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 skills add Rheinmir/setup --skill dym-harness-sdk-evalsgit clone --depth 1 https://github.com/Rheinmir/setupWrote 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/rheinmir/setup/dym-harness-sdk-evals)<a href="https://agentmods.dev/skills/rheinmir/setup/dym-harness-sdk-evals"><img src="https://agentmods.dev/badge/skills/rheinmir/setup/dym-harness-sdk-evals/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rheinmir/setup/dym-harness-sdk-evals"><img src="https://agentmods.dev/badge/skills/rheinmir/setup/dym-harness-sdk-evals.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00661 |
| Opus 5.5 | $0.00027 | $0.00264 |
| Sonnet 5.5 | $0.00014 | $0.00132 |
| Haiku 4.5 | $0.00007 | $0.00066 |
Grade A, and why
dym-harness-sdk-evals 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 yesterday.
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.
What it actually says
Skill: dym-harness-sdk-evals — chấm điểm agent (chưa kiểm chứng)
When to use
- Cần điểm số chất lượng (không chỉ pass/fail hiệu ứng) cho agent Strands, hoặc chấm trace production đã có.
- Trạng thái: mọi thứ dưới đây lấy từ docs
site/src/content/docs/.../evals-sdk/trong harness-sdk; code nằm ở repostrands-agents/evalschưa clone, chưa chạy. Coi là giả thuyết.
Steps
- Mô hình: Experiment → Case → Task → Evaluator → Detector (
how-evaluation-works.mdx:17-48).- Evaluator chấm ở 3 mức: OUTPUT (câu trả lời) · TRACE (chuỗi span/tool) · SESSION (cả phiên).
- Evaluator có loại LLM-judge và loại tất định (
evaluators/deterministic_evaluators.mdx). - Detector đọc trace của run FAIL để tìm nguyên nhân (tách "có đạt không" khỏi "vì sao").
- Simulator đóng vai user/tool cho test nhiều lượt.
- Chấm trace production không cần chạy lại agent: trace provider lấy span OTel từ CloudWatch/Langfuse (
how-to/trace_providers.mdx). Harness phát span khiOTEL_TRACES_EXPORTERđược đặt. - Gate CI: CLI
run ... --fail-on threshold:0.8(cli/run.mdx:104,110); kết quả task cache theocase.name(how-to/result_caching.mdx:44) — đổi input mà giữ tên case thì dính cache cũ. - Đa phương thức (design
0010): MLLM-as-judge, Likert-5 cho chất lượng tổng + nhị phân cho correctness/faithfulness.
Rules
- Chưa kiểm chứng — trước khi dùng: clone
strands-agents/evals, đối chiếu--helpthật, sửa file này. - Harness-sdk KHÔNG dùng evals SDK trong CI của chính nó (grep
strands_evalstrong harness-py/ts/.github = rỗng) → đổi default harness không có regression eval tự động. - Con số "benchmarked defaults" (trung bình 6 benchmark: alfworld, ContextBench, GAIA, WebShop, τ³-bench, Terminal-Bench 2.1) chép tay trong
site/public/embeds/strands-harness-benchmarks.html; file số liệu gốc và harness chạy benchmark (strands-labs/benchmark-harnesses, Harbor trên EC2) KHÔNG có trong repo → không tái lập được.
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.
- yesterday First seen · 27 lines · 68 tokens per session scan A 83b392a4594c
dym-harness-sdk-evals is a skill published in the GitHub repository Rheinmir/setup (11 stars, last pushed 20d ago), licensed MIT. It adds 68 tokens to every session and 661 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-11-07.
dym-harness-sdk-evals compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dym-harness-sdk-evalsRheinmir/setup | Skill | 11 | 20d ago | MIT | A | 68 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | yesterday | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 100k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| browser-automationopenclaw/openclaw | Skill | 394k | today | MIT | A | 36 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 4d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |