Re AI Model
dslsdzc/rev-skills
AI 模型文件逆向与静态分析：ONNX/PyTorch/Safetensors/TFLite 格式解析、 网络结构还原、权重提取、文件级水印分析（权重 pattern/metadata/tensor hash/embedding 异常）。
ProteinBERT 昇腾 NPU 部署与迁移 Skill，适用于将 TensorFlow 或 Keras 版 ProteinBERT 转成基于 PyTorch 与 torchnpu 的实现，覆盖权重转换、embedding 提取、微调训练、注意力可视化和 GPU 与 NPU 精度验证。
Install AI For Science Proteinbert
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-proteinbert --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/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-for-science/models/proteinbert .claude/skills/ai-for-science-proteinbert && 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 "ai-for-science-proteinbert" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/proteinbert into .claude/skills/ai-for-science-proteinbert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-proteinbert", 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/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/proteinbertType 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 ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-proteinbert --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-for-science/models/proteinbert .agents/skills/ai-for-science-proteinbert && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-for-science-proteinbert" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/proteinbert into .agents/skills/ai-for-science-proteinbert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-proteinbert", 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 ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-proteinbert --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-for-science/models/proteinbert .cursor/skills/ai-for-science-proteinbert && 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 "ai-for-science-proteinbert" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/proteinbert into .cursor/skills/ai-for-science-proteinbert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-proteinbert", 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/ascend-ai-coding/awesome-ascend-skills.git --path skills/ai-for-science/models/proteinbert--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 ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-proteinbert --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-for-science/models/proteinbert .gemini/skills/ai-for-science-proteinbert && 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 "ai-for-science-proteinbert" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/proteinbert into .gemini/skills/ai-for-science-proteinbert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-proteinbert", 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 ascend-ai-coding/awesome-ascend-skills ai-for-science-proteinbertInstalls 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 ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-for-science/models/proteinbert .github/skills/ai-for-science-proteinbert && 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 "ai-for-science-proteinbert" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/proteinbert into .github/skills/ai-for-science-proteinbert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-proteinbert", 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 ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-proteinbert --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-for-science/models/proteinbert .opencode/skills/ai-for-science-proteinbert && 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 "ai-for-science-proteinbert" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/proteinbert into .opencode/skills/ai-for-science-proteinbert/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-proteinbert", 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
ai-for-science-proteinbert- GitHub stars
- 174
- Token cost
- ~1.9k tokens
- SKILL.md length
- 306 words
- Files
- 24 (incl. scripts, references)
- Skills in repo
- 70
- Repo updated
- First seen
- Licence
- None found
At a glance
ProteinBERT 昇腾 NPU 部署与迁移 Skill，适用于将 TensorFlow 或 Keras 版 ProteinBERT 转成基于 PyTorch 与 torchnpu 的实现，覆盖权重转换、embedding 提取、微调训练、注意力可视化和 GPU 与 NPU 精度验证。
- Works in 2 steps: K.dot 与 einsum 维度映射错误 → LayerNorm epsilon 默认值差 100 倍
- Tasks that involve Deep learning
- SKILL.md covers 模型概况, 环境准备, 文件结构 and 快速开始, plus 4 more sections
- Runs Python and Shell scripts from its folder; calls python, conda and pip; reaches repo.huaweicloud.com and zenodo.org
What it does
AI For Science Proteinbert is an agent skill from ascend-ai-coding/awesome-ascend-skills. ProteinBERT 昇腾 NPU 部署与迁移 Skill，适用于将 TensorFlow 或 Keras 版 ProteinBERT 转成基于 PyTorch 与 torchnpu 的实现，覆盖权重转换、embedding 提取、微调训练、注意力可视化和 GPU 与 NPU 精度验证。
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 28 other files, including scripts and reference files (for example `references/benchmark-debug.md`, `references/migration_details.md` and `scripts/demo_scripts/demo1_signalP_gpu.py`).
It sits in AI & LLM Engineering, covering Deep learning and Embeddings. It works with PyTorch and TensorFlow. The repository describes itself as: A comprehensive knowledge base for Huawei Ascend NPU development, structured as distributed Agent Skills. https://ascend-ai-coding.github.io/awesome-ascend-skills/.
When your agent uses it
- Tasks that involve Deep learning
- Tasks that involve Embeddings
Example prompts
- “/ai-for-science-proteinbert”
Requirements
- Python 3
- A Bash shell
Workflow steps
2 steps, taken from the first numbered list in SKILL.md.
- K.dot 与 einsum 维度映射错误
- LayerNorm epsilon 默认值差 100 倍
What it can do on your machine
Read from SKILL.md and the folder at commit 62a4ecb. 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
Ships 13 files in scripts/ (Python and Shell, from the files we listed), which the agent can run.
Shell commands in SKILL.md call:
pythoncondapip
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
repo.huaweicloud.comzenodo.org
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
AI For Science Proteinbert loads about 1.9k tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 43 tokens; SKILL.md has 306 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); the scripts in this folder are not scanned.
SKILL.md
Without a licence we can't republish the file, so here is its outline and opening line. It has 306 words (~1,924 tokens).
“将 ProteinBERT 从 TensorFlow/Keras 完整迁移到 PyTorch + torch_npu， 已通过 5 个基准任务的精度验证（以 GPU 为基线）。”
- name
- ai-for-science-proteinbert
- keywords
- ai-for-science, proteinbert, protein-language-model, tensorflow, pytorch, ascend
Files
SKILL.md and 23 other files (scripts, references) in skills/ai-for-science/models/proteinbert of ascend-ai-coding/awesome-ascend-skills.
- SKILL.md
- references/benchmark-debug.md
- references/migration_details.md
- scripts/demo_scripts/demo1_signalP_gpu.py
- scripts/demo_scripts/demo1_signalP_npu.py
- scripts/demo_scripts/demo2_all_benchmarks_gpu.py
- scripts/demo_scripts/demo2_all_benchmarks_npu.py
- scripts/demo_scripts/demo3_attention_gpu.py
- scripts/demo_scripts/demo3_attention_npu.py
- scripts/deploy_toolkit/convert_weights.py
- scripts/deploy_toolkit/finetune_npu.py
- scripts/deploy_toolkit/inference_npu.py
- scripts/deploy_toolkit/requirements.txt
- scripts/deploy_toolkit/setup.sh
- scripts/proteinbert_pytorch/convert_weights.py
- scripts/proteinbert_pytorch/finetune.py
- … and 8 more
Open the folder on GitHubat commit 62a4ecb
Compare with similar skills
AI For Science Proteinbert 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 |
|---|---|---|---|---|---|---|
| AI For Science Proteinbert this skillascend-ai-coding/awesome-ascend-skills | 174 | — | ~1.9k | Automated safety check: Pass | None | |
| Re AI Modeldslsdzc/rev-skills | 135 | — | ~2.4k | Automated safety check: Pass | Apache-2.0 | |
| Formattingbrendanhasz/probflow | 175 | — | ~381 | Automated safety check: Pass | MIT | |
| Ray Train Distributed TrainingOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~2.7k | Automated safety check: Pass | MIT | |
| Embedded AI Deploymentmatlab/agent-skills-playground | 184 | — | ~3.4k | Automated safety check: Pass | Custom licence | |
| PerforatedaiPerforatedAI/PerforatedAI | 237 | — | ~17k | Automated safety check: Pass | Apache-2.0 |
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Scales PyTorch, TensorFlow and Hugging Face training from a single GPU to multi-node clusters with Ray Train, including Ray Tune sweeps and checkpoint recovery.
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Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder).
Perforatedai
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Expert in PerforatedAI library for adding artificial dendrites to PyTorch neural networks.
Matlab Import External AI Model
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Import PyTorch, ONNX, or Keras 3 / TensorFlow 2.16+ deep learning models into MATLAB as dlnetwork objects.
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Questions about AI For Science Proteinbert
What does AI For Science Proteinbert do?
ProteinBERT 昇腾 NPU 部署与迁移 Skill，适用于将 TensorFlow 或 Keras 版 ProteinBERT 转成基于 PyTorch 与 torchnpu 的实现，覆盖权重转换、embedding 提取、微调训练、注意力可视化和 GPU 与 NPU 精度验证。. AI For Science Proteinbert is an agent skill from ascend-ai-coding/awesome-ascend-skills.
When should I use AI For Science Proteinbert?
AI For Science Proteinbert fits situations like: tasks that involve Deep learning; tasks that involve Embeddings.
How do I install AI For Science Proteinbert in Claude Code?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a claude-code`. Or copy the skill folder (skills/ai-for-science/models/proteinbert in ascend-ai-coding/awesome-ascend-skills) into .claude/skills/ai-for-science-proteinbert in your project. Claude Code loads it when a task matches its description.
How do I install AI For Science Proteinbert in Codex?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a codex`. Or copy the skill folder (skills/ai-for-science/models/proteinbert in ascend-ai-coding/awesome-ascend-skills) into .agents/skills/ai-for-science-proteinbert in your project. Codex loads it when a task matches its description.
Can I use AI For Science Proteinbert 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 ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-proteinbert -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-for-science-proteinbert, .gemini/skills/ai-for-science-proteinbert, .github/skills/ai-for-science-proteinbert and .opencode/skills/ai-for-science-proteinbert in your project.
What does AI For Science Proteinbert need to run?
Going by SKILL.md and its folder, AI For Science Proteinbert needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (python, conda and pip). Our summary lists: Python 3; A Bash shell.
Does AI For Science Proteinbert access the network?
SKILL.md names 2 domains. In commands or code: repo.huaweicloud.com and zenodo.org; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
Is AI For Science Proteinbert 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does AI For Science Proteinbert use?
No licence was found for AI For Science Proteinbert or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does AI For Science Proteinbert use?
About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.7k tokens, read only when the agent opens those files.
What are the alternatives to AI For Science Proteinbert?
Skills that share tags, products or a category with AI For Science Proteinbert: Re AI Model (dslsdzc/rev-skills, 135 stars), Formatting (brendanhasz/probflow, 175 stars), Ray Train Distributed Training (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Embedded AI Deployment (matlab/agent-skills-playground, 184 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains AI For Science Proteinbert?
ascend-ai-coding (a GitHub organization) maintains it in ascend-ai-coding/awesome-ascend-skills, which has 174 GitHub stars. The repository holds 70 skills in this directory. The repository was last updated on October 10, 2026.
Source: ascend-ai-coding/awesome-ascend-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.