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 wentorai/research-plugins --skill domain-adaptation-papers-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/domain-adaptation-papers-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/domain-adaptation-papers-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/domain-adaptation-papers-guide/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/skills/wentorai/research-plugins/domain-adaptation-papers-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/domain-adaptation-papers-guide.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00015 | $0.01547 |
| Opus 5 | $0.00008 | $0.00773 |
| Sonnet 5 | $0.00003 | $0.00309 |
| Haiku 4.5 | $0.00002 | $0.00155 |
Grade A, and why
domain-adaptation-papers-guide 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 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.
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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Adaptation Papers Guide
Overview
Domain adaptation addresses the problem of training models on one data distribution (source domain) and deploying them on a different distribution (target domain). This curated collection covers the full spectrum — from unsupervised domain adaptation (UDA) and domain generalization to partial, open-set, and source-free adaptation. Organized by methodology and application area with regularly updated paper lists.
Taxonomy of Methods
Domain Adaptation
├── Unsupervised DA (UDA)
│ ├── Discrepancy-based (MMD, CORAL, CDD)
│ ├── Adversarial-based (DANN, ADDA, CDAN)
│ ├── Reconstruction-based (DRCN, DSN)
│ └── Self-training (SHOT, CBST)
├── Semi-supervised DA
├── Source-free DA (no source data at adaptation time)
├── Partial DA (target has subset of source classes)
├── Open-set DA (target has unknown classes)
├── Universal DA (no prior on label set relationship)
├── Multi-source DA
├── Domain Generalization (no target data at all)
└── Test-time Adaptation (adapt at inference)
Key Methods by Era
Classical Methods
| Method | Year | Approach | Key Idea |
|---|---|---|---|
| TCA | 2011 | Kernel | Transfer Component Analysis |
| GFK | 2012 | Subspace | Geodesic Flow Kernel |
| SA | 2013 | Subspace | Subspace Alignment |
| DAN | 2015 | MMD | Deep Adaptation Networks |
| DANN | 2016 | Adversarial | Domain-Adversarial Neural Networks |
| ADDA | 2017 | Adversarial | Adversarial Discriminative DA |
| CORAL | 2016 | Statistics | Correlation Alignment |
Modern Methods
| Method | Year | Approach | Key Idea |
|---|---|---|---|
| CDAN | 2018 | Adversarial | Conditional adversarial + entropy |
| MCD | 2018 | Discrepancy | Maximum Classifier Discrepancy |
| SHOT | 2020 | Source-free | Self-supervised pseudo-labeling |
| TENT | 2021 | Test-time | Entropy minimization at test time |
| DAFormer | 2022 | Transformer | DA for semantic segmentation |
| PADCLIP | 2023 | Vision-language | CLIP-based domain adaptation |
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 · 174 lines · 15 tokens per session scan A 000382aa94e3
domain-adaptation-papers-guide is a skill published in the GitHub repository wentorai/research-plugins (290 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 1,547 once invoked, about $0.0001 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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