N
natea/harness-eval
Eval framework that ranks agentic coding frameworks (Superpowers, Compound Engineering, Agent Skills, GSD) by…
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
- ★ 数取值
- 15 ★ · natea/harness-eval · 取自 GitHub 搜索 API · (2026-11-13 20:00:50 核对)
- 来源明细
- 从该仓库的 GitHub 页面直接读取 · 条目类型:开源项目
- 收录与活跃
- 收录于 2026-11-02 · 最近见到 2026-11-14 · 共见到 13 次 · 被 1 个来源列出
- 评分构成
- 综合分 103 · 15 ★ · 1 个来源 · repo
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我们把它归在这些标签下: 开源项目。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库
natea/harness-eval;我们收录于 2026-11-02;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。# harness-eval
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
An evaluation framework that definitively ranks agentic coding frameworks by
having each one build the same product from the same spec, with the same
harness and model, in isolated sandboxes — then grading every artifact on a
weighted, evidence-based rubric.
First question answered: which of Superpowers, Compound Engineering,
Agent Skills, and GSD builds the best implementation of the
Symphony Service Specification (an
issue-tracker-driven orchestration daemon) when driven by Claude Code +
Opus 4.6? The architecture deliberately holds every other variable fixed so
the framework is the only thing being measured — and swaps cleanly for phase
2 (other harnesses like OpenCode/Codex, other models like GLM via z.ai).
## Status
Built (merged or in review) — each capability was specified under
openspec/changes/
before implementation:
Core harness — candidate registry, orchestration with isolation +
budgets + provenance, headless build sessions, two-instrument grading
(evidence-based evaluator + blind code-quality judge), telemetry, and
results.json + scorecard.md. (setup-harness-eval-framework)
Results dashboard — cross-run leaderboard, per-run scorecards, trial
drill-downs with evidence, and live re-weighting. (add-results-dashboard)
PRD library + bring-your-own-PRD — a target abstraction spanning several
product shapes (symphony-daemon, cli-tool, plus rest-api and web-app
adapted from ViBench), an init --target … --spec scaffolder, and
freeze/attribution rules. (add-prd-library)
Pluggable models — a model-profile registry, worker/judge resolution,
GLM via z.ai validated end-to-end, a model probe connectivity check,
and cross-vendor-judge + cost-basis caveats in results/scorecards.
(add-pluggable-models)
Pluggable providers/harnesses — Docker, E2B, macOS Virtualization, and
worktree providers share the same sandbox contract; harness registry entries
are validated per implemented driver. See
docs/HARNESS-ONBOARDING.md.
In review on branches: the Eval Studio web UI (review + configure on
shadcn/ui — this branch). Not yet built: a language-effectiveness
evaluation mode. See
ROADMAP.md.
## How it works
registry (pinned frameworks) ──┐
eval target (PRD + test plan) ─┼─▶ orchestrator ─▶ per-trial sandbox ─▶ headless build session(s)
run config (budgets, weights) ─┘ │ │
▼ ▼
provenance + telemetry archived workspace + transcripts
│ │
└────────────▶ grading ◀────────────┘
evaluator (test plan, evidence)
blind judge (code quality)
results.json ─▶ scorecard.md + web dashboard
1. Candidate registry (config/registry.yaml) — each framework under
test is declared with version-pinned install commands, its prescribed
session script (e.g. GSD's /gsd-new-project phases vs Superpowers' single
prompt), and a content-free continuation allowlist. Fairness rules are
enforced: every candidate gets the identical rendered base prompt; only
framework-prescribed command wrappers differ.
2. Eval targets (targets/ /) — a target couples a PRD, a frozen
weighted test plan derived from it (content-hashed at run start; fatal
cold-start gates; optional bonus steps), and any fixtures the evaluator
needs (e.g. a mock Linear GraphQL API and a stub Codex app-server for the
having each one build the same product from the same spec, with the same
harness and model, in isolated sandboxes — then grading every artifact on a
weighted, evidence-based rubric.
First question answered: which of Superpowers, Compound Engineering,
Agent Skills, and GSD builds the best implementation of the
Symphony Service Specification (an
issue-tracker-driven orchestration daemon) when driven by Claude Code +
Opus 4.6? The architecture deliberately holds every other variable fixed so
the framework is the only thing being measured — and swaps cleanly for phase
2 (other harnesses like OpenCode/Codex, other models like GLM via z.ai).
## Status
Built (merged or in review) — each capability was specified under
openspec/changes/
before implementation:
Core harness — candidate registry, orchestration with isolation +
budgets + provenance, headless build sessions, two-instrument grading
(evidence-based evaluator + blind code-quality judge), telemetry, and
results.json + scorecard.md. (setup-harness-eval-framework)
Results dashboard — cross-run leaderboard, per-run scorecards, trial
drill-downs with evidence, and live re-weighting. (add-results-dashboard)
PRD library + bring-your-own-PRD — a target abstraction spanning several
product shapes (symphony-daemon, cli-tool, plus rest-api and web-app
adapted from ViBench), an init --target … --spec scaffolder, and
freeze/attribution rules. (add-prd-library)
Pluggable models — a model-profile registry, worker/judge resolution,
GLM via z.ai validated end-to-end, a model probe connectivity check,
and cross-vendor-judge + cost-basis caveats in results/scorecards.
(add-pluggable-models)
Pluggable providers/harnesses — Docker, E2B, macOS Virtualization, and
worktree providers share the same sandbox contract; harness registry entries
are validated per implemented driver. See
docs/HARNESS-ONBOARDING.md.
In review on branches: the Eval Studio web UI (review + configure on
shadcn/ui — this branch). Not yet built: a language-effectiveness
evaluation mode. See
ROADMAP.md.
## How it works
registry (pinned frameworks) ──┐
eval target (PRD + test plan) ─┼─▶ orchestrator ─▶ per-trial sandbox ─▶ headless build session(s)
run config (budgets, weights) ─┘ │ │
▼ ▼
provenance + telemetry archived workspace + transcripts
│ │
└────────────▶ grading ◀────────────┘
evaluator (test plan, evidence)
blind judge (code quality)
results.json ─▶ scorecard.md + web dashboard
1. Candidate registry (config/registry.yaml) — each framework under
test is declared with version-pinned install commands, its prescribed
session script (e.g. GSD's /gsd-new-project phases vs Superpowers' single
prompt), and a content-free continuation allowlist. Fairness rules are
enforced: every candidate gets the identical rendered base prompt; only
framework-prescribed command wrappers differ.
2. Eval targets (targets/ /) — a target couples a PRD, a frozen
weighted test plan derived from it (content-hashed at run start; fatal
cold-start gates; optional bonus steps), and any fixtures the evaluator
needs (e.g. a mock Linear GraphQL API and a stub Codex app-server for the
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想把收录标记放进 README 就复制这行,可选,不影响收录。
[](https://anclum.com/zh/s/natea-harness-eval/)
- 来源
- github · https://github.com/natea/harness-eval
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