What is phoenix-research-pipeline?

A research workflow that searches YouTube, the web, and Reddit for security topics, filters the findings, and sends selected sources to Google NotebookLM. NotebookLM is a Google service that analyses sources you upload and answers questions about them.

Use it to collect security videos, articles, and Reddit discussions, then prepare the results for a blog, slide deck, or video script in NotebookLM.

It combines source collection and source-based analysis instead of requiring separate searches and manual uploads. It helps gather current material for a focused security topic.

phoenix-research-pipeline — Skill for Claude Code by Security-Phoenix-

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add Security-Phoenix-demo/security-skills-claude-code --skill phoenix-research-pipeline

Clone the repo

git clone --depth 1 https://github.com/Security-Phoenix-demo/security-skills-claude-code

Made for: Claude Code.

Or install phoenix-docs-research, the plugin that ships this one along with the rest of its 3 skills.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/security-phoenix-demo/security-skills-claude-code/phoenix-research-pipeline/github.svg)](https://agentmods.dev/skills/security-phoenix-demo/security-skills-claude-code/phoenix-research-pipeline)

Your own site

<a href="https://agentmods.dev/skills/security-phoenix-demo/security-skills-claude-code/phoenix-research-pipeline"><img src="https://agentmods.dev/badge/skills/security-phoenix-demo/security-skills-claude-code/phoenix-research-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/security-phoenix-demo/security-skills-claude-code/phoenix-research-pipeline"><img src="https://agentmods.dev/badge/skills/security-phoenix-demo/security-skills-claude-code/phoenix-research-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 173 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,072 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00173	$0.02072
Opus 5	$0.00086	$0.01036
Sonnet 5	$0.00035	$0.00414
Haiku 4.5	$0.00017	$0.00207

Measured yesterday against content hash b05109c18a34, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

phoenix-research-pipeline scanned grade A with 1 finding 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 yesterday.

The scan reads SKILL.md. This mod also ships 6 executable files (scripts/install.sh, scripts/notebooklm_push.py, scripts/pipeline.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

Installs: yt-dlp, scrapling, notebooklm-py, notebooklm-cli, praw, requests, curl-cffi, browserforge

plugins/phoenix-docs-research/skills/phoenix-research-pipeline/SKILL.md · 242 lines

How it starts

The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Phoenix Security Research Pipeline

Automated research → quality filtering → NotebookLM ingestion with Phoenix brand prompts.

Quick Start

# Interactive (prompts for topic)
python3 scripts/research_pipeline.py

# Full pipeline: YouTube + web + NotebookLM
python3 scripts/research_pipeline.py "CISA KEV exploited vulnerabilities 2025" \
  --count 25 --notebooklm --prompt blog

# YouTube only
python3 scripts/youtube_research.py "container security kubernetes" --count 25

# Web only (Brave primary, Scrapling fallback)  
python3 scripts/web_research.py "ASPM application security posture" --count 20 --reddit

# Push URLs directly to NotebookLM
python3 scripts/notebooklm_push.py --urls urls.txt --title "My Research" \
  --backend teng --prompt slides

CRITICAL: Authentication Required

NotebookLM requires Google auth. Before first use, tell the user:

"Open a separate terminal and run: notebooklm login This will open a browser for Google authentication. Come back when it completes."

This must happen before any --notebooklm flag will work.

Workflow

User gives topic (or Claude asks)
     ↓
[1] YouTube Research (yt-dlp) — metadata: title, views, channel, duration, URL
[2] Web Research (Brave → Scrapling fallback) — title, URL, description, quality score
[3] Reddit Research (optional flag) — top posts from security subreddits  
     ↓
Quality Filter (min score 5/10 default, configurable)
Deduplication
     ↓
[4] NotebookLM Push — all URLs → new notebook → Phoenix prompt → analysis
     ↓
Summary: sources ingested, notebook URL, analysis preview

Claude Behaviour

When a user gives a research command:

Extract topic — if none given, ask: "What topic do you want to research?"

Run pipeline using bash_tool:

cd /path/to/skill && python3 scripts/research_pipeline.py "TOPIC" \
  --count 25 --notebooklm --prompt blog

Check auth — if NotebookLM fails with auth error, show login instruction
Report results — paste the summary output; include notebook URL
Offer next steps — "Want slides or a video script version? Re-run with --prompt slides"

Read the full file on GitHub · 242 lines

Files

What ships with it

8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

yesterday First seen · 242 lines · 173 tokens per session scan A b05109c18a34

Subscribe to this mod's changes

phoenix-research-pipeline is a skill published in the GitHub repository Security-Phoenix-demo/security-skills-claude-code (70 stars, last pushed yesterday), licensed MIT. It adds 173 tokens to every session and 2,072 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-11.

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