rdk-model-zoo-validate
Skill404 starsChanged 30 days agoApache-2.0View on GitHub
Use when planning or executing sample-scoped RDK Model Zoo smoke, numerical, accuracy, performance, or regression checks and preparing verification evidence. 触发词：样例验收、回归测试、数值一致性。Do not use as a PR verdict, a published-benchmark lookup, or a quantization implementation.
1 other project uses a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- D-Robotics/rdk-skillsedited, first seen Oct 9, 2026
Other versions1 project uses a version of this.
Word for word · 0
None found.
Edited · 1
- D-Robotics/rdk-skills★ 598% same
Wrote their own version · 0
None found.
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository6 other files it gives its agents.
- rdk-model-zoo-developSkill
- rdk-model-zoo-integrateSkill
- rdk-model-zoo-releaseSkill
- rdk-model-zoo-repoSkill
- rdk-model-zoo-reviewSkill
- rdk-model-zooSkill
The file
Files
RDK Model Zoo Validation
Purpose
为目标样例提供可复现、分范围的验证计划和证据。组织既有 evaluator 与平台工具，不重复实现通用 Benchmark，也不把生成 JSON 当成真实测试。
When to use
适用：跑 smoke、比较 Python/C++、精度对照、测当前模型性能、验证修复、组织缺板卡情况下的可执行计划。查询已发布数字用 rdk-model-zoo；判断 PR 是否满足规范用 review；量化调优交工具链。
Instructions
- 读取 context-policy.md 和 evidence-contract.md。确定目标提交及 dirty 内容、实际平台/SoC（X5、S100/S100P/S600、X3 或 legacy）、模型/输入/runtime、交付条件。Pack 所在分支（交付默认
main、集成开发develop、历史交付线rdk_x5/rdk_s）不能替代目标 ref；用户约束冲突时保持 not-run 并不切换分支。 - 按 validation-matrix.md 列出检查与 required 属性，先声明阈值和范围。没有板卡、私有数据或可信模型时只阻断相应行；其余 host 检查可继续。验证 README 命令与 API 示例时，先把文档代码块按 说明 / 主机 / 板端 / 转换 分类：主机类在授权内实际执行并绑定代码 SHA、cwd 与制品/输入身份；板端与转换类只形成待执行计划交对应执行者；纯说明文字不冒充已验证命令。结构校验通过不是命令验证。
- 选择目标版本已有脚本/evaluator/构建命令。先读源码和文档，再确定 cwd、argv、输出和副作用；
--help也不能盲目执行。首次范围不自动扩成全仓下载和重测。 - 实际执行前确认权限、环境、空间与输出隔离；有板卡串行占用策略时遵守，不在共享板卡上任意停进程或改频率。PR 不可信代码不得接触生产凭据和长期自托管 runner。
- 保存退出码、日志、模型/输入身份和结果；准确对照判定条件。性能测量记录真实计时口径；工具链 perf/UCP 等按当前实际可用平台工具委托，不能把 X5 工具用于 S 产物。
- 用 verification.template.json 记录结果，schema 位于 verification.schema.json。不执行的行保持 not-run 并给原因，不能填模板性 passed。
- 可用只读校验器检查报告结构及证据哈希：
python3 "$SKILL_ROOT/scripts/validate_evidence.py" "$RECEIPT" --evidence-root "$EVIDENCE_ROOT"RECEIPT是实际报告路径，EVIDENCE_ROOT是其中相对证据路径的根。不提供该根时只做结构检查；退出 0 不是模型通过证明。 - 汇总每行状态、未覆盖范围和必需门禁；将报告交给 review。失败时交 develop 或对应工具链诊断，保留失败输入，不自动降低阈值重判通过。
Output
验证矩阵、精确命令和证据、条件/指标、通过与失败、未运行原因。结构通过、host 通过、单项 board 通过和整体交付完成分别陈述。
Safety
除审阅过且获授权的测试外不修改系统，不伪造数值/哈希/板测。脚本校验不执行 JSON 中的命令、不上传文件。机器人样例只验证离线输出；闭环安全需要额外明确批准的流程。
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.