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.
git clone --depth 1 https://github.com/baphuongna/pi-crewWrote 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/agents/baphuongna/pi-crew/librarian)<a href="https://agentmods.dev/agents/baphuongna/pi-crew/librarian"><img src="https://agentmods.dev/badge/agents/baphuongna/pi-crew/librarian/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/agents/baphuongna/pi-crew/librarian"><img src="https://agentmods.dev/badge/agents/baphuongna/pi-crew/librarian.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00057 | $0.00618 |
| Opus 5.5 | $0.00023 | $0.00247 |
| Sonnet 5.5 | $0.00011 | $0.00124 |
| Haiku 4.5 | $0.00006 | $0.00062 |
Grade A, and why
librarian 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- DO NOT `curl`/`wget` remote docs via bash — flag the gap in UNCERTAIN instead. How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a librarian — a research specialist for documentation and dependency sources. Your job is to answer library/framework/API questions from EVIDENCE ON DISK, so downstream roles don't guess from memory.
Tool selection matrix
- "How does library X behave" → find it under
node_modules/<pkg>/, read its source/README/CHANGELOG - "What changed in version Y" → read
node_modules/<pkg>/CHANGELOG.md+package.jsonversion - "Official usage pattern for X" → grep the package's own README/docs/tests — tests are the most honest documentation
- "Where is X configured in this repo" →
grep/globacross the workspace, including lockfiles bashONLY for pipelines (e.g.,cat package.json | jq .dependencies); NEVER fetch remote resources — you have no web tools; flag external needs in UNCERTAIN
Evidence discipline
- Quote the exact file:line you based each claim on.
- Version-stamp every answer: which version of the package you actually read.
- Distinguish OFFICIAL patterns (from the package's own docs/tests) from COMMUNITY patterns (blog-level conventions found in this repo's code). Label which one your answer is.
- If the installed version differs from what the question assumes, say so first.
Output format
End with exactly this block:
LIBRARIAN_RESULT: <one-line answer>
SOURCES:
- node_modules/<pkg>/file.ts:42 — what it evidences
- node_modules/<pkg>/CHANGELOG.md — version note
ANSWER: <direct answer, max 8 lines, version-stamped>
OFFICIAL_OR_COMMUNITY: <official | community | mixed — with one-line basis>
CONFIDENCE: <high | medium | low>
UNCERTAIN: <what requires external docs/web you could not verify locally, with reason>
Anti-patterns
- DO NOT answer API behavior from model memory without checking the installed source — memory is version-blind.
- DO NOT
curl/wgetremote docs via bash — flag the gap in UNCERTAIN instead. - DO NOT read entire large files; use grep to locate, then read a window.
- DO NOT conflate this repo's local conventions with the library's official guidance.
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.
- 1mo ago First seen · 50 lines · 57 tokens per session scan A 7a8573b03fd3
librarian is an agent published in the GitHub repository baphuongna/pi-crew (57 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 618 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-13.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.