Split datasets for training, validation, and testing
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin dataset-splitter in jar-reis/athenaeum. Commands read from the repo's manifest — 1 month ago.
Splitting DatasetsThis skill enables Claude to split datasets into training, validation, and testing sets. It is useful when preparing data for machine learning model development. Use this skill when the user requests to split a dataset, create train-test splits, or needs data partitioning for model training. The ski
/dataset-splitter:split-dataExecute AI/ML task with intelligent automation
Dialectic claim discipline for AI agents: scholastic vocabulary, peer-grill file-based reconciliation, runnable verifiers/falsifiers, multi-agent ratification, fleet-ratify N-agent attestation, permutation NxN fleet topology ratification. Synthesis: an unexamined claim does not exist.
10 skills
Open-source investment research terminal integration - equity analysis, crypto tracking, macro indicators, portfolio optimization, and AI-powered financial insights using OpenBB Platform
6 commands · 4 agents
Comprehensive operations manager for FairDB managed PostgreSQL service - SOPs, incident response, monitoring, and automation
6 commands · 3 agents
Comprehensive operations manager for FairDB managed PostgreSQL service - SOPs, incident response, monitoring, and automation
6 commands · 3 agents
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.
8 skills
Complete operations kit for FairDB PostgreSQL as a Service - VPS setup, PostgreSQL management, customer provisioning, monitoring, and backup automation
1 skill · 4 commands · 1 agent
Comprehensive toolkit for developing Claude Code plugins. Includes 7 expert skills covering hooks, MCP integration, commands, agents, and best practices. AI-assisted plugin creation and validation.
7 skills · 1 command · 3 agents
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3 skills · 1 command · 1 agent
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44 skills · 1 MCP server
WCAG accessibility auditing, compliance validation, UI testing for screen readers, keyboard navigation, and inclusive design
2 skills · 1 command · 1 agent
Deploy, debug, and monitor applications on Render: skills for blueprints, deploys and troubleshooting, a Render assistant agent, deploy/status commands, a render.yaml validation hook, and the Render MCP connector preconfigured over OAuth.
21 skills · 2 commands · 1 agent · 1 MCP server · hooks
Build, launch, grade, and schedule Claude Managed Agents (CMA) in your own Anthropic account — a plugin re-implementation of Anthropic's launch-your-agent reference skill (Apache-2.0; independent, not a fork). Every session starts with a goal (./my-agent/goal.json, opt-in SessionStart hook, driven by /cs-goal); loop_compiler.py compiles it into a bounded grade->iterate loop (max_iterations 1..20), a recurring cron scheduled-deployment loop, or a single-pass interview->stage->launch workflow. 6 skills: orchestrator (context: fork goal router) + interview + stage-launch + grade-iterate + run-without-you + wrap-up. 18 stdlib-only deterministic scaffolders (no network/API calls; live launches emitted as BYOK curl that never prints the key), 4 agents, 8 commands, hooks, 5 references, 4 assets. Validators enforce CMA limits. Distinct from engineering/agent-harness (generic domain loop) and write-a-skill (authors Claude Code skills, not CMAs).
6 skills · 8 commands · 4 agents · hooks
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