Agent skills for Cursor

For expert opinion writing practice: assigns norm, burden of proof and counter-argument; result: test product with risk and next step.

Multi-framework content review with convergent synthesis.

Use when a user wants an AI, LLM, machine-learning, agent, optimizer, protocol, or infrastructure topic turned into a sourced visual…

Use when the user asks for a rich explanation of a code change, diff, branch, or PR.

Use when dense technical reports, status updates, incidents, errors, decisions, or prior answers need to be restated for a nontechnical…

Explain SwiftASB in user-facing terms, including what it does, what it does not do, adoption tradeoffs, licensing, and when it is or is…

Use when explaining code, technical concepts, or implementation decisions.

Generates polished 1080p technical explainer and demo videos with Chinese voiceover, automated subtitles, and structured visual cards…

Create high-quality animated explainer visuals for essays and blog posts.

Explain any topic at five escalating levels — High School, College, PhD, Philosopher, Gigabrain — where each level responds directly to…

Exploits Kerberos Constrained Delegation misconfigurations in Active Directory using Impacket's findDelegation.py and getST.py (or…

Tests and exploits deep link (URL scheme and App Link) vulnerabilities

Exploits JWT algorithm confusion where the server's verification library trusts the alg named in the token header, by switching RS256 to…

Performs systematic, hypothesis-driven exploration and debugging across complex codebases using tree-search algorithms and domain adapters.

Analyze datasets to discover patterns, anomalies, and relationships.

Explore the local codebase before changes; a folder outside any repo, or machine state, is research's.

Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known…

Find where an AI/LLM application is failing in production and surface the failure patterns, working from real traces.

Investigate AI observability evaluations — `hog` (deterministic code-based), `llm_judge` (LLM-prompt-based), and `sentiment` (user-message…

Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP.

Guides exploration of $autocapture events captured by posthog-js to understand user interactions, find CSS selectors (especially data-attr…