Formatting
brendanhasz/probflow
Ensure consistent code formatting using the uv package manager and pre-commit.
DeepFRI 的 TensorFlow 到 PyTorch 转换与昇腾 NPU 迁移 Skill，适用于蛋白质功能预测场景下的 TF 模型分析、PyTorch 重写、权重逐层映射、NPU 推理与精度验证，尤其适合需要在 Ascend 上运行 DeepFRI CNN 或 GCN 路径时使用。
Install AI For Science Deepfri
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-deepfri -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-deepfri --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/deepfri .claude/skills/ai-for-science-deepfri && 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-deepfri" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/deepfri into .claude/skills/ai-for-science-deepfri/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-deepfri", 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/deepfriType 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-deepfri -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-deepfri --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/deepfri .agents/skills/ai-for-science-deepfri && 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-deepfri" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/deepfri into .agents/skills/ai-for-science-deepfri/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-deepfri", 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-deepfri -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-deepfri --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/deepfri .cursor/skills/ai-for-science-deepfri && 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-deepfri" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/deepfri into .cursor/skills/ai-for-science-deepfri/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-deepfri", 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/deepfri--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-deepfri -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-deepfri --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/deepfri .gemini/skills/ai-for-science-deepfri && 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-deepfri" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/deepfri into .gemini/skills/ai-for-science-deepfri/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-deepfri", 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-deepfriInstalls 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-deepfri -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/deepfri .github/skills/ai-for-science-deepfri && 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-deepfri" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/deepfri into .github/skills/ai-for-science-deepfri/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-deepfri", 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-deepfri -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-deepfri --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/deepfri .opencode/skills/ai-for-science-deepfri && 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-deepfri" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/models/deepfri into .opencode/skills/ai-for-science-deepfri/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-deepfri", 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-deepfri- GitHub stars
- 174
- Token cost
- ~2.4k tokens
- SKILL.md length
- 583 words
- Files
- 8 (incl. scripts, references)
- Skills in repo
- 70
- Repo updated
- First seen
- Licence
- None found
At a glance
DeepFRI 的 TensorFlow 到 PyTorch 转换与昇腾 NPU 迁移 Skill，适用于蛋白质功能预测场景下的 TF 模型分析、PyTorch 重写、权重逐层映射、NPU 推理与精度验证，尤其适合需要在 Ascend 上运行 DeepFRI CNN 或 GCN 路径时使用。
- Works in 7 steps: ：克隆仓库 & 下载模型 → ：创建 Conda 环境 → ：验证 NPU 可用 → …
- Tasks that involve Deep learning
- SKILL.md covers 项目概述, 前置条件, 迁移步骤 and 关键转换规则, plus 5 more sections
- Runs Python scripts from its folder; calls python, pip and conda; reaches github.com and users.flatironinstitute.org
What it does
AI For Science Deepfri is an agent skill from ascend-ai-coding/awesome-ascend-skills. DeepFRI 的 TensorFlow 到 PyTorch 转换与昇腾 NPU 迁移 Skill，适用于蛋白质功能预测场景下的 TF 模型分析、PyTorch 重写、权重逐层映射、NPU 推理与精度验证，尤其适合需要在 Ascend 上运行 DeepFRI CNN 或 GCN 路径时使用。
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `references/weight-conversion-checklist.md`, `scripts/convert_weights.py` and `scripts/predict_npu.py`).
It sits in AI & LLM Engineering, covering Deep learning. 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
Example prompts
- “/ai-for-science-deepfri”
Requirements
- Python 3
Workflow steps
7 steps, taken from the step headings in SKILL.md.
- ：克隆仓库 & 下载模型
- ：创建 Conda 环境
- ：验证 NPU 可用
- ：分析 TF 模型权重结构
- ：部署 PyTorch 模型文件 & 转换权重
- ：NPU 推理
- ：精度验证
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 6 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonpipcondagitwgetpython3
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:
github.comusers.flatironinstitute.orgrepo.huaweicloud.com
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 Deepfri loads about 2.4k tokens when it runs, and up to ~2.6k if it reads all its reference files. Until then it costs about 42 tokens; SKILL.md has 583 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 583 words (~2,385 tokens).
“DeepFRI 是一个基于 GCN + LSTM 语言模型的蛋白质功能预测框架，原始实现基于 TensorFlow/Keras。本 Skill 记录将其完整迁移到 PyTorch + 昇腾 NPU 的全过程。”
- name
- ai-for-science-deepfri
- keywords
- ai-for-science, deepfri, protein-function, tensorflow, pytorch, ascend
Files
SKILL.md and 7 other files (scripts, references) in skills/ai-for-science/models/deepfri of ascend-ai-coding/awesome-ascend-skills.
- SKILL.md
- references/weight-conversion-checklist.md
- scripts/convert_weights.py
- scripts/predict_npu.py
- scripts/torch_layers.py
- scripts/torch_model.py
- scripts/torch_predictor.py
- scripts/verify_accuracy.py
Open the folder on GitHubat commit 62a4ecb
Compare with similar skills
AI For Science Deepfri 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 Deepfri this skillascend-ai-coding/awesome-ascend-skills | 174 | — | ~2.4k | Automated safety check: Pass | None | |
| Formattingbrendanhasz/probflow | 175 | — | ~381 | Automated safety check: Pass | MIT | |
| Embedded AI Deploymentmatlab/agent-skills-playground | 183 | — | ~3.4k | Automated safety check: Pass | Custom licence | |
| Ray Train Distributed TrainingOrchestra-Research/AI-Research-SKILLs | 13k | 2 repos | ~2.7k | Automated safety check: Pass | MIT | |
| PerforatedaiPerforatedAI/PerforatedAI | 237 | — | ~17k | Automated safety check: Pass | Apache-2.0 | |
| Matlab Import External AI Modelmatlab/matlab-agentic-toolkit | 1.1k | — | ~2.8k | Automated safety check: Pass | Custom licence |
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Questions about AI For Science Deepfri
What does AI For Science Deepfri do?
DeepFRI 的 TensorFlow 到 PyTorch 转换与昇腾 NPU 迁移 Skill，适用于蛋白质功能预测场景下的 TF 模型分析、PyTorch 重写、权重逐层映射、NPU 推理与精度验证，尤其适合需要在 Ascend 上运行 DeepFRI CNN 或 GCN 路径时使用。. AI For Science Deepfri is an agent skill from ascend-ai-coding/awesome-ascend-skills.
When should I use AI For Science Deepfri?
AI For Science Deepfri fits situations like: tasks that involve Deep learning.
How do I install AI For Science Deepfri in Claude Code?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-deepfri -a claude-code`. Or copy the skill folder (skills/ai-for-science/models/deepfri in ascend-ai-coding/awesome-ascend-skills) into .claude/skills/ai-for-science-deepfri in your project. Claude Code loads it when a task matches its description.
How do I install AI For Science Deepfri in Codex?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-deepfri -a codex`. Or copy the skill folder (skills/ai-for-science/models/deepfri in ascend-ai-coding/awesome-ascend-skills) into .agents/skills/ai-for-science-deepfri in your project. Codex loads it when a task matches its description.
Can I use AI For Science Deepfri 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-deepfri -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-deepfri, .gemini/skills/ai-for-science-deepfri, .github/skills/ai-for-science-deepfri and .opencode/skills/ai-for-science-deepfri in your project.
What does AI For Science Deepfri need to run?
Going by SKILL.md and its folder, AI For Science Deepfri needs Python for the scripts in its folder and the command-line tools its instructions call (python, pip, conda, git, wget and python3). Our summary lists: Python 3.
Does AI For Science Deepfri access the network?
SKILL.md names 3 domains. In commands or code: github.com, users.flatironinstitute.org and repo.huaweicloud.com; 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 Deepfri 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 Deepfri use?
No licence was found for AI For Science Deepfri or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does AI For Science Deepfri use?
About 2.4k tokens (SKILL.md is roughly 9.5k 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 212 tokens, read only when the agent opens those files.
What are the alternatives to AI For Science Deepfri?
Skills that share tags, products or a category with AI For Science Deepfri: Formatting (brendanhasz/probflow, 175 stars), Embedded AI Deployment (matlab/agent-skills-playground, 183 stars), Ray Train Distributed Training (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Perforatedai (PerforatedAI/PerforatedAI, 237 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 Deepfri?
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 9, 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.