Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Build software with AI by describing what you want. Covers prompting techniques, workflow patterns, rules files, and when to intervene.
Build software with AI by describing what you want. Covers prompting techniques, workflow patterns, rules files, and when to intervene.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Programming where you describe what you want and let AI generate code. You evaluate by results, not by reading every line. Coined by Andrej Karpathy (Feb 2025). Key distinction (Simon Willison): If you review, test, and can explain the code โ that's software development, not vibe coding. Vibe coding means accepting AI output without fully understanding every function.
TopicFilePrompting techniquesprompting.mdResearch-Plan-Implement workflowworkflow.mdRules files (.cursorrules, CLAUDE.md)rules-files.mdCommon pitfalls and fixespitfalls.mdTool selection by use casetools.md
Vague prompts โ vague results. Before touching your AI tool: What specific problem are you solving? What does "done" look like? What are the constraints (stack, integrations, flow)? Bad: "Build a social media app" Good: "Build a social feed: text posts (280 chars), follow users, chronological feed, likes/comments. Use React, Tailwind, Supabase."
Persistent context that teaches AI your conventions. Put it in once, applies to every interaction: Cursor: .cursorrules or .cursor/rules/ Claude Code: CLAUDE.md Windsurf: .windsurfrules See rules-files.md for templates.
Before implementing, have AI explore and plan: Research: "Read the auth module, explain how sessions work" Plan: "Write the files you'll modify and changes in each" Implement: Only after reviewing the plan Catching misunderstanding during planning = 10x cheaper than debugging cascading errors.
Let it flow: Scaffolding, UI components, exploring ideas Intervene: Auth, payments, data handling, anything security-adjacent Always review: Database schemas, API permissions, user data handling
AI generates code that looks flawless but has subtle bugs. After every change: Run test suite Manually test the affected feature Check console for errors Verify happy path AND edge cases
The Karpathy move: copy error message, paste with no comment, usually it fixes it. If AI can't fix after 2-3 attempts, describe the behavior you want instead.
Set explicit boundaries: Length: "Under 50 lines of code" Format: "Only the modified function, not entire file" Scope: "Only payment flow, don't touch auth" Style: "Follow existing pattern in UserService.ts"
Good for: Prototypes, MVPs, internal tools, weekend projects, UI components, boilerplate, learning Bad for: Security-critical code, performance-critical code, compliance-heavy domains, long-term production systems
The best vibe coders understand architecture, spot bad AI output, and know when to intervene. If you can't evaluate whether AI produced good code, you need to learn more before vibe coding production systems.
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.