path/to/my-code-model \
--tasks humaneval \
--trust_remote_code \
--use_auth_token \
--allow_code_execution
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 407 lines · 68 tokens per session scan A bb0d756dabdc
evaluating-code-models is a skill published in the GitHub repository synthetic-sciences/openscience (3,493 stars, last pushed today), licensed Apache-2.0. It adds 68 tokens to every session and 3,249 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to evaluating-code-models, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
evaluating-code-models
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.