Agent skills for HTTP / API

Plan, create, and test lead magnets and data collection processes in a consent-based and transparent manner.

Plan and optimize lead magnets for email capture and lead generation.

Scrape and verify business leads using Apify, classify with LLM, enrich emails, and save to Google Sheets.

Automate Leadoku tasks via Rube MCP (Composio).

Generate a Lean Canvas with problem, solution, metrics, cost structure, UVP, unfair advantage, channels, segments, and revenue.

Local context tooling for AI agents. Use it to select, shape, reuse, recover, and inspect context before inference when reading files…

Code search via LeanKG MCP when HTTP :9699 is healthy; otherwise skip LeanKG and use default Grep/Glob/Read.

Turn an uploaded PDF, paper, book chapter, document, URL, or user-provided topic into a structured, interactive learning course.

Generates structured HTML study plans with interactive Mermaid diagrams from pre-analyzed video data.

Study the project before touching it — read the structure, find the entry points, trace how it actually works, learn its conventions — and…

Extract a learned skill from the current conversation

Use when a user asks conceptual questions about Shuriken — what it does, what features exist, how a feature works, or "what is X on…

Use when the user invokes /learn or asks to remember, save, or capture a pattern, decision, gotcha, procedure, or feature dossier from…

[Beta] CI-only learning aggregation workflow using gh-aw (GitHub Agentic Workflows).

An opt-in programming tutor, configurable by level, for junior developers who want to understand what they are building.

Adjusts and refines dynamic learning plans based on learner feedback while preserving existing progress and milestones.

Operate a user-authorized online learning platform to resume eligible courses, use the highest visible allowed playback speed, handle…

Tracks study milestones, mastery evidence, review schedules, and recommended next steps using evidence-based, read-only progress summaries.

Generates standardized, multi-chapter learning project directories and runnable workspaces for interactive coding courses.

For factual learning: assigns norm, burden of proof and counter-argument; result: test product with risk and next step.

Converts structured knowledge into interactive diagrams using 5 core visualization types