Hugging Face Paper Pages
huggingface/skills
Fetches Hugging Face paper pages as markdown and reads paper metadata through the papers API when you share a paper URL, an arXiv link or an arXiv ID.
A skill your agent uses when the user provides a paper, arXiv link, technical report, model card, checkpoint name, GitHub repository, or local codebase and asks to research, explain, compare, or…
Install Phyai Model Arch Research
$ npx skills add mingti-org/phyai --skill phyai-model-arch-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mingti-org/phyai phyai-model-arch-research --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mingti-org/phyai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/phyai-model-arch-research .claude/skills/phyai-model-arch-research && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "phyai-model-arch-research" agent skill from https://github.com/mingti-org/phyai/tree/main/.claude/skills/phyai-model-arch-research into .claude/skills/phyai-model-arch-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phyai-model-arch-research", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mingti-org/phyai/tree/main/.claude/skills/phyai-model-arch-researchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mingti-org/phyai --skill phyai-model-arch-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mingti-org/phyai phyai-model-arch-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mingti-org/phyai.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/phyai-model-arch-research .agents/skills/phyai-model-arch-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "phyai-model-arch-research" agent skill from https://github.com/mingti-org/phyai/tree/main/.claude/skills/phyai-model-arch-research into .agents/skills/phyai-model-arch-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phyai-model-arch-research", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mingti-org/phyai --skill phyai-model-arch-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mingti-org/phyai phyai-model-arch-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mingti-org/phyai.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/phyai-model-arch-research .cursor/skills/phyai-model-arch-research && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "phyai-model-arch-research" agent skill from https://github.com/mingti-org/phyai/tree/main/.claude/skills/phyai-model-arch-research into .cursor/skills/phyai-model-arch-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phyai-model-arch-research", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mingti-org/phyai.git --path .claude/skills/phyai-model-arch-research--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mingti-org/phyai --skill phyai-model-arch-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mingti-org/phyai phyai-model-arch-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mingti-org/phyai.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/phyai-model-arch-research .gemini/skills/phyai-model-arch-research && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "phyai-model-arch-research" agent skill from https://github.com/mingti-org/phyai/tree/main/.claude/skills/phyai-model-arch-research into .gemini/skills/phyai-model-arch-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phyai-model-arch-research", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mingti-org/phyai phyai-model-arch-researchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mingti-org/phyai --skill phyai-model-arch-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mingti-org/phyai.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/phyai-model-arch-research .github/skills/phyai-model-arch-research && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "phyai-model-arch-research" agent skill from https://github.com/mingti-org/phyai/tree/main/.claude/skills/phyai-model-arch-research into .github/skills/phyai-model-arch-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phyai-model-arch-research", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mingti-org/phyai --skill phyai-model-arch-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mingti-org/phyai phyai-model-arch-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mingti-org/phyai.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/phyai-model-arch-research .opencode/skills/phyai-model-arch-research && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "phyai-model-arch-research" agent skill from https://github.com/mingti-org/phyai/tree/main/.claude/skills/phyai-model-arch-research into .opencode/skills/phyai-model-arch-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "phyai-model-arch-research", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
phyai-model-arch-research- GitHub stars
- 130
- Token cost
- ~1.8k tokens
- SKILL.md length
- 740 words
- Files
- 1
- Skills in repo
- 5
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when the user provides a paper, arXiv link, technical report, model card, checkpoint name, GitHub repository, or local codebase and asks to research, explain, compare, or…
- Works in 5 steps: Clarify the Target → Collect Architecture Evidence → Build the Architecture Map → …
- The user provides a paper
- SKILL.md covers Research Goals, Source Priority, Workflow and Code Mapping Rules, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Phyai Model Arch Research is an agent skill from mingti-org/phyai. Use this skill when the user provides a paper, arXiv link, technical report, model card, checkpoint name, GitHub repository, or local codebase and asks to research, explain, compare, or implement a model architecture. This skill guides architecture research for PHYAI: source collection, paper/code tracing, module decomposition, tensor shape analysis, dependency mapping, and implementation-oriented reporting.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Model hubs and datasets and Academic paper search. It works with GitHub and arXiv. The repository describes itself as: PhyAI is a high-performance framework for running Physical AI models (VLA, WAM, and beyond), supporting both cloud-based serving and on-device deployment. The licence is MIT.
When your agent uses it
- The user provides a paper
- Technical report
- Checkpoint name
- GitHub repository
Example prompts
- “/phyai-model-arch-research”
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 36a46bf. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Phyai Model Arch Research loads about 1.8k tokens when it runs. Until then it costs about 109 tokens; SKILL.md has 740 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from mingti-org/phyai at commit 36a46bf, republished under its MIT licence (© mingti-org). 740 words, ~1,832 tokens.
Download SKILL.mdSave it as
.claude/skills/phyai-model-arch-research/SKILL.md (or your agent's skills folder).- name
- phyai-model-arch-research
- description
- Use this skill when the user provides a paper, arXiv link, technical report, model card, checkpoint name, GitHub repository, or local codebase and asks to research, explain, compare, or implement a model architecture. This skill guides architecture research for PHYAI: source collection, paper/code tracing, module decomposition, tensor shape analysis, dependency mapping, and implementation-oriented reporting.
PHYAI Model Architecture Research
Use this skill to study a model architecture from a paper, repository, or both, then produce an implementation-oriented architecture report. Optimize for facts that help PHYAI model support, kernel work, quantization, profiling, and deployment.
Research Goals
Answer these questions before proposing implementation work:
- What model family is this, and what prior architectures does it inherit from?
- What are the major modules, data flow, tensor shapes, and configurable hyperparameters?
- Which parts are standard, and which parts are novel or likely to need custom code?
- Where is the authoritative implementation: paper equations, official repo, model config, or checkpoint metadata?
- What are the likely integration risks for PHYAI: custom ops, attention variants, positional encoding, MoE routing, normalization, multimodal preprocessing, cache layout, dtype, quantization, and generation behavior?
Source Priority
Prefer sources in this order:
- Official code repository, release branch, or tagged commit.
- Paper or technical report, especially architecture figures, tables, appendices, and ablations.
- Model card, config files, tokenizer/preprocessor files, checkpoint metadata.
- Reputable secondary explanations only when primary sources are missing or unclear.
When sources disagree, state the conflict and prefer executable/configured behavior over prose unless the user asks for a paper-only summary.
Workflow
1. Clarify the Target
Identify:
- model name and version;
- paper URL, repo URL, local repo path, checkpoint path, or package name;
- desired output depth: quick summary, implementation plan, code mapping, comparison, or full report.
If the user's paper, article, link, repository, checkpoint, or model name is missing, inaccessible, ambiguous, or could refer to multiple targets, ask the user a concise follow-up question before doing architecture research. Do not guess the target architecture when the source is unclear.
If the user gives a recognizable but incomplete name, first state the likely interpretation and ask for confirmation or a source link. Only search for official sources after the target is clear enough to avoid researching the wrong model.
2. Collect Architecture Evidence
For papers, inspect:
- abstract and introduction for the claimed contribution;
- architecture section, equations, diagrams, and algorithm blocks;
- model size/config tables;
- training/inference details that affect architecture behavior;
- appendix for hidden implementation details.
For code repositories, inspect:
README, model cards, docs, and config examples;- model definition files such as
modeling_*.py,configuration_*.py,model.py,modules.py,layers.py; - preprocessing/tokenizer/processor code for multimodal models;
- generation, cache, sampling, and inference utilities;
- custom kernels, CUDA/Triton ops, fused layers, or extension bindings;
- tests and examples that show expected shapes and behavior.
Use rg first for local code search. Useful patterns:
class .*Model|class .*For|forward\(|attention|rotary|rope|norm|mlp|moe|expert|cache|kv|vision|encoder|decoder|embed|projectorShow full SKILL.md (333 more words)Show less
3. Build the Architecture Map
Document the model in layers:
- input pipeline: tokenizer, image/audio/video preprocessing, patching, feature extraction;
- embedding path: token/patch embeddings, special tokens, position encoding;
- backbone blocks: attention, MLP, normalization, residual layout, routing, recurrence/state-space components;
- cross-modal or adapter components: projector, connector, resampler, cross-attention;
- output heads: LM head, classification head, diffusion head, value head, decoder head;
- inference state: KV cache, recurrent state, streaming buffers, masks, packed sequences;
- configuration knobs: depth, hidden size, heads, head dim, intermediate size, experts, vocab, context length, precision.
Track tensor shapes at module boundaries when possible. Use symbolic names like:
B = batch size
T = sequence length
H = hidden size
N = number of heads
D = head dim
V = vocab size4. Compare Against Known Patterns
Call out whether the model resembles:
- LLaMA/Qwen/Mistral/GPT-style decoder-only transformers;
- encoder-decoder transformers;
- ViT/SigLIP/CLIP-style vision encoders;
- multimodal LLMs such as LLaVA/Qwen-VL/InternVL-style projector stacks;
- MoE models with router/top-k experts;
- diffusion/DiT/U-Net architectures;
- state-space or hybrid attention models;
- custom research architecture not covered by common model families.
The comparison should explain implementation consequences, not just naming similarity.
5. Produce a Report
Use this structure unless the user asks for a different format:
## Executive Summary
- model family:
- core idea:
- implementation difficulty:
- main integration risks:
## Sources Checked
- paper:
- repo/code:
- configs/checkpoints:
## Architecture
Describe the full data flow from input to output.
## Module Breakdown
| Module | Purpose | Key config | Shape notes | Code location |
| --- | --- | --- | --- | --- |
## Novel or Nonstandard Parts
List components that may require custom implementation, kernels, or careful validation.
## PHYAI Integration Notes
Discuss model loading, config mapping, tokenizer/processor, kernels, cache, quantization, tests, and performance risks.
## Open Questions
List missing facts, ambiguous source conflicts, or items needing a run/checkpoint.Code Mapping Rules
When analyzing a repository:
- link architecture claims to exact file paths and functions/classes;
- distinguish public API wrappers from the actual implementation;
- check config defaults instead of assuming paper hyperparameters;
- trace
forward()through helper modules until the real tensor operations are clear; - inspect tests/examples to confirm expected behavior;
- avoid large refactors or code changes unless the user explicitly asks for implementation.
Paper Reading Rules
When analyzing a paper:
- do not summarize every section equally; focus on architecture and implementation facts;
- extract equations only when they define behavior needed for implementation;
- note missing details that must be recovered from code or configs;
- separate the authors' claims from confirmed architecture mechanics.
Validation Checklist
Before finalizing, check whether the report covers:
- source authority and version;
- full input-output path;
- module list and config knobs;
- tensor shape assumptions;
- attention/cache behavior;
- normalization, activation, positional encoding;
- custom ops/kernels or dependencies;
- checkpoint/config compatibility;
- minimal tests needed for PHYAI support.
If a fact is inferred rather than directly sourced, label it as an inference.
© mingti-org, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .claude/skills/phyai-model-arch-research of mingti-org/phyai.
Open the folder on GitHubat commit 36a46bf
Compare with similar skills
Phyai Model Arch Research next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Phyai Model Arch Research this skillmingti-org/phyai | 130 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Hugging Face Paper Pageshuggingface/skills | 11k | 3 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| Ideer Daily PaperAI45Lab/iDeer | 416 | — | ~2.3k | Automated safety check: Notes | AGPL-3.0 | |
| Morning AIdavepoon/buildwithclaude | 3.6k | — | ~405 | Automated safety check: Pass | MIT | |
| News Aggregator Skillcclank/news-aggregator-skill | 1.3k | — | ~2.1k | Automated safety check: Pass | None | |
| Hugging Face Paper Publisherhuggingface/skills | 11k | 4 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 |
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Categories
Questions about Phyai Model Arch Research
What does Phyai Model Arch Research do?
A skill your agent uses when the user provides a paper, arXiv link, technical report, model card, checkpoint name, GitHub repository, or local codebase and asks to research, explain, compare, or…. Phyai Model Arch Research is an agent skill from mingti-org/phyai. Use this skill when the user provides a paper, arXiv link, technical report, model card, checkpoint name, GitHub repository, or local codebase and asks to research, explain, compare, or implement a model architecture.
When should I use Phyai Model Arch Research?
Phyai Model Arch Research fits situations like: the user provides a paper; technical report; checkpoint name; GitHub repository.
How do I install Phyai Model Arch Research in Claude Code?
Run `npx skills add mingti-org/phyai --skill phyai-model-arch-research -a claude-code`. Or copy the skill folder (.claude/skills/phyai-model-arch-research in mingti-org/phyai) into .claude/skills/phyai-model-arch-research in your project. Claude Code loads it when a task matches its description.
How do I install Phyai Model Arch Research in Codex?
Run `npx skills add mingti-org/phyai --skill phyai-model-arch-research -a codex`. Or copy the skill folder (.claude/skills/phyai-model-arch-research in mingti-org/phyai) into .agents/skills/phyai-model-arch-research in your project. Codex loads it when a task matches its description.
Can I use Phyai Model Arch Research in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mingti-org/phyai --skill phyai-model-arch-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/phyai-model-arch-research, .gemini/skills/phyai-model-arch-research, .github/skills/phyai-model-arch-research and .opencode/skills/phyai-model-arch-research in your project.
What does Phyai Model Arch Research need to run?
SKILL.md names no scripts, command-line tools or credentials: Phyai Model Arch Research is instructions for the agent only.
Does Phyai Model Arch Research access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Phyai Model Arch Research safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Phyai Model Arch Research use?
Phyai Model Arch Research is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Phyai Model Arch Research use?
About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Phyai Model Arch Research?
Skills that share tags, products or a category with Phyai Model Arch Research: Hugging Face Paper Pages (huggingface/skills, 11k stars), Ideer Daily Paper (AI45Lab/iDeer, 416 stars), Morning AI (davepoon/buildwithclaude, 3.6k stars) and News Aggregator Skill (cclank/news-aggregator-skill, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Phyai Model Arch Research?
mingti-org (a GitHub organization) maintains it in mingti-org/phyai, which has 130 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 6, 2026.
Source: mingti-org/phyai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.