Automate source-grounded research with Google NotebookLM. Create notebooks from URLs, text, or local files; ask cited questions; run fast or deep web research; create articles and social drafts; and generate or download audio, video, cinematic video, slides, reports, study guides, quizzes, flashcards, mind maps…
A Chinese-language workflow for turning collected source material into articles and versions for several publishing platforms, including formatted previews, cover images, social posts, podcasts, and videos.
Full-autopilot trend discovery, deep research, and social publishing pipeline. Uses trend-pulse (20 sources), cf-browser (headless Chrome), and notebooklm (research + artifacts) MCP servers. Generates algorithm-optimized content based on Meta's 7 patent-based ranking algorithms. Use when user mentions trending topics…
Audit an existing content library for freshness decay and coverage gaps — scores every piece 0-100 on age, statistic currency, link health, and citation recency, maps covered topics against target keywords, and produces a prioritized report: top refresh candidates, gap topics, retire candidates, and the exact…
Generate a research-backed content brief from a keyword or topic — keyword data with volume and difficulty, top-5 competitor and E-E-A-T analysis, search-intent classification, audience pain points, a section-by-section outline with word counts and citation targets, plus SEO and AEO/GEO strategy (AI Overview status…
Produce a publication-ready, fact-checked, brand-compliant, SEO-optimized content piece through the full 10-phase pipeline: every phase dispatched to a dedicated subagent (researcher, fact-checker, drafter, visual annotator, scientific validator, structurer, SEO/GEO optimizer, humanizer, reviewer, output manager)…
Run a multi-agent content production pipeline where specialist agents work together to research, write, edit, optimise for SEO/GEO, and validate against analytics data — with a master agent reviewing all outputs before human approval. Use this skill whenever the user wants to produce content using multiple agents…
Own the whole marketing content pipeline for this codebase — set itself up, scan the product for what actually works, write the copy, enforce the rules mechanically, and render cards (PNG) and video (MP4) from one HTML contract. Use for "set up marketing", "write a post", "review this copy", "make a card for this"…
End-to-end content pipeline — idea in, published evidence-backed page out. Orchestrates search recon, real lab experiments (cloud VMs / local Docker), screenshot and asset production, drafting in a defined voice, verification loops, ship, and a write-back to your knowledge base. Triggers on "/factory ...", "new piece…
Prompts used by the composable atoms in services/atoms/ — the founder-voice devdiary narrator and the LangGraph pipeline architect. These wrap the lower-level building blocks the TemplateRunner chains into pipelines.
Content quality assurance — the adversarial QA gate pack. Topic-delivery, internal-consistency, publication-readiness review, aggregate rewrite, writer self-review for contradictions, self-consistency sampling, an LLM quality rubric, and vision-QA for inline images and rendered preview screenshots. Use after a draft…
Video director — given a post body + narration script + target duration, produces a JSON shot list (ordered shots with per-shot source plugin, prompt/query, and duration) for the post's video. Enforces the no-AI-humans + stylized-not-photoreal policies. Used by the generatevideoshotlist pipeline stage. Operator brand…
Complete API for Google NotebookLM - full programmatic access including features not in the web UI. Create notebooks, add sources, generate all artifact types, download in multiple formats. Activates on explicit /notebooklm or intent like "create a podcast about X".
Content creation pipeline orchestrator for any AI agent. Chains great-writer -> brilliant-visualizer -> typeset -> deliver into a complete write-to-deliver flow. Optional multi-platform publishing via /publish skill (superb-publisher). Loose coupling: depends only on skill names and I/O contracts, not sub-skill…