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Found 2,002 Skills
Run full security scans on the codebase using Ruflo security tools
Use when reading from or writing to Neo4j with Apache Spark or Databricks using the Neo4j Connector for Apache Spark (org.neo4j:neo4j-connector-apache-spark). Covers SparkSession setup, DataFrame reads via labels/Cypher/relationship scan, DataFrame writes with SaveMode, node.keys for MERGE, relationship write mapping, partition and batch tuning, PySpark and Scala examples, Databricks cluster config, Databricks secrets for credentials, Delta Lake to Neo4j pipelines. Does NOT handle Cypher authoring — use neo4j-cypher-skill. Does NOT handle the Python bolt driver — use neo4j-driver-python-skill. Does NOT handle GDS algorithms — use neo4j-gds-skill.
Financial leadership for startups and scaling companies. Financial modeling, unit economics, fundraising strategy, cash management, and board financial packages. Use when building financial models, analyzing unit economics, planning fundraising, managing cash runway, preparing board materials, or when user mentions CFO, burn rate, runway, fundraising, unit economics, LTV, CAC, term sheets, or financial strategy.
MUST activate when the user wants to build, create, or generate a React application, React app, web application, single-page application (SPA), or frontend application — even if no project files exist yet. MUST also activate when the project contains a uiBundles/*/src/ directory or sfdx-project.json and the prompt says create, build, construct, or generate a new app, site, or page from scratch — even if the prompt also describes visual styling. MUST also activate when the task spans more than one ui-bundle skill. Use this skill when building a complete app end-to-end. This is the orchestrator that coordinates scaffolding, features, data access, frontend UI, integrations, and deployment in the correct dependency order. Without it, phases execute out of order and the app breaks. Do NOT use for Lightning Experience apps with custom objects (use generating-lightning-app). Do NOT use for single-concern edits to an existing page (use building-ui-bundle-frontend).
Interactive workflow for creating new skills for the skills-il organization. Guides through category selection, use case definition, folder scaffolding, metadata.json generation with bilingual metadata, instruction writing, Hebrew companion creation, and validation. Use when user asks to create a new skill, scaffold a skill for skills-il, write a SKILL.md, contribute a skill, new skill template, or liztor skill chadash. Enforces skills-il conventions (kebab-case naming, Hebrew transliterations, bilingual display names, progressive disclosure, validate-skill.sh compliance). Do NOT use for editing existing skills, creating skills for non-skills-il platforms, or generic markdown file creation.
Security & compliance skill suite providing OWASP scanning, CVE detection, GDPR/SOC2 audits, threat modeling, and incident response workflows for AI coding agents
When the user says phrases like "update the index", "add this to the index", "added a new file", "added a new book/note/document", "need to update the material library index", the **Incremental Maintenance Mode** of this skill should be used. When the user says phrases like "index health check", "index体检", "check the index", "sync the index", "fill in missing index entries", the **Health Check Mode** of this skill should be used to scan all libraries ↔ INDEX to find missing/invalid entries and batch fill them in. Applicable to all libraries with INDEX.md: Reading Notes Library, Core Concepts Library, Viral Script Library, 04-Methodology Precipitation, and any newly created libraries in the future. The value of an index depends on content accuracy—you must fully read the original text before writing; inferring based on file names or impressions will render the index useless for reference. Even if the user only says "I added a file" or "put this in", as long as it involves the index, this skill should be triggered. Do NOT trigger for: Only viewing the index (directly Read), deepening topics (use li-topic), generating scripts (use li-writer). Use when the user adds new content to any indexed library, OR runs a global index health-check to find missing/stale entries.
Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.
Add a new package to the Remotion monorepo, including package scaffolding, monorepo registration, documentation, build scripts, tests, and release checklist updates. Use when creating a new @remotion package.
~60-80s explainer video for any URL — GitHub repo, product page, docs site, blog post, or launch. Canonical workflow for URL walkthroughs. Use when the user asks to "explain this URL / repo / website / product", "make a walkthrough video for [url]", "demo this site", "Loom-style explainer of [url]", "explainer for github.com/...", or "explain this product link". Drives a real browser through the URL, generates an avatar lipsync, and composites in a 1280×800 macOS Sonoma frame with a 246-pixel bottom-left avatar circle. GitHub URLs activate a repo-aware mode (README scan + live-demo detection); other URLs use a generic page-walkthrough flow.
Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
Run a spec-driven agent loop where coding tasks live as markdown specs that move through inbox → active → archive, get implemented by Claude Code or Codex, and pass a review gate before they count as done. Use when the user mentions "loop factory", a "spec-driven loop", an "agent factory", wants repeatable/reviewable agent work, or when a repo has a factory/specs/inbox or factory/specs/active directory. Also covers installing and scaffolding the loop-factory CLI into a project.