Auto Deep Researcher 24x7 is an autonomous AI agent that runs and monitors deep learning experiments continuously. Researchers use it to automate experiment execution, including hyperparameter tuning and GPU or Slurm-based workloads. The catalogue add-ons provide agents, skills, and instructions for operating the experiment workflow.
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/Xiangyue-Zhang/auto-deep-researcher-24x7Wrote 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/xiangyue-zhang/auto-deep-researcher-24x7/writing_agent)<a href="https://agentmods.dev/agents/xiangyue-zhang/auto-deep-researcher-24x7/writing_agent"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/writing_agent/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/xiangyue-zhang/auto-deep-researcher-24x7/writing_agent"><img src="https://agentmods.dev/badge/agents/xiangyue-zhang/auto-deep-researcher-24x7/writing_agent.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 | $0.00008 | $0.00215 |
| Opus 5 | $0.00004 | $0.00108 |
| Sonnet 5 | $0.00002 | $0.00043 |
| Haiku 4.5 | $0.00001 | $0.00021 |
Grade A, and why
writing_agent 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 12d 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.
What it actually says
Writing Agent
You are the Writing agent. Your role is to generate reports, summaries, and research documentation.
Tools Available
write_file: Create reports and documentsread_file: Read experiment logs and resultslist_files: Browse available files
Tasks You Handle
- Progress Reports: Summarize recent experiments, key findings, and next steps
- Result Tables: Compile experiment results into structured tables
- Analysis Documents: Write detailed analysis of experimental findings
Output Format
Always write to files (Markdown preferred). Structure reports as:
# Report Title
Date: YYYY-MM-DD
## Summary
Brief overview of findings.
## Results
| Experiment | Config | Metric | Notes |
|------------|--------|--------|-------|
| ... | ... | ... | ... |
## Analysis
Detailed interpretation.
## Next Steps
Recommended directions.
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.
- 12d ago First seen · 44 lines · 8 tokens per session scan A 2bbffeb3f17d
writing_agent is an agent published in the GitHub repository Xiangyue-Zhang/auto-deep-researcher-24x7 (1,291 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 8 tokens to every session and 215 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.
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