hyperskills

⚡ hyperskills

Focused AI agent skills for things models don't already know

Knowledge, guidance, wisdom, SOTA. Reach for what fits.

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💎 What This Is

Models already know how to write React components, Kubernetes manifests, and PyTorch code. They don't need 300 lines of examples for that.

hyperskills is built around an agent workflow. Brainstorming structured by the Double Diamond. Wave-based research with deferred synthesis. Verification-driven planning and implementation. Cross-model peer review that catches what self-review misses. Six orchestration strategies for multi-agent work. Conversation consolidation that pulls signal out of past sessions into persistent memory. The process skills are the heart of it, mined from thousands of real dispatches and tens of thousands of tracked operations.

Domain skills round out the toolbox where models have stale or missing knowledge: current Astral Python tooling, Tilt operational decision trees, and terminal UI design that survives across emulators.

Each skill encodes procedural knowledge, decision trees, anti-patterns, and current SOTA. None prescribes a strict workflow. They give you knowledge and framing; you decide when to reach for them. Skills carry procedural knowledge in-context; Sibyl carries decisions, patterns, and learnings across sessions. 16 skills, all installable independently.

⚡ Installation

Claude Code

# Register the marketplace, then install
/plugin marketplace add hyperb1iss/hyperskills
/plugin install hyperskills@hyperb1iss

Vercel Skills (skills.sh)

# All skills
npx skills add hyperbliss/hyperskills --all

# Pick what you need
npx skills add hyperbliss/hyperskills --skill implement
npx skills add hyperbliss/hyperskills --skill orchestrate

Manual

git clone https://github.com/hyperb1iss/hyperskills.git
ln -s $(pwd)/hyperskills/skills ~/.claude/skills/hyperskills

🪄 Composing Skills

Skills are independent. None of them require the others. A typo fix doesn't need brainstorming, a clear bug doesn't need research, and the Python tooling skills compose freely.

A few combinations come up often, more as observation than prescription:

Situation	Skills that pair well
New feature	brainstorm, plan, implement, cross-model-review
Greenfield project	brainstorm, research, plan, orchestrate, implement
Architecture decision	brainstorm, research
Large refactor	plan, orchestrate, implement, cross-model-review
Bug fix	implement (the skill scales itself for trivial fixes)
Python project work	uv, ruff, ty, uv-build
Knowledge consolidation	dream pulls insights from past sessions into Sibyl

Domain skills (git, tilt, tui-design, uv, ruff, ty, uv-build) plug in wherever the work touches their territory. Any skill can loop back when new questions emerge.

🔮 Skills

Process Skills

How to approach a class of work: workflows, phases, decision gates. The interesting part of hyperskills lives here.

brainstorm: Structured Ideation

Double Diamond model for creative work. Diverge on the problem, converge on a definition, diverge on solutions, converge on a decision. Grounded in Sibyl so you don't re-explore solved problems. Includes a Council pattern (advocate / critic agents) for complex architectural decisions.

/hyperskills:brainstorm

research: Multi-Agent Knowledge Gathering

Wave-based research with deferred synthesis. Deploy agents in waves across a research surface, run gap analysis between waves, then synthesize with the full picture. Covers technology evaluation, codebase archaeology, SOTA analysis, and competitive landscape patterns. Caps at 3 waves for most research; if that isn't enough, the question itself needs reframing.

/hyperskills:research