Borrowing it
Nothing to install: this file belongs to guzus/ai-research-arm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/guzus/ai-research-arm/main/.claude/commands/gen-research.mdgit clone --depth 1 https://github.com/guzus/ai-research-armWrote 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/commands/guzus/ai-research-arm/gen-research)<a href="https://agentmods.dev/commands/guzus/ai-research-arm/gen-research"><img src="https://agentmods.dev/badge/commands/guzus/ai-research-arm/gen-research/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/commands/guzus/ai-research-arm/gen-research"><img src="https://agentmods.dev/badge/commands/guzus/ai-research-arm/gen-research.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 | $0.00051 | $0.04896 |
| Opus 5 | $0.00026 | $0.02448 |
| Sonnet 5 | $0.00010 | $0.00979 |
| Haiku 4.5 | $0.00005 | $0.00490 |
Grade A, and why
gen-research 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 10d 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.
- `curl -sL <url> -o /tmp/x.pdf && pdftotext /tmp/x.pdf - | head -c 60000` How it starts
The opening of the file, as written. The whole thing — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generative Research
User-supplied topic: $ARGUMENTS
If the topic above is empty or just whitespace, ask the user for a topic and stop until they answer.
Step 1 — choose execution mode (always ask)
Use AskUserQuestion to ask the user how they want this run
executed. DO NOT skip this prompt — the two modes have very
different cost, latency, and depth profiles. Present these two
options:
- Run locally (in this chat) — ~5–15 min. Deep pipeline runs
in the current session using sub-agents (Task tool), evidence
packets, and a verifier pass. Article lands as a local commit
in
research/generative/(not pushed). Recommended for topics you want to iterate on, or when you want to read the result immediately. - Route to GitHub workflow — ~20–45 min on the self-hosted
runner. Dispatches
generative-research.ymlwithbackend=deepseek-v4-flashby default. Your chat is freed instantly. The runner produces a longer article (4500–7000 words target, ≥20 cited references) and pushes the commit directly tomain. Recommended for substantive topics where you want maximum depth without spending in-chat time.
After the user picks:
- Route → follow PATH A below.
- Run locally → follow PATH B below.
PATH A — Route to the GitHub workflow
Dispatch the workflow with the user's topic. Use deepseek-v4-flash as
the backend unless the user explicitly asked for Claude in their brief.
gh workflow run generative-research.yml \
-f topic="<the exact topic the user gave>" \
-f backend=deepseek-v4-flash
Then wait briefly for the run to register and surface its URL:
sleep 5
gh run list --workflow=generative-research.yml --limit 1 \
--json databaseId,status,url,createdAt,event \
--jq '.[0]'
Report to the user in one short message:
- "Dispatched to GitHub workflow."
- The run URL.
- The command to watch live:
gh run watch <id>. - Expected runtime (~20–45 min) and the destination
(
research/generative/). The runner pushes the commit, so the article shows up at https://ara.guzus.xyz/research/ after the next Vercel deploy.
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.
- 10d ago First seen · 441 lines · 51 tokens per session scan A 0595240ee5c4
gen-research is a command published in the GitHub repository guzus/ai-research-arm (11 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 4,896 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
Other commands, from other repositories
start-11-3.en
Lesson 11-3: News Fetching → Email/Slack Distribution!
start-11-3.es
Leccion 11-3: Obtencion de noticias y distribucion por correo/Slack!
start-11-3
A lesson on building a workflow that fetches news from an RSS feed and distributes it by email or Slack. An RSS feed is a regularly updated list of items from a website.
pi-code-review
Adversarial code review via Codex — break confidence in changes, not validate them.
pi-fact-check
Fact-check content via Gemini search + Claude verification — cross-provider claim validation.
pi-askall
Ask both Codex and Gemini the same question — get three perspectives with Claude synthesis.