suaveplan-drive

Hash, curve, cipher, AEAD, signing, key agreement, vaults

10 skills

Test utilities: vitest fixtures, DOM helpers, Playwright E2E, integration containers, linter

10 skills

Bytes, timing, datetime, MIME, serialize, diff, templates, bitpacker

9 skills

Authentication + authorization: auth, permissions, JWT, OAuth, RBAC/ABAC

7 skills

ETL, CRDT, dedup, pagination, A/B testing, browser analytics, GraphQL

7 skills

Event bus, sourcing, OTel/metrics/schema bridges, transport

6 skills

AGENTS.md as project instructions, by one option. Under claude-md-or-agents-md a project with no instruction files of its own gets its AGENTS.md files instead: a prompt.context hook asks $.fs.ancestors for every AGENTS.md and .claude/AGENTS.md from the filesystem root down to the working directory and hands them to the engine as project instruction files, which it renders, announces and withholds from agents exactly as it does CLAUDE.md, skipping any file the engine already loaded by import or link; a tool.call hook on Read attaches the ones under the project root as the engine attaches a nested CLAUDE.md. Under claude-md-and-agents-md every AGENTS.md is loaded beside CLAUDE.md. Under managed-only the project's and the person's instruction files are dropped and the organization's managed ones kept. Under claude-md the engine's walk stands alone. claude-md-or-agents-md is the default.

Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data

9 skills · 9 commands · 11 agents · 1 MCP server

Automates browser interactions for web testing, form filling, screenshots, and data extraction

12 skills

Install the "AAS Data Analytics & Business Intelligence" workflow plugin from Agentic Awesome Skills.

16 skills

The official CCXT MCP server plus the ccxt-mcp usage skill — market data, balances and opt-in trading across 100+ crypto exchanges and prediction markets, with keys kept local.

1 MCP server

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