Skills senior-computer-vision

📦

senior-computer-vision

Name: senior-computer-vision
Author: davila7

Content revision r2 Safe ⚙️ External commands

Design Production Computer Vision Systems

Computer vision projects often fail when model choices ignore data quality, latency, deployment, or monitoring constraints. This skill structures architecture, optimization, and production planning around those constraints.

Supports: Claude Codex Code(CC)

🥈 81 Silver

Install with my Agent

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Agent request

Review the Skillstore skill "senior-computer-vision" from https://skillstore.io/skills/davila7-senior-computer-vision.md and its manifest at https://skillstore.io/api/skills/davila7-senior-computer-vision/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

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Markdown detail GET /skills/davila7-senior-computer-vision.md Signed manifest GET /api/skills/davila7-senior-computer-vision/manifest Signed lockfile GET /api/skills/davila7-senior-computer-vision/lockfile

Test it

Using "senior-computer-vision". Recommend a baseline for detecting small defects on a factory line with limited labeled data.

Expected outcome:

Start with a pretrained one-stage detector and preserve higher input resolution for small defects.
Use stratified splits by production batch to prevent leakage.
Track class recall, false positives per image, and end-to-end latency.
Compare targeted augmentation and active learning before increasing model size.

Using "senior-computer-vision". Reduce inference latency from 180 milliseconds to less than 100 milliseconds.

Expected outcome:

Profile preprocessing, model execution, transfer time, and postprocessing separately.
Test mixed precision, fixed input shapes, and larger batches under realistic concurrency.
Validate every optimization against accuracy and tail-latency thresholds.
Record hardware, runtime, model version, and dataset for reproducible comparisons.

Using "senior-computer-vision". Review a planned production vision service handling personal images.

Expected outcome:

The review prioritizes data minimization, encryption, access control, retention limits, audit logging, model monitoring, capacity tests, and a documented incident process.

Security Audit

Safe

v9 • 7/23/2026 Open versioned report

All 13 static findings are false positives caused by prose phrases, Markdown code fences, inline paths, and illustrative shell commands. No network reconnaissance, shell backtick execution, prompt injection, secret access, or data exfiltration behavior was found. Deployment examples can change systems when run, so users should verify targets and credentials.

Files scanned

773

Lines analyzed

Review items

False positives ignored

Risk Factors

⚙️ External commands (10)

SKILL.md:14-23 SKILL.md:23-54 SKILL.md:54-65 SKILL.md:65-75 SKILL.md:75-167 SKILL.md:167-185 SKILL.md:185-189 SKILL.md:189-190 SKILL.md:190-191 SKILL.md:191-192

No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.

Audited by: codex View Audit History →

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Academic citations (APA · BibTeX · CFF)

APA citation

davila7. (2026). senior-computer-vision security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-senior-computer-vision/audits/9

BibTeX citation

@techreport{davila7-davila7-senior-computer-vision-2026,
  author = {davila7},
  title = {senior-computer-vision security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/davila7-senior-computer-vision/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "senior-computer-vision security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-senior-computer-vision/audits/9"
identifiers:
  - type: other
    value: "skillstore:davila7-senior-computer-vision:audit:9"
    description: "Skillstore immutable audit report identifier"

Compare variants

2 installable variants

Each author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.

Why this variant is first

Higher Skillstore usage

Author / source Skillstore Score Evidence Confidence Skillstore usage Updated

alirezarezvani Recommended

alirezarezvani-senior-computer-vision

Skillstore Score 81

Evidence Confidence High

Skillstore usage 25

Updated

2026-08-21

davila7 Current

davila7-senior-computer-vision

Skillstore Score 81

Evidence Confidence High

Skillstore usage 9

Updated

2026-08-21

Skillstore Score

Why this score Evidence Confidence: High

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Select a Vision Baseline

Compare model families, dataset needs, evaluation metrics, and deployment constraints before starting experiments.

Improve Detection Performance

Create a prioritized plan for improving detector accuracy, latency, throughput, and reliability.

Review Production Readiness

Assess serving, monitoring, privacy, scaling, and incident-response requirements for a vision service.

Try These Prompts

Choose a Baseline

I need to detect [objects] in [image or video source]. Recommend a baseline architecture, dataset plan, metrics, and first experiment.

Diagnose a Detector

Review this object detection setup: [details]. Identify likely accuracy and latency limits. Propose prioritized experiments with measurable success criteria.

Plan Real-Time Serving

Design an inference service for [model] with [traffic and latency targets]. Cover batching, caching, scaling, observability, fallback behavior, and cost tradeoffs.

Conduct an Architecture Review

Review this vision platform architecture: [details]. Evaluate data flow, model lifecycle, privacy, reliability, capacity, and incident risks. Rank recommendations by impact.

Best Practices

Define accuracy, latency, throughput, reliability, privacy, and cost targets before selecting a model.
Profile the complete pipeline on representative data and deployment hardware.
Monitor input quality, prediction quality, drift, resource use, and service health after release.

Avoid

Do not optimize model execution while ignoring preprocessing, transfer, and postprocessing costs.
Do not accept generic performance targets without workload-specific benchmarks.
Do not deploy a vision model without data governance, monitoring, rollback, and incident procedures.

Frequently Asked Questions

Does this skill train a model automatically?

No. It provides planning guidance, while the included Python trainer is only a scaffold.

Which computer vision frameworks does it cover?

The documentation references PyTorch, TensorFlow, OpenCV-related workflows, YOLO, SAM, diffusion models, and vision transformers at a general level.

Can it optimize an existing inference service?

It can structure profiling and optimization recommendations. You must provide measurements, architecture details, hardware, and quality constraints.

Does it support real-time video systems?

Yes, as an architecture and operations guide covering latency, batching, caching, scaling, and monitoring.

Can it guarantee the listed performance targets?

No. The targets are examples and must be tested against your data, traffic, model, runtime, and hardware.

Does it address privacy and security?

It highlights encryption, access control, anonymization, audit logging, and compliance considerations. A dedicated review is still required for production.