1,181 boosters — open source, verified from GitHub, ready to install
You are a writing editor. Your job: take text that reads like AI wrote it and make it read like a specific human did. That means two things: strip the machine patterns and inject real voice. One without the other fails. 1. Read the input text carefully 2. Scan for all 40 patterns listed below
Expert in content strategy, SEO-driven storytelling, and developer advocacy. Bridges the gap between technical products and market adoption. Use when creating marketing content, developing content strategy, writing blog posts, or planning developer outreach.
Clawstr is a decentralized social network built on Nostr that enables AI agents to post, interact, and transact with each other using Bitcoin payments. It benefits AI developers and agents seeking censorship-resistant, decentralized communication and economic coordination.
你精通 LaTeX/TikZ 绘图和 draw.io XML 生成，擅长将论文中的系统架构、协议流程、技术方案、技术路线图转化为高质量配图。 执行任务时不要陷入惯性——不是所有图都该用 TikZ，不是所有架构都该自下而上，不是遇到编译错误就反复微调同一行代码。带着目标进入，边画边判断，遇到问题就诊断根因，发现方向错了就换方案——全程围绕「这张图要传达什么信息」做决策。 ① 定义成功标准：这张图要传达什么信息？读者看到后应该理解什么？几个模块、几层关系、什么样的视觉层次？这是后续所有判断的锚点。
Transform podcast audio into cinematic visual content using Seedance 2.0 on Higgsfield. This skill produces video prompts that replace static audiograms with storytelling-driven visual experiences built entirely from constructed imagery. The hook is the opening frame that stops the scroll. It must c
Videos with 80%+ completion get 2-3x algorithmic reach. Your hook determines everything. Layer these elements for maximum impact: 1. Base Layer — Ambient bed (room tone, wind, hum) or silence
Traditional testimonials fail because they're static talking heads in flat lighting. Cinematic social proof makes viewers feel the transformation. Show the result first, the emotion second, the person third. The first two seconds determine whether the viewer stays. Every testimonial video needs an i
Transformation content is the highest-engagement format on social media. This skill generates precise Seedance 2.0 prompts that capture the full emotional arc of change: tension, reveal, payoff. Before generating prompts, establish: Show both states in rapid alternation — before/after/before — withi
Seedance 2.0 accepts multi-modal inputs and produces short-form cinematic video output. Before writing any prompt, confirm the assets available: Before generating a prompt, answer these questions: Every course promo follows one arc:
The first two seconds determine whether a viewer stops scrolling. These patterns open with immediate visual authority. Luxury camera work operates within a narrow, controlled vocabulary. No exceptions. The most used luxury camera move. Camera travels toward subject at 0.1x–0.3x of normal speed. Tota
1. Never show a flat screenshot. Always add depth — perspective, reflection, environment, motion. 2. The UI is the hero, not the narrator. Show the interface doing things, not someone explaining it. 3. Sound sells more than visuals. A satisfying click, a smooth transition sound, a bass hit on featur
When generating a prompt, collect or infer: Seedance 2.0 prompt structure: Each prompt block should be 15–25 lines covering: scene, camera, lighting, timing breakdown, motion notes, and sound direction.
The most powerful personal brand camera move. Communicates: "this person is worth getting closer to." Creates authenticity by mimicking doc-style camera work. Shows the subject from multiple angles, communicating confidence.
A complete system for crafting Seedance 2.0 prompts on Higgsfield that feature AI avatars, digital personas, virtual presenters, and synthetic characters as the primary subject. This skill covers everything from photorealistic digital humans to stylized 3D characters, including hook patterns, enviro
Thin CLI wrapper around PaperBanana (a.k.a. PaperVizAgent), a multi-agent figure-generation pipeline for academic papers. The skill provides exactly one script: . It feeds
Systematic management of an academic literature library via Zotero. Standard collection hierarchy: Use tags to cross-cut the collection hierarchy:
Structured workflow for reading academic papers efficiently. Follow the appropriate level template above. When in doubt, start with Level 2. After reading, save the digest:
Turn social media paper recommendations into actionable research items. Use platform-specific tools to fetch the full content: From the extracted content, identify all referenced papers:
A creative prompt booster that encourages Claude to identify interconnected patterns in code and systems with three intensity levels (hit/party/trip). Best suited for developers seeking unconventional, pattern-focused code analysis and ideation.
Systematic literature survey for positioning a research contribution. Work with the user to pin down: Every research topic sits at an intersection of multiple dimensions. Identify 2-4 axes:
Pipeline for downloading academic paper full-text at scale. Handles the three classes of sources that exist in 2026: 1. Publisher TDM APIs (Elsevier / Wiley / Springer) — for paywalled content where the institution has a subscription 2. OA aggregators (Unpaywall / OpenAlex / Crossref) — for Open Acc
Systematic, multi-engine academic paper search. Choose engines based on the search goal: Before searching, clarify:
A structured After-Action Review skill that guides teams through debrief questions (expected vs. actual outcomes, analysis, and next steps) to extract learning from significant events. Useful for project managers, team leads, and anyone conducting post-event analysis.
WRAP is a decision-making framework that helps developers and AI systems work through major decisions by countering cognitive biases (narrow framing, confirmation bias, emotion, overconfidence). Best suited for product decisions, system design choices, or when stuck between technical approaches.
