6 on GitHubby Claude Code Pluginsv1.0.0MITupdated 2 months ago
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin computer-vision-processor in jar-reis/athenaeum. Commands read from the repo's manifest — 1 month ago.
What it installs
Skills (1)
Processing Computer Vision Tasks
This skill enables Claude to process and analyze images using computer vision techniques. It's used to perform tasks such as object detection, image classification, and image segmentation. Use this skill when a user requests analysis of an image, asks for identification of objects within an image, o
Engineering lifecycle skills forked from Matt Pocock's framework: SDLC scaffolding (setup-matt-pocock-skills), test-driven development (tdd), plan-to-issues decomposition (to-issues), context-to-PRD (to-prd), issue triage state machine (triage), disciplined diagnosis (diagnose), architecture deepening (improve-codebase-architecture), and zoom-out perspective (zoom-out). Run setup-matt-pocock-skills first to configure your repo before using the others.
Complete operations kit for FairDB PostgreSQL as a Service - VPS setup, PostgreSQL management, customer provisioning, monitoring, and backup automation
Open-source AI job search that runs locally in your AI coding CLI: scan job portals, evaluate listings into a structured A-H report with a 1-5 score, tailor your CV, track applications
53 marketing skills for technical marketers and founders: CRO, copywriting, cold email, prospecting, SEO, AI SEO, paid ads, SMS, ad creative, video production, image generation, co-marketing, churn prevention, pricing, referrals, revenue operations, sales enablement, customer research, site architecture, comprehensive AARRR-structured marketing plans, public relations and earned media, offer design, event marketing and webinars, and more
Harness Adobe's creative AI-powered tools to edit images, automate design workflows, and bring creative visions to life — from background removal to vectorization and professional retouching.
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
wshobson/agents 42,461
Maintain computer-vision-processor?
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