Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Transform brain dumps into polished blog posts in Nick Nisi's voice. Use when the user wants to write a blog post with scattered ideas, talking points, and conclusions that need organization into a cohesive narrative with Nick's conversational, authentic, and thoughtful tone.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Transform unstructured brain dumps into polished blog posts that sound like Nick Nisi.
Accept whatever the user provides:
Don't require organization. The mess is the input.
Load references/voice-tone.md to understand Nick's writing style.
Key characteristics:
Read references/story-circle.md to understand the narrative framework.
Determine if the content fits a story structure:
Not every post needs the full Story Circle, but look for narrative opportunities.
Structure the material into sections:
Common structures:
Choose the structure that fits the content.
Apply voice characteristics:
Opening:
Body:
Technical content:
Tone modulation:
Ending:
Check the post:
Show the post to the user for feedback and iterate.
"AI is going to replace developers."
I must have heard that phrase a hundred times in the last year.
I've been thinking a lot about how we use AI in our daily work.
Then something clicked.
I watched it use rg to search through codebases, just like I would.
I won't lie – joining Meta was intimidating.
I watched it use `rg` to search through codebases, just like I would.
It ran `npm test` to verify its changes weren't breaking anything.
You're not being replaced; you're being amplified.
references/voice-tone.md - Complete voice and tone guide. Read this first to capture Nick's style.references/story-circle.md - Story Circle narrative framework. Check if content fits a story structure.User provides brain dump:
thoughts on using cursor vs claude code
- cursor is in IDE, feels familiar
- but claude code is in terminal, my natural environment
- tried cursor first, felt weird leaving vim
- claude code met me where I was
- not about which is better, about workflow fit
- some devs love IDE integration
- I need terminal access
- conclusion: use what fits YOUR workflow
Process:
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
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Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.