Borrowing it
Nothing to install: this file belongs to claude-world/claude-world-studio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/claude-world/claude-world-studio/main/.claude/skills/content-pipeline/SKILL.mdgit clone --depth 1 https://github.com/claude-world/claude-world-studioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/claude-world/claude-world-studio/content-pipeline)<a href="https://agentmods.dev/skills/claude-world/claude-world-studio/content-pipeline"><img src="https://agentmods.dev/badge/skills/claude-world/claude-world-studio/content-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/claude-world/claude-world-studio/content-pipeline"><img src="https://agentmods.dev/badge/skills/claude-world/claude-world-studio/content-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 17 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
- medium MCP Rug Pull · line 18 uvx/uv tool run commands without ==version create a rug-pull risk.Fix: Pin the version: uvx package-name==1.2.3
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.02496 |
| Opus 5 | $0.00046 | $0.01248 |
| Sonnet 5 | $0.00018 | $0.00499 |
| Haiku 4.5 | $0.00009 | $0.00250 |
Grade A, and why
content-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Pipeline
AI-powered content pipeline: trend discovery -> deep research -> algorithm-optimized writing -> social publishing.
MCP Servers Required
Install via uvx (one-time, auto-cached):
uvx --from 'trend-pulse[mcp]' trend-pulse-server
uvx --from cf-browser-mcp cf-browser-mcp
uvx --from notebooklm-skill notebooklm-mcp
MCP Tools Reference
trend-pulse (12 tools)
Trend Data:
- get_trending(sources, geo, count): Query ALL 20 free sources. sources="" for all. geo: "TW"/"US"/"JP"/"". count: 20.
- Sources: google_trends, hackernews, mastodon, bluesky, wikipedia, github, pypi, google_news, lobsters, devto, npm, reddit, coingecko, dockerhub, stackoverflow, producthunt, arxiv, lemmy, dcard, ptt
- search_trends(query, sources, geo): Cross-source keyword search.
- list_sources(): List all sources.
- take_snapshot(sources, geo, count): Save snapshot for velocity tracking.
- get_trend_history(keyword, days, source): Historical data with direction.
Content Guide:
- get_content_brief(topic): Writing brief with hook examples, patent strategies, CTA.
- get_scoring_guide(): 5-dimension patent scoring. Score >= 70 required.
- get_platform_specs(platform): Platform specs (char limits, algorithm signals, posting times).
- get_review_checklist(): Quality review checklist (7 checks).
- get_reel_guide(): Reels script guide (3 styles).
Search:
- search_threads_posts(query): Search Threads posts by heat score.
cf-browser (10 tools)
Headless Chrome via Cloudflare Browser Rendering. Use instead of WebFetch for JS-rendered pages.
- browser_markdown(url): Clean Markdown. Most used for deep research.
- browser_content(url): Full rendered HTML.
- browser_screenshot(url): Full page screenshot (PNG).
- browser_pdf(url): Generate PDF.
- browser_scrape(url, selector): CSS selector extraction.
- browser_json(url, schema): AI-driven structured data extraction.
- browser_links(url): Extract all hyperlinks.
- browser_a11y(url): Accessibility tree (low token cost).
- browser_crawl(url): Async multi-page crawl.
- browser_crawl_status(id): Check crawl progress.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 211 lines · 91 tokens per session scan A a41e75fb77db
content-pipeline is a skill published in the GitHub repository claude-world/claude-world-studio (72 stars, last pushed 2mo ago), licensed MIT. It adds 91 tokens to every session and 2,496 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
notebooklm-research
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…
notebooklm
Install, authenticate, troubleshoot, and operate Gemini Notebook through the notebooklm-py CLI or typed async Python API. Use for notebook and source management, grounded chat and research, and artifact generation or download when the user mentions Gemini Notebook, notebooklm-py, the notebooklm CLI, or its Python API.…
release-sync
Syncs latest release content to NotebookLM and HQ Knowledge Base after version tagging. Reads CHANGELOG, CLAUDE.md, and hook README, updates notebook sources, and ingests release digest. Optionally generates podcast from updated knowledge base. Use after tagging a new version to propagate release knowledge.
notebooklm
This skill should be used when the user wants to query their Google NotebookLM notebooks for citation-backed, source-grounded answers, or manage notebooks, sources, and Studio content (audio, report, video, infographic, presentation, data table, flashcards, quiz, mind map). It drives the @roomi-fields/notebooklm-mcp…
content-engine
Use when a content operation needs a SYSTEM: a dated editorial calendar built top-down from pillars, plus the stage gates, briefs, WIP limits and 1:10 atomization plan that move each slot to publish-ready. NOT writing the pieces (that is article-writing), NOT publishing them (that is social-publisher), NOT keyword…
content-factory
The whole setup in one command — run the entire reel factory end to end. Claude takes a proven viral format, studies it frame by frame, writes an original hook + script in your voice, builds the finished reel (on-screen text, scene per beat, voiceover/music/captions/animations), writes the caption + hashtags, and…