By roboflow
Manages end-to-end computer vision pipelines on Roboflow: upload and annotate datasets, train detection/segmentation/classification models, and deploy inference via serverless or self-hosted endpoints.
Protocol-level facts for Roboflow REST and Inference APIs — URL patterns, auth, parameters, error codes, and SDK quick-start. For deployment strategy and Workflow execution patterns, see roboflow-inference.
Use when uploading images, labeling, organizing datasets, creating Roboflow projects (detection/segmentation/keypoint/classification), tags, splits, versions, or RoboQL search.
Deployment option comparison (serverless, dedicated, self-hosted, batch) and Workflow execution patterns. For raw API URL patterns, auth, and request/response formats, see roboflow-api-reference.
Use when answering questions about Roboflow plans, credit usage, or cost estimation; directs users to roboflow.com/pricing for current dollar amounts.
Use when explaining where Roboflow features live in the app.roboflow.com web app, mapping intents like upload, annotate, train, deploy to specific page URLs.
External network access
Connects to servers outside your machine
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Agent-ready Roboflow plugin that contains skills and MCP configuration for computer vision workflows: data management, training, evaluation, inference, model selection, Workflows, Universe, plans, and Roboflow platform APIs.
This repository is a plugin-shaped source of truth for AI agents (Claude Code, Codex, Cursor, OpenCode, and others). The canonical skill content lives in skills/; plugin manifests point at those files instead of copying them elsewhere.
The repo ships both plugin manifests pointing at the same skill content and MCP config:
.claude-plugin/plugin.json — plugin name roboflow.codex-plugin/plugin.json — plugin name roboflowBoth manifests load skills from skills/ and bundle the Roboflow MCP server config from .mcp.json.
Install from GitHub — no clone required:
claude plugin marketplace add roboflow/computer-vision-skills
claude plugin install roboflow
The first command registers this repo as a marketplace source (run once per machine). The second installs the plugin.
For per-project isolation — for example, when different projects need different ROBOFLOW_API_KEY values for different workspaces:
claude plugin install roboflow --scope local
Local scope writes the plugin into the current project only and reads the API key from that project's environment.
git clone https://github.com/roboflow/computer-vision-skills
claude plugin marketplace add ./computer-vision-skills
claude plugin install roboflow
For a throwaway test without touching the installed-plugins list:
cd computer-vision-skills
claude --plugin-dir .
The Codex CLI currently exposes codex plugin marketplace add, upgrade, and remove. It does not expose a direct codex plugin install command or a codex --plugin-dir flow, so add this repo as a marketplace source and install the plugin from the plugin browser.
Install from GitHub:
codex plugin marketplace add roboflow/computer-vision-skills
Restart Codex, then open the plugin browser:
codex /plugins
Choose the Roboflow marketplace source, select the Roboflow plugin, install it, and press Space if it is installed but still disabled.
When editing a local clone, register it as a local marketplace source:
git clone https://github.com/roboflow/computer-vision-skills
cd computer-vision-skills
codex plugin marketplace add .
Restart Codex after edits. If the plugin browser still shows stale metadata, remove and re-add the local marketplace:
codex plugin marketplace remove roboflow
codex plugin marketplace add .
If you registered the GitHub marketplace source instead, refresh it with codex plugin marketplace upgrade roboflow.
Codex reads .agents/plugins/marketplace.json, which points source.path at the repo root via ./. Codex resolves source.path relative to the marketplace root, so the plugin manifest in .codex-plugin/plugin.json, the skills in skills/, and the Roboflow MCP server config in .mcp.json are all loaded from this repository.
Codex caches installed plugins under ~/.codex/plugins/cache/, so a running Codex session may not see edits until Codex is restarted or the plugin is reinstalled from the Plugin Directory.
Codex CLI picks up ROBOFLOW_API_KEY from the shell environment that launches the codex binary. In Codex desktop, set the key in the local environment used by the workspace. Use a project-scoped .env if you need different keys per project.
If you want the skills without the MCP server bundle — for example, with an agent that doesn't speak the plugin manifest format — install them directly:
npx skills add roboflow/computer-vision-skills
Install a single skill:
npx skills add roboflow/computer-vision-skills --skill inference
By default this installs into ./.claude/skills/ for the current project. Pass -g for ~/.claude/skills/ (global).
The npx skills CLI works with any agent that reads SKILL.md files from .claude/skills/ — Claude Code, Cursor, OpenCode, and others. See vercel-labs/skills for the full CLI reference.
npx claudepluginhub roboflow/computer-vision-skills --plugin roboflowBuild high-quality datasets and computer vision models. Visualize datasets, analyze models, find duplicates, run inference, evaluate predictions, and develop custom plugins.
Computer vision image processing and analysis
Skills for finding, comparing, running, and prompting AI models on Replicate
Expert in vision models, OCR systems, barcode detection, and visual AI. Stays current with latest models (GPT-4V, Claude Vision, Mistral-OCR, etc.), optimization techniques, and specialized libraries. Use PROACTIVELY for image processing, document analysis, or visual AI tasks.
Machine learning training and inference pipeline using cloud GPUs (Modal, Lambda Labs, RunPod) with HuggingFace ecosystem - no local GPU required
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.