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/liortesta/clawdagent/bigcode-evaluation-harnessnpx skills add liortesta/ClawdAgent --skill bigcode-evaluation-harnessgit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote 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/liortesta/clawdagent/bigcode-evaluation-harness)<a href="https://agentmods.dev/skills/liortesta/clawdagent/bigcode-evaluation-harness"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/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 | $0.03244 |
| Opus 5 | $0.00034 | $0.01622 |
| Sonnet 5 | $0.00014 | $0.00649 |
| Haiku 4.5 | $0.00007 | $0.00324 |
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
100% identical to evaluating-code-models — 0 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 — 406 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 · 406 lines · 68 tokens per session scan A 7c46e8eb3ca8
evaluating-code-models is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 9d ago), licensed Apache-2.0. It adds 68 tokens to every session and 3,244 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to evaluating-code-models, differing in 0 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.