Cognitum cog: Federated Learning. Train AI across seeds without sharing raw data
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin cog-federated-learning in cognitum-one/cognitum-claude-plugin. Commands read from the repo's manifest — 1 month ago.
/cog-federated-learning:federated-learningInstall (if needed) and run the `federated-learning` cog on the paired seed
Slash commands, skills, and subagents for the Cognitum cog dev/test/deploy/ops loop. Wires a Claude Code session to a seed's /mcp endpoint so cogs can be authored against live device state.
5 skills · 5 commands · 4 agents
Cognitum Seed device plugin — connect to a local Seed appliance at https://169.254.42.1:8443 (HTTPS) or http://169.254.42.1 (USB mode), invoke its 114 MCP tools, and run guided setup / pairing / OTA / thermal / sensor / vector-store flows from Claude Code.
1 skill · 8 commands · 1 agent · 1 MCP server
Cognitum cog: Adversarial Detection. Detects tampered or spoofed sensor signals
1 command
Cognitum cog: Air Quality Monitor. Track indoor air quality with CO2 and particle sensors
1 command
Cognitum cog: Anomaly Attractor. Learns what's normal and catches anything weird
1 command
Cognitum cog: Audit Trail Logger. Record every action for compliance — tamper-proof log
1 command
AI-powered codebase understanding — analyze, visualize, and explain any project
9 skills · 10 agents · hooks
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
5 skills · hooks
Rewrite AI-sounding text so it reads like the writer without changing what it says.
1 skill
AI memory system — mine projects and conversations into a searchable palace. 47 MCP tools, auto-save hooks, guided setup.
3 skills · 6 commands · 1 MCP server · hooks
Perform AI code review on Git diffs — supports workspace changes, branch ranges, and single commits with concurrent per-file analysis, codebase search, and deep context-aware review.
2 commands
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
This page updates itself from your repo. Add the badge to your README, or get it Featured to pin it above the organic results with a followed link to your site.