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.
npx skills add BingHanOfUESTC/open_agent_team --skill research-depth-controlgit clone --depth 1 https://github.com/BingHanOfUESTC/open_agent_teamWrote 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/skills/binghanofuestc/open_agent_team/research-depth-control)<a href="https://agentmods.dev/skills/binghanofuestc/open_agent_team/research-depth-control"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/research-depth-control.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.00891 |
| Opus 5 | $0.00022 | $0.00445 |
| Sonnet 5 | $0.00009 | $0.00178 |
| Haiku 4.5 | $0.00004 | $0.00089 |
Grade A, and why
research-depth-control 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Depth Control
This skill raises the research-depth floor for auto research tasks. Use it together with literature-evidence-mapping before ideation, before experiment planning, and again before final paper writing.
1. Depth Targets
Default targets unless Boss narrows the scope:
candidate papers: 30-60
included papers: 15-30
deep-read papers: 8-12
baseline/source papers: 3-6
recent papers: at least 5 from the last 24 months when the field is active
negative/limitation papers: at least 3
code/data/benchmark sources: at least 5 combined
For small or niche fields, record why the target cannot be met and list the exact searches that were exhausted.
2. Four-Pass Search
Run and log four distinct passes:
breadth pass: broad field terms, surveys, benchmark papers, classic baselines
depth pass: citation chasing from top papers, authors, labs, official repos
gap pass: limitations, failure modes, robustness, negative results, open problems
recency pass: latest 6-24 month papers from arXiv/OpenReview/venue pages/Papers With Code
Each pass must record:
query
source/index
date searched
number of hits screened
inclusion/exclusion rationale
new papers added
Do not stop after a single search engine or a single query phrasing.
3. Source Stratification
The final paper set should cover these buckets where applicable:
foundational work
strong baseline methods
most recent frontier methods
benchmark/dataset/evaluation papers
reproducibility or implementation sources
failure/limitation/negative evidence
adjacent methods that solve a similar bottleneck
If one bucket is empty, explain whether it is irrelevant or still an evidence gap.
4. Evidence Density Gate
Before idea selection, require:
at least 15 included papers or a documented niche-field exception
at least 8 deep-read evidence cards
at least 3 papers tied to the chosen benchmark/dataset
at least 3 explicit limitations or open problems cited from papers
at least 2 reusable baselines or implementation references
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.
- 8d ago First seen · 154 lines · 44 tokens per session scan A 7e3f90f68a67
research-depth-control is a skill published in the GitHub repository BingHanOfUESTC/open_agent_team (109 stars, last pushed 2mo ago), licensed MIT. It adds 44 tokens to every session and 891 once invoked, about $0.0002 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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