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output-validation

作者 wu-uk · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install dynamic-object-aware-egomotion-output-validation
功能描述
Local self-check of instructions and mask outputs (format/range/consistency) without using GT.
使用说明 (SKILL.md)

When to use

  • After generating your outputs (interval instructions, masks, etc.), before submission/hand-off.

Checks

  • Key format: every key is "{start}->{end}", integers only, start\x3C=end.
  • Coverage: max frame index ≤ video total-1; consistent with your sampling policy.
  • Frame count: NPZ f_{i}_* count equals sampled frame count; no gaps or missing components.
  • CSR integrity: each frame has data/indices/indptr; len(indptr)==H+1; indptr[-1]==indices.size; indices within [0,W).
  • Value validity: JSON values are non-empty string lists; labels in the allowed set.

Reference snippet

import json, numpy as np, cv2
VIDEO_PATH = "\x3Cpath/to/video>"
INSTRUCTIONS_PATH = "\x3Cpath/to/interval_instructions.json>"
MASKS_PATH = "\x3Cpath/to/masks.npz>"
cap=cv2.VideoCapture(VIDEO_PATH)
n=int(cap.get(cv2.CAP_PROP_FRAME_COUNT)); H=int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)); W=int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
j=json.load(open(INSTRUCTIONS_PATH))
npz=np.load(MASKS_PATH)
for k,v in j.items():
    s,e=k.split("->"); assert s.isdigit() and e.isdigit()
    s=int(s); e=int(e); assert 0\x3C=s\x3C=e\x3Cn
    for lbl in v: assert isinstance(lbl,str)
frames=0
while f"f_{frames}_data" in npz: frames+=1
assert frames>0
assert npz["shape"][0]==H and npz["shape"][1]==W
indptr=npz["f_0_indptr"]; indices=npz["f_0_indices"]
assert indptr.shape[0]==H+1 and indptr[-1]==indices.size
assert indices.size==0 or (indices.min()>=0 and indices.max()\x3CW)

Self-check list

  • JSON keys/values pass format checks.
  • Max frame index within video range and near sampled max.
  • NPZ frame count matches sampling; keys consecutive.
  • CSR structure and shape validated.
安全使用建议
This skill is an instruction-only local validator for interval/mask outputs and appears coherent with that purpose. Before installing/using: (1) ensure you run it in an environment with Python, numpy, and OpenCV (cv2) available, since the snippet assumes those libraries; (2) provide only trusted local VIDEO_PATH / INSTRUCTIONS_PATH / MASKS_PATH files (the checks read those files but do not contact external services); (3) be prepared for the snippet to raise assertions on invalid data — consider running it in an isolated environment or CI step; and (4) if you need automated installation, request or add an explicit install spec declaring required packages to avoid surprises.
功能分析
Type: OpenClaw Skill Name: dynamic-object-aware-egomotion-output-validation Version: 0.1.0 The skill bundle provides instructions and a reference Python snippet for validating the integrity and format of video processing outputs (JSON and NPZ files). The logic is focused on local data consistency checks, such as verifying frame ranges and CSR matrix structure using standard libraries like OpenCV and NumPy, with no indicators of malicious intent, data exfiltration, or unauthorized execution.
能力评估
Purpose & Capability
Name and description describe local output validation; SKILL.md contains focused checks (key format, coverage, NPZ/CSR structure, value validity) that directly implement that purpose. Nothing in the instructions attempts to access unrelated services or secrets.
Instruction Scope
Instructions operate on user-supplied local files (VIDEO_PATH, INSTRUCTIONS_PATH, MASKS_PATH) and perform file- and content-level assertions. They do not ask the agent to read unrelated system files, environment variables, or send data externally. The scope stays within validating generated outputs.
Install Mechanism
This is instruction-only (no install spec), which is low-risk. The reference snippet uses Python libraries (numpy, cv2) but the skill does not declare runtime dependencies or required binaries; users should ensure Python + numpy + OpenCV are available in their environment.
Credentials
No environment variables, credentials, or config paths are requested. The skill's data access is limited to the file paths the user supplies, which is proportionate to its validation purpose.
Persistence & Privilege
The skill is not always-on and does not request persistent or elevated privileges. It does not modify other skills or system-wide settings.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install dynamic-object-aware-egomotion-output-validation
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /dynamic-object-aware-egomotion-output-validation 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Bulk publish from all-task-skills-dedup
元数据
Slug dynamic-object-aware-egomotion-output-validation
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

output-validation 是什么?

Local self-check of instructions and mask outputs (format/range/consistency) without using GT. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 74 次。

如何安装 output-validation?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install dynamic-object-aware-egomotion-output-validation」即可一键安装,无需额外配置。

output-validation 是免费的吗?

是的,output-validation 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

output-validation 支持哪些平台?

output-validation 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 output-validation?

由 wu-uk(@wu-uk)开发并维护,当前版本 v0.1.0。

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