run-rq

run-rq is a command for coding agents from marcoemrich/agentic_coding_lab. It costs 7 tokens per session (40 once invoked), scanned A, original, MIT.

A command that runs a research question from start to finish using a separate run-rq skill.

In plain words
What is it for?
It is for starting an end-to-end research task identified by an RQ number.
Why use it?
It gives a repeatable way to carry out the full research-question process instead of running each part manually.

Command

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 commands/marcoemrich/agentic_coding_lab/run-rq
Clone the repo
git clone --depth 1 https://github.com/marcoemrich/agentic_coding_lab

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.

agentmods badge for run-rq

README.md
[![agentmods](https://agentmods.dev/badge/commands/marcoemrich/agentic_coding_lab/run-rq.svg)](https://agentmods.dev/commands/marcoemrich/agentic_coding_lab/run-rq)
Your own site
<a href="https://agentmods.dev/commands/marcoemrich/agentic_coding_lab/run-rq"><img src="https://agentmods.dev/badge/commands/marcoemrich/agentic_coding_lab/run-rq.svg" alt="Measured on agentmods" height="20"></a>
Per session 7 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 40 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 $0.00007 $0.00040
Opus 5 $0.00003 $0.00020
Sonnet 5 $0.00001 $0.00008
Haiku 4.5 $0.00001 $0.00004

Measured 3d ago against content hash 21099b049fed, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

run-rq 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 3d 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/run-rq.md · 5 lines

What it actually says

Use the run-rq skill to drive RQ-$ARGUMENTS end-to-end. Load the skill first, then follow its instructions.

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. 3d ago First seen · 5 lines · 7 tokens per session scan A 21099b049fed

Subscribe to this mod's changes

run-rq is a command published in the GitHub repository marcoemrich/agentic_coding_lab (11 stars, last pushed 16d ago), licensed MIT. It adds 7 tokens to every session and 40 once invoked, about $0.0000 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.