aider-polyglot-evals
GitHub运行和校准基于Aider Polyglot语料的编码代理评估,支持多模型对比、性能测量及公平基准测试,确保结果可复现。
Trigger Scenarios
Install
npx skills add theStrangeAdventurer/capstan --skill aider-polyglot-evals -g -y
SKILL.md
Frontmatter
{
"name": "aider-polyglot-evals",
"description": "Run, calibrate, compare, or inspect reproducible coding-agent evaluations using the open-source Aider Polyglot corpus. Use for a 12-task Capstan or OpenCode comparison, per-task upstream-test results, agent CPU\/RSS measurement, or a fair model\/agent benchmark without task-specific instructions."
}
Aider Polyglot evaluations
Use benchmarks/polyglot/scripts/run_eval.py as the canonical harness. Keep
the corpus commit, mini-v2 task list, public prompt construction, timeouts,
and upstream scorer unchanged while comparing configurations.
Prepare
-
From the repository root, run:
./benchmarks/polyglot/bootstrap.sh ./build.shBootstrap clones
Aider-AI/polyglot-benchmarkintobenchmarks/work/polyglot-benchmark, checks all required language toolchains, and checks out the pinned corpus revision. -
Before spending model calls, run
git status --shortin both the Capstan repository and corpus. Refuse a dirty corpus. Record a dirty Capstan worktree as non-reproducible exploratory work, not a publishable result. -
Put provider credentials in the environment, never in a committed file.
-
Confirm an agent's authentication with one neutral request before a full suite. Use fresh output directories outside the corpus.
Canonical comparison
For the published-style local comparison, use direct DeepSeek V4 Pro, medium reasoning, 240-second agent timeout, and sequential full suites:
- Capstan:
--provider deepseek --model deepseek-v4-pro - OpenCode:
--model deepseek/deepseek-v4-pro --variant medium
First run the documented Capstan or OpenCode command in
benchmarks/polyglot/README.md with --dry-run. Only remove --dry-run after
checking corpus revision, task matrix, output directory, command, and model.
Run each complete 12-task suite three times in fresh r1, r2, and r3
output directories. Run suites sequentially; do not reuse partial output,
selectively rerun failed tasks, or run comparison configurations concurrently.
Evaluation integrity
- Give agents only the public exercise prompt. Never add solution paths, implementation hints, task-specific instructions, or agent-specific help.
- Do not expose
.metaor.docsto the agent workspace. - Score only upstream tests in the clean scoring copy. Agent exit status or self-reported success is not a pass.
- Keep test timeout and task selection fixed. A changed task list, corpus, timeout, model, or harness is a new benchmark configuration.
- Treat
agent_timeout,agent_error,test_failure, andharness_erroras distinct outcomes. - If the harness is defective, fix it and invalidate affected runs. Never edit CSV or summary results manually.
Results
Report score, grouped failure modes, per-task and aggregate wall time, CPU, peak RSS, corpus commit, clean revisions, agent versions, machine details, provider/model/reasoning settings, and paths to raw output. State that results are workload- and environment-specific; they are not a universal ranking.
Use benchmarks/polyglot/promptfoo/ only when an HTML report is requested.
Promptfoo is an optional orchestration layer; run_eval.py owns task isolation
and scoring. Read its README before use and keep a filled local config only at
its ignored benchmarks/polyglot/promptfoo/config.local.yaml path.
Version History
- 5c90e77 Current 2026-08-27 21:30


