senior-computer-vision

senior-computer-vision is a skill for Codex from vadimcomanescu/codex-skills. It costs 57 tokens per session (246 once invoked), scanned A, original, MIT.

A workflow for designing, evaluating, and deploying computer-vision systems that understand images or video. It covers classification, detection, segmentation, datasets, metrics, inference speed, and deployment.

In plain words
What is it for?
Use it to plan datasets and labels, choose a model type, define evaluation splits and metrics, investigate errors, optimize inference, and prepare deployment.
Why use it?
It helps make vision models measurable and dependable on real data and target hardware.

Skill for Codex

Written for Codex: reads ~/.codex or $CODEX_HOME. Also seen: mentions Codex.

Good fit Use it to plan datasets and labels, choose a model type, define evaluation splits and metrics, investigate errors, optimize inference, and prepare deployment.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/vadimcomanescu/codex-skills/senior-computer-vision
Install

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.

Any agent
npx skills add vadimcomanescu/codex-skills --skill senior-computer-vision
Clone the repo
git clone --depth 1 https://github.com/vadimcomanescu/codex-skills

Made for: Codex.

Wrote 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.

agentmods badge for senior-computer-vision

README.md
[![agentmods](https://agentmods.dev/badge/skills/vadimcomanescu/codex-skills/senior-computer-vision/github.svg)](https://agentmods.dev/skills/vadimcomanescu/codex-skills/senior-computer-vision)
Your own site
<a href="https://agentmods.dev/skills/vadimcomanescu/codex-skills/senior-computer-vision"><img src="https://agentmods.dev/badge/skills/vadimcomanescu/codex-skills/senior-computer-vision/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.

agentmods 80×15 button for senior-computer-vision

Your own site · 80×15
<a href="https://agentmods.dev/skills/vadimcomanescu/codex-skills/senior-computer-vision"><img src="https://agentmods.dev/badge/skills/vadimcomanescu/codex-skills/senior-computer-vision.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 246 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00057 $0.00246
Opus 5 $0.00028 $0.00123
Sonnet 5 $0.00011 $0.00049
Haiku 4.5 $0.00006 $0.00025

Measured 9d ago against content hash 59ef0e9cfcfb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

senior-computer-vision 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 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/dataset_inventory.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/.curated/data/senior-computer-vision/SKILL.md · 25 lines

What it actually says

Senior Computer Vision

Build CV systems that generalize, are measurable, and are deployable.

Quick Start

  1. Specify the task precisely: classification vs detection vs segmentation; latency and target hardware.
  2. Dataset first: define label taxonomy, edge cases, split strategy, and evaluation metrics.
  3. Train with discipline: baselines, ablations, and error analysis (not just “more epochs”).
  4. Deploy with realism: preprocessing parity, batching, quantization/trt where needed, monitoring.

Optional tool: dataset inventory (no ML deps)

For a directory like data/train/<class>/... or any image folder:

python ~/.codex/skills/senior-computer-vision/scripts/dataset_inventory.py data/ --out /tmp/dataset_report.json

References

  • Metrics and splits: references/metrics-and-splits.md
Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 9d ago First seen · 25 lines · 57 tokens per session scan A 59ef0e9cfcfb

Subscribe to this mod's changes

senior-computer-vision is a skill published in the GitHub repository vadimcomanescu/codex-skills (24 stars, last pushed 7mo ago), licensed MIT. It adds 57 tokens to every session and 246 once invoked, about $0.0003 per session on Opus 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 2026-08-30.

Related

Other skills, from other repositories

arboreto

Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for…

K-Dense-AI/scientific-agent-skills · 66 tokens

torchdrug

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.

K-Dense-AI/scientific-agent-skills · 61 tokens

deepspot-m

Generate transcriptome-wide virtual spatial transcriptomics from H&E histology with DeepSpot-M. Use when you need spatial gene expression in log1p-CPM for 224x224 tiles at about 20x, want to query protein-coding genes by symbol instead of a fixed panel, or want to run prediction across a whole slide after tiling with…

K-Dense-AI/scientific-agent-skills · 80 tokens

pyhealth

Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14, EHRShot), defining tasks (mortality, readmission, length-of-stay, drug recommendation, sleep staging, ICD coding, EEG events), instantiating models (Transformer…

K-Dense-AI/scientific-agent-skills · 216 tokens

pick-a-pii-model

Select an on-device OpenMed PII model from the committed registry by language, runtime format, and size budget, then require recall validation before deployment. Use when an agent must choose a local PII detector for CPU, Apple Silicon, or a mobile export without relying on live model discovery.

maziyarpanahi/openmed · 64 tokens

esm

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…

synthetic-sciences/openscience · 86 tokens