1,330 boosters — open source, verified from GitHub, ready to install
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.
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.
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
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
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:
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:
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
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:
Systematic management of an academic literature library via Zotero. Standard collection hierarchy: Use tags to cross-cut the collection hierarchy:
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:
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.
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.
Named in homage to Tagore, whose prose carried what frontier models reach for and miss: a point of view, specificity over abstraction, and restraint over puffery. The skill exists to bring those qualities back to AI-drafted text. You are a writing editor whose job is to make prose sound like a human
Treat the input file as the content source and the Word template as the formatting source. Do not expect pandoc or PDF import to infer template semantics. Build a project-specific Python postprocessor after inspecting the template and the converted body document. Do not treat the bundled starter pip
Grasp is a metacognitive verification protocol that helps users achieve confirmed understanding of AI-generated results through structured dialogical verification. It benefits developers and collaborators who need to validate their comprehension of complex AI outputs.
Analyze skills using historical session data + static quality checks, output a diagnostic report with P0/P1/P2 prioritized fixes. Scores each skill on a 5-point composite scale across 8 dimensions. CSO (Claude/Agent Search Optimization) = writing skill descriptions so agents select the right skill a
Nereid enables real-time collaborative diagram editing in Mermaid through an MCP-powered TUI interface, with attention-coordination features for sequence diagrams and flowcharts. Best suited for teams working live on system architecture and flow visualization.
Turn a folder of raw files into a Markdown vault that an LLM can grep, and then answer questions over that vault responsibly. source file, carrying retrieval frontmatter (abstract / tags / synonyms) + a
Automates upgrading the sunsama-api dependency and wrapping new API endpoints as MCP tools, helping developers keep their Sunsama integrations current and fully functional.
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