research-depth-control

research-depth-control is a skill for Claude Code, Codex from BingHanOfUESTC/open_agent_team. It costs 44 tokens per session (891 once invoked), scanned A, original, MIT.

A research-quality checkpoint that requires broader and deeper coverage of academic literature before planning experiments or writing a paper. It sets targets for papers, recent work, limitations, code, data, and benchmarks, then records how searches were performed.

In plain words
What is it for?
Checking literature coverage, finding related work and negative results, tracking search decisions, and preparing research for experiments or publication.
Why use it?
It reduces the risk of basing ideas or conclusions on a shallow or incomplete review of existing research.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Checking literature coverage, finding related work and negative results, tracking search decisions, and preparing research for experiments or publication.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/binghanofuestc/open_agent_team/research-depth-control
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.

Any agent
npx skills add BingHanOfUESTC/open_agent_team --skill research-depth-control
Clone the repo
git clone --depth 1 https://github.com/BingHanOfUESTC/open_agent_team

Made for: Claude Code, Codex.

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 research-depth-control

README.md
[![agentmods](https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/research-depth-control.svg)](https://agentmods.dev/skills/binghanofuestc/open_agent_team/research-depth-control)
Your own site
<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>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 891 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00044 $0.00891
Opus 5 $0.00022 $0.00445
Sonnet 5 $0.00009 $0.00178
Haiku 4.5 $0.00004 $0.00089

Measured 8d ago against content hash 7e3f90f68a67, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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.

teams/auto_research_team/skills/research-depth-control/SKILL.md · 154 lines

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.


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

Read the full file on GitHub · 154 lines

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. 8d ago First seen · 154 lines · 44 tokens per session scan A 7e3f90f68a67

Subscribe to this mod's changes

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.

Related

Other skills, from other repositories