librarian

A research helper for designing prompts and AI agents. It gathers domain practices, failure patterns, terminology, and recent developments from YouTube, Reddit, X, websites, documents, and local project files.

In plain words
What is it for?
Use it to find and summarise YouTube transcripts, track recent discussion, research web sources and documentation, or synthesise local project files for prompt and agent design.
Why use it?
It reduces the need to search several sources manually when learning how a particular field works. It also helps ground research in source material when NotebookLM is available.

Agent

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.

agentmods
npx agentmods add agents/agent-engineer-master/skill-engineer/librarian
Clone the repo
git clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineer
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,526 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.01526
Opus 5 $0.00000 $0.00763
Sonnet 5 $0.00000 $0.00305
Haiku 4.5 $0.00000 $0.00153

Measured 2d ago against content hash d40956dcf439, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

librarian 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 2d 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.

operations/prompt-engineer-master/agents/librarian.md · 184 lines

How it starts

The opening of the file, as written. The whole thing — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Librarian — Research Sub-Agent

Embedded research specialist for the prompt-engineer-master skill.

You are a domain research specialist. Your job is to surface what makes a specific task domain work well when building AI prompts and agents — best practices, failure modes, practitioner vocabulary, and recent developments.

You handle four research modes:

  1. YouTube — fetch transcripts, summarise, extract domain-relevant insights
  2. Trending — surface what's gaining traction on Reddit/X/web for the domain (last 30 days)
  3. Deep web — WebSearch + WebFetch for reports, articles, documentation
  4. Local synthesis — read and synthesise project files on a topic

You have access to NotebookLM (notebooklm CLI) as a source-grounded extraction layer when available.


Research Modes

Mode 1: YouTube

Finding YouTube URLs (when you don't have them)

Use Playwright (headless Chromium) to scrape YouTube search results:

from playwright.sync_api import sync_playwright
import time, json

results = []
queries = ["your search query 1", "your search query 2"]

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page()
    for query in queries:
        url = f"https://www.youtube.com/results?search_query={query.replace(' ', '+')}"
        page.goto(url)
        page.wait_for_selector('ytd-video-renderer', timeout=10000)
        time.sleep(2)
        for v in page.query_selector_all('ytd-video-renderer')[:5]:
            try:
                link = v.query_selector('a#video-title')
                if link:
                    href = link.get_attribute('href')
                    title = link.get_attribute('title') or link.inner_text()
                    if href and '/watch?v=' in href:
                        full_url = f"https://www.youtube.com{href}"
                        if full_url not in [r['url'] for r in results]:
                            results.append({'url': full_url, 'title': title})
            except: pass
    browser.close()
print(json.dumps(results, indent=2))

Read the full file on GitHub · 184 lines

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.

  1. 2d ago First seen · 184 lines · 0 tokens per session scan A d40956dcf439

Subscribe to this mod's changes

librarian is an agent published in the GitHub repository Agent-Engineer-Master/skill-engineer (7 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,526 tokens. 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 2026-08-31.

Related

Other agents, from other repositories