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output-validation

by wu-uk · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install dynamic-object-aware-egomotion-output-validation
Description
Local self-check of instructions and mask outputs (format/range/consistency) without using GT.
README (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.
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install dynamic-object-aware-egomotion-output-validation
  3. After installation, invoke the skill by name or use /dynamic-object-aware-egomotion-output-validation
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v0.1.0
Bulk publish from all-task-skills-dedup
Metadata
Slug dynamic-object-aware-egomotion-output-validation
Version 0.1.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is output-validation?

Local self-check of instructions and mask outputs (format/range/consistency) without using GT. It is an AI Agent Skill for Claude Code / OpenClaw, with 74 downloads so far.

How do I install output-validation?

Run "/install dynamic-object-aware-egomotion-output-validation" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is output-validation free?

Yes, output-validation is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does output-validation support?

output-validation is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created output-validation?

It is built and maintained by wu-uk (@wu-uk); the current version is v0.1.0.

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