A command for researching scientific papers in three stages: finding candidate papers, obtaining their documents, and combining their findings into an answer. PaperQA is the tool used for the final evidence-based synthesis.

Use it to investigate a biomedical or scientific question across multiple papers and produce a combined literature summary.

It keeps paper discovery, document retrieval, and answer writing in the required order, while handling cases where documents cannot be obtained.

How omar-A-hassan/medsci-agent configures Claude Code and OpenCode

Reuse

Borrowing it

Nothing to install: this file belongs to omar-A-hassan/medsci-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/omar-A-hassan/medsci-agent/main/.opencode/commands/lit-deep.md

Clone the repo

git clone --depth 1 https://github.com/omar-A-hassan/medsci-agent

Made for: Claude Code, OpenCode.

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/commands/omar-a-hassan/medsci-agent/lit-deep/github.svg)](https://agentmods.dev/commands/omar-a-hassan/medsci-agent/lit-deep)

Your own site

<a href="https://agentmods.dev/commands/omar-a-hassan/medsci-agent/lit-deep"><img src="https://agentmods.dev/badge/commands/omar-a-hassan/medsci-agent/lit-deep/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/commands/omar-a-hassan/medsci-agent/lit-deep"><img src="https://agentmods.dev/badge/commands/omar-a-hassan/medsci-agent/lit-deep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 15 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 172 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. 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	$0.00015	$0.00172
Opus 5	$0.00008	$0.00086
Sonnet 5	$0.00003	$0.00034
Haiku 4.5	$0.00002	$0.00017

Measured 9d ago against content hash 29fcf2536f6b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

lit-deep 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 9d 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.

.opencode/commands/lit-deep.md · 19 lines

What it actually says

Execute a three-phase literature workflow for the question below.

Question: $ARGUMENTS

Requirements:

Phase 1 discovery using literature tools first.
Use needs_synthesized_summary=false during discovery.
Collect top candidate papers with DOI/PMID metadata.
Phase 2 acquisition via medsci-acquisition (resolve_identifier_to_sources, acquire_documents) and keep content_level metadata.
Phase 3 deep synthesis with PaperQA using acquired documents when available; fall back to papers identifiers only when acquisition fails.
Respect strict limit: never pass more than 10 papers per PaperQA call.
If more than 10, batch sequentially and provide merged synthesis.

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.

9d ago First seen · 19 lines · 15 tokens per session scan A 29fcf2536f6b

Subscribe to this mod's changes

lit-deep is a command published in the GitHub repository omar-A-hassan/medsci-agent (18 stars, last pushed 2d ago), licensed MIT. It adds 15 tokens to every session and 172 once invoked, about $0.0001 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.

Research Scientific agents mcp medgemma ollama opencode txgemma

medsci-agent: Command for Claude Code

Borrowing it

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

What it actually says

What this file has done since we first saw it

Other commands, from other repositories

planning

analysis

astro-dso-doc

checklist

clarify

specify