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.
npx agentmods add agents/agent-engineer-master/skill-engineer/librariangit clone --depth 1 https://github.com/Agent-Engineer-Master/skill-engineerWhat 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 | $0.00000 | $0.01526 |
| Opus 5 | $0.00000 | $0.00763 |
| Sonnet 5 | $0.00000 | $0.00305 |
| Haiku 4.5 | $0.00000 | $0.00153 |
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.
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:
- YouTube — fetch transcripts, summarise, extract domain-relevant insights
- Trending — surface what's gaining traction on Reddit/X/web for the domain (last 30 days)
- Deep web — WebSearch + WebFetch for reports, articles, documentation
- 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))
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.
- 2d ago First seen · 184 lines · 0 tokens per session scan A d40956dcf439
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.
Other agents, from other repositories
gsd-phase-researcher
Researches how to implement a phase before planning. Produces RESEARCH.md consumed by gsd-planner. Spawned by /gsd:plan-phase orchestrator.
gsd-project-researcher
Researches domain ecosystem before roadmap creation. Produces files in .planning/research/ consumed during roadmap creation. Spawned by /gsd:new-project or /gsd:new-milestone orchestrators.
oss-growth-hacker
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apm-primitives-architect
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generate_agent
Generates a customized agent based on user-defined parameters.
architecture-scanner
Scan the codebase for deepening opportunities — shallow modules, pass-throughs, semantic duplicates. Read-only. Produces a visual HTML report with before/after diagrams. Routes: CODEBASE-HEALTH workflow.