hyperskills
Health Warn
- License — License: MIT
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Low visibility — Only 5 GitHub stars
Code Pass
- Code scan — Scanned 3 files during light audit, no dangerous patterns found
Permissions Pass
- Permissions — No dangerous permissions requested
This is a curated collection of specialized agent skills designed to guide AI models through complex development workflows like brainstorming, planning, and orchestration. It acts as a pipeline for tasks where standard AI knowledge typically falls short.
Security Assessment
The overall risk is rated as Low. The automated code scan reviewed Makefile and configuration files without finding any dangerous execution patterns, hardcoded secrets, or requests for overly broad permissions. However, it is worth noting that the tool is designed to orchestrate development pipelines and interfaces with system utilities like Git. While the audited files are clean, users should exercise standard caution, as the deep integration of agent workflows means they inherit the permissions of the host CLI environment. No malicious network requests or sensitive data harvesting were detected.
Quality Assessment
The project is in active development, with its most recent updates pushed today. It is properly licensed under the permissive MIT license, making it fully open source and safe for integration. The primary drawback is its low community visibility; it currently has only 5 GitHub stars. This indicates that while the code is actively maintained by the original creator, it has not yet undergone widespread peer review or large-scale community testing.
Verdict
Safe to use. The code is clean, actively maintained, and properly licensed, though its low community footprint means you are relying primarily on the original author's expertise.
My collection of agent skills for a variety of tasks
hyperskills
Focused AI agent skills for things models don't already know
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 provides skills for things that are genuinely hard to get right without guidance — procedural knowledge distilled from thousands of real sessions, decision trees for high-stakes operations, and multi-agent orchestration patterns that actually work in production.
10 skills. Zero bloat. Each one earned its place through real-world evidence.
Installation
Claude Code
/plugin install hyperskills
Vercel Skills (skills.sh)
# Install all skills
npx skills add hyperbliss/hyperskills --all
# Or 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
How the Skills Work Together
The skills form a workflow pipeline. Each one handles a phase of the development lifecycle and hands off to the next:
brainstorm ──→ research ──→ plan ──→ implement ──→ cross-model-review
│ │ │ │
│ │ │ └──→ git
│ │ │
│ │ └──→ orchestrate (parallel agents)
│ │
└──────────────┴──→ Any skill can loop back when new questions emerge
Typical flows:
| Scenario | Flow |
|---|---|
| New feature | brainstorm → plan → implement → cross-model-review |
| Greenfield project | brainstorm → research → plan → orchestrate → implement |
| Bug fix | implement (straight to it — scale selection handles this) |
| Architecture decision | brainstorm → research → decide |
| Large refactor | plan → orchestrate → implement → cross-model-review |
You don't need to follow the full pipeline. Each skill has built-in scale selection — a typo fix doesn't need brainstorming, and a clear bug doesn't need research. Start wherever makes sense.
Skills
Process Skills
These encode how to approach a class of work — workflows, phases, and decision gates.
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 persistent memory (Sibyl) so you never re-explore solved problems. Includes a Council pattern for complex architectural decisions using advocate/critic agents.
/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. Max 3 waves — if that's not enough, reframe the question.
/hyperskills:research
plan — Task Decomposition
Verification-driven planning where every step has a concrete check. Decomposes work into 2-5 minute tasks ordered by dependency, marks parallelizable waves for orchestration, and tracks everything in Sibyl. Includes a trust gradient — full ceremony for early tasks, lighter review as patterns stabilize.
/hyperskills:plan
implement — Verification-Driven Coding
The core implementation skill, distilled from 21,321 tracked operations across 64+ projects. Encodes the patterns that consistently ship working code:
- 2-3 edits then verify — the sweet spot that prevents debugging spirals
- Scale selection — from trivial (1-5 edits) to epic (1000+), with the right strategy for each
- Dependency chains — build order for fullstack and Rust projects
- Error recovery — spiral prevention, the two-correction rule, when to
/clearand restart - Decision trees — read vs edit, subagents vs direct, bug fix vs feature vs refactor
/hyperskills:implement
orchestrate — Multi-Agent Coordination
Meta-orchestration patterns mined from 597+ real agent dispatches. Tells you which multi-agent strategy to use, how to structure prompts for parallel agents, and when to use background vs foreground. Six strategies: Research Swarm, Epic Parallel Build, Sequential Pipeline, Parallel Sweep, Multi-Dimensional Audit, and Full Lifecycle.
/hyperskills:orchestrate
cross-model-review — Bidirectional Cross-Model Code Review
The authoring model writes code, a different model reviews it — different architecture, different training distribution, no self-approval bias. Detects your host (Claude Code or Codex) and invokes the other model's CLI automatically. Covers both directions: Claude → Codex via codex review, and Codex → Claude via claude -p. Includes multi-pass strategy, piped-diff vs tool-access modes, and 7 ready-to-use prompt templates.
/hyperskills:cross-model-review
codex-review — Codex-Specific Code Review
The Claude → Codex direction in depth. Full Codex CLI reference including codex review (structured diff review) and codex exec (freeform deep-dive), multi-pass review strategy (correctness → security → architecture → performance), and integration with the Ralph Loop for iterative quality enforcement. Use cross-model-review if you want bidirectional host detection; use this one when you specifically want Codex as the reviewer from a Claude session.
/hyperskills:codex-review
Domain Skills
These encode specialized knowledge for specific technologies — reference material, decision trees, and hard-won patterns.
security — Security Operations
Frameworks and checklists for secure systems. STRIDE threat modeling, Zero Trust principles, OWASP Top 10, SLSA supply chain levels, incident response phases, and compliance framework reference (SOC 2, HIPAA, PCI DSS).
/hyperskills:security
git — Advanced Git Operations
Decision trees for the operations that actually cause problems. When to rebase vs merge, how to handle lock file conflicts, SOPS encrypted file resolution, undo operations by scenario, cherry-pick workflows, and repository archaeology commands.
/hyperskills:git
tilt — Kubernetes Development
Complete Tilt operational reference. CLI commands for log viewing, resource management, and debugging. Tiltfile authoring with build strategy selectors, live update decision trees, and resource configuration. Full API catalog and power patterns in progressive disclosure references.
/hyperskills:tilt
tui-design — Terminal UI Design System
Universal design patterns for building exceptional terminal user interfaces. Layout paradigm selector, interaction model decision trees, terminal color theory, visual hierarchy techniques, data visualization, and animation patterns. Framework-agnostic — works with Ratatui, Ink, Textual, Bubbletea, or any TUI toolkit. Includes a Unicode visual catalog and gallery of real TUI app design patterns.
/hyperskills:tui-design
Architecture
Skills use progressive disclosure to manage context efficiently:
Level 1: Metadata (name + description) ← Always in context (~100 words)
Level 2: SKILL.md body ← Loaded when skill triggers (~1,500-3,000 words)
Level 3: references/ ← Loaded on demand (unlimited)
Skills with reference files for deep-dive content:
| Skill | Reference Files |
|---|---|
implement |
benchmarks.md — quantitative data from 21k operations |
codex-review |
prompts.md — 7 ready-to-use review prompt templates |
cross-model-review |
prompts.md — 7 ready-to-use review prompt templates |
tilt |
api-reference.md, patterns.md — full Tiltfile API + power patterns |
tui-design |
visual-catalog.md, app-patterns.md — Unicode catalog + app gallery |
Compatibility
| Platform | Installation |
|---|---|
| Claude Code | /plugin install hyperskills |
| Codex CLI | npx skills add hyperbliss/hyperskills -a codex |
| Cursor | npx skills add hyperbliss/hyperskills -a cursor |
| GitHub Copilot | npx skills add hyperbliss/hyperskills -a copilot |
| Gemini CLI | npx skills add hyperbliss/hyperskills -a gemini |
Development
git clone https://github.com/hyperb1iss/hyperskills.git
cd hyperskills
make lint # Run linters
make format # Format files
make check # Validate plugin structure
make stats # Show plugin statistics
See AGENTS.md for the full contributor guide on adding new skills.
License
Licensed under the MIT License. See LICENSE for details.
Built by Hyperbliss Technologies
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