Deep-Research-skills vs paperjury
The two are close in size: 2.1k stars for Deep-Research-skills, 1.1k for paperjury. Over the days we have tracked them Deep-Research-skills moved +107% and paperjury +31%, so Deep-Research-skills is growing faster right now.
paperjury leads on context depth and noise control. Deep-Research-skills does not take any axis by a clear margin.
Stars and commit dates come from our own daily tracking. The six axes are read off each project's documentation by our review pipeline, so they describe what a project says about itself, not what we measured in its code.
Where they stand today
Provides a structured two-phase (outline then deep investigation) human-in-the-loop research workflow packaged specifically as skills for Claude Code / OpenCode / Codex, including parallel web research modules and report generation.
- Stars
- 2.1k
- Tracked growth
- +107%
- Maturity
- ●●●●●
- Last commit
- 13d ago
- Language
- Python
- License
- MIT
- Cost to run
- Depends on model provider
Bundles a bounded, audit-ready review→decide→patch→recheck loop with ledgered issues and edit-safety safeguards, exposed as a Claude Code skill.
- Stars
- 1.1k
- Tracked growth
- +31%
- Maturity
- ●●●●●
- Last commit
- 22d ago
- Language
- JavaScript
- License
- MIT
- Cost to run
- Runs in your Claude session — model usage billed by the provider
Six axes, head to head
Each axis runs 0 to 5. The label under a score is what that project's own docs claim, not a category average.
| Axis | Deep-Research-skills | paperjury |
|---|---|---|
Context depth How much of your codebase it sees before it answers: the open diff, the diff plus related files, or the whole repository. | ●●●●● Web-only | ●●●●● Whole-repo analysis |
Noise control How it keeps output volume down — severity thresholds, deduplication, incremental runs over new commits only. | ●●●●● Human-in-the-loop | ●●●●● Severity gating & dedup |
Customization How far it bends to your team: custom rules, prompts, style guides, per-path config. | ●●●●● Config file options | ●●●●● Modes & personas |
Privacy Whether your code stays on your own infrastructure: fully local, self-hostable, or cloud API only. | ●●●●● Cloud APIs | ●●●●● Cloud LLM via session |
Model freedom Whether you can point it at any provider, or it is wired to one. | ●●●●● Specific providers | ●●●●● Claude-first (fixed) |
Setup ease What it takes to get a first useful run out of it. | ●●●●● Manual install steps | ●●●●● Plugin install + session |
Which one to pick
Pick Deep-Research-skills if…
Claude/OpenCode/Codex integration — choose this when you run those agent clients and want an extensible, human-in-the-loop two-phase workflow (outline + deep web research) that outputs organized markdown reports.
Pick paperjury if…
Audit-ready review loop — pick PaperJury when you want a pre-submission, multi-round reviewer simulation that produces minimal, author-confirmed LaTeX patches and deterministic compile/compliance checks.
- Context depth: Whole-repo analysis (5/5 against 1/5)
- Noise control: Severity gating & dedup (5/5 against 3/5)
What people want from each one
u7079256/paperjury
Questions people ask
Is Deep-Research-skills better than paperjury?
paperjury leads on context depth and noise control. Deep-Research-skills does not take any axis by a clear margin. Deep-Research-skills is worth picking when claude/OpenCode/Codex integration — choose this when you run those agent clients and want an extensible, human-in-the-loop two-phase workflow (outline + deep web research) that outputs organized markdown reports.
Which of Deep-Research-skills and paperjury keeps my code private?
Deep-Research-skills: Cloud APIs (3/5). paperjury: Cloud LLM via session (3/5).
What does each one cost to run?
Deep-Research-skills: Depends on model provider. paperjury: Runs in your Claude session — model usage billed by the provider.
Full profiles: Weizhena/Deep-Research-skills and u7079256/paperjury. Everything else in Research agents.