Formatting
brendanhasz/probflow
Ensure consistent code formatting using the uv package manager and pre-commit.
Agent skill
AI For Science Tf To Pytorch
by ascend-ai-coding in ascend-ai-coding/awesome-ascend-skills
TensorFlow 或 Keras 模型改写到 PyTorch 的通用 Skill，适用于在华为 Ascend NPU 或其他依赖 PyTorch 生态的平台上完成层级映射、权重转换、逐层数值验证和端到端精度对比，尤其适合 ProteinBERT、DeepFRI 这类科学模型的跨框架迁移。
Install AI For Science Tf To Pytorch
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-tf-to-pytorch -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-tf-to-pytorch --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/tf-framework/tf-to-pytorch .claude/skills/ai-for-science-tf-to-pytorch && 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-tf-to-pytorch" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/tf-framework/tf-to-pytorch into .claude/skills/ai-for-science-tf-to-pytorch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-tf-to-pytorch", 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/tf-framework/tf-to-pytorchType 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-tf-to-pytorch -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-tf-to-pytorch --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/tf-framework/tf-to-pytorch .agents/skills/ai-for-science-tf-to-pytorch && 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-tf-to-pytorch" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/tf-framework/tf-to-pytorch into .agents/skills/ai-for-science-tf-to-pytorch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-tf-to-pytorch", 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-tf-to-pytorch -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ai-for-science-tf-to-pytorch --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/tf-framework/tf-to-pytorch .cursor/skills/ai-for-science-tf-to-pytorch && 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-tf-to-pytorch" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/tf-framework/tf-to-pytorch into .cursor/skills/ai-for-science-tf-to-pytorch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-tf-to-pytorch", 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/tf-framework/tf-to-pytorch--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-tf-to-pytorch -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-tf-to-pytorch --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/tf-framework/tf-to-pytorch .gemini/skills/ai-for-science-tf-to-pytorch && 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-tf-to-pytorch" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/tf-framework/tf-to-pytorch into .gemini/skills/ai-for-science-tf-to-pytorch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-tf-to-pytorch", 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-tf-to-pytorchInstalls 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-tf-to-pytorch -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/tf-framework/tf-to-pytorch .github/skills/ai-for-science-tf-to-pytorch && 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-tf-to-pytorch" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/tf-framework/tf-to-pytorch into .github/skills/ai-for-science-tf-to-pytorch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-tf-to-pytorch", 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-tf-to-pytorch -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-tf-to-pytorch --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/tf-framework/tf-to-pytorch .opencode/skills/ai-for-science-tf-to-pytorch && 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-tf-to-pytorch" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/ai-for-science/tf-framework/tf-to-pytorch into .opencode/skills/ai-for-science-tf-to-pytorch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-for-science-tf-to-pytorch", 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-tf-to-pytorch- GitHub stars
- 174
- Token cost
- ~3.4k tokens
- SKILL.md length
- 978 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 70
- Repo updated
- First seen
- Licence
- None found
At a glance
TensorFlow 或 Keras 模型改写到 PyTorch 的通用 Skill，适用于在华为 Ascend NPU 或其他依赖 PyTorch 生态的平台上完成层级映射、权重转换、逐层数值验证和端到端精度对比，尤其适合 ProteinBERT、DeepFRI 这类科学模型的跨框架迁移。
- Works in 6 steps: Analyze the TF Model → Layer-by-Layer Conversion Rules → K.dot and High-Dimensional Tensor… → …
- Tasks that involve Deep learning
- SKILL.md covers When to Use, Conversion Workflow, Step 1: Analyze the TF Model and Step 2: Layer-by-Layer…, plus 7 more sections
- Runs Python scripts from its folder; calls python
What it does
AI For Science Tf To Pytorch is an agent skill from ascend-ai-coding/awesome-ascend-skills. TensorFlow 或 Keras 模型改写到 PyTorch 的通用 Skill，适用于在华为 Ascend NPU 或其他依赖 PyTorch 生态的平台上完成层级映射、权重转换、逐层数值验证和端到端精度对比，尤其适合 ProteinBERT、DeepFRI 这类科学模型的跨框架迁移。
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/numerical-validation.md`, `references/weight_conversion_example.md` and `scripts/compare_arrays.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-tf-to-pytorch”
Requirements
- Python 3
Workflow steps
6 steps, taken from the step headings in SKILL.md.
- Analyze the TF Model
- Layer-by-Layer Conversion Rules
- K.dot and High-Dimensional Tensor Operations
- Weight Mapping
- Layer-by-Layer Verification
- Fine-tuning Verification
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 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
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
AI For Science Tf To Pytorch loads about 3.4k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 44 tokens; SKILL.md has 978 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 978 words (~3,422 tokens).
“A systematic approach to converting TF/Keras models to PyTorch with numerical precision verification. Derived from real production experience converting ProteinBERT (6-layer attention model, 16M params, 145 weight arrays) and DeepFRI (GCN + CNN protein function prediction with CuDNNLSTM language model).”
- name
- ai-for-science-tf-to-pytorch
- keywords
- ai-for-science, tensorflow, keras, pytorch, conversion, weight-mapping
Files
SKILL.md and 3 other files (scripts, references) in skills/ai-for-science/tf-framework/tf-to-pytorch of ascend-ai-coding/awesome-ascend-skills.
- SKILL.md
- references/numerical-validation.md
- references/weight_conversion_example.md
- scripts/compare_arrays.py
Open the folder on GitHubat commit 62a4ecb
Compare with similar skills
AI For Science Tf To Pytorch 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 Tf To Pytorch this skillascend-ai-coding/awesome-ascend-skills | 174 | — | ~3.4k | Automated safety check: Pass | None | |
| 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 | |
| Matlab Import External AI Modelmatlab/matlab-agentic-toolkit | 1.1k | — | ~2.8k | Automated safety check: Pass | Custom licence |
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Categories
Questions about AI For Science Tf To Pytorch
What does AI For Science Tf To Pytorch do?
TensorFlow 或 Keras 模型改写到 PyTorch 的通用 Skill，适用于在华为 Ascend NPU 或其他依赖 PyTorch 生态的平台上完成层级映射、权重转换、逐层数值验证和端到端精度对比，尤其适合 ProteinBERT、DeepFRI 这类科学模型的跨框架迁移。. AI For Science Tf To Pytorch is an agent skill from ascend-ai-coding/awesome-ascend-skills.
When should I use AI For Science Tf To Pytorch?
AI For Science Tf To Pytorch fits situations like: tasks that involve Deep learning.
How do I install AI For Science Tf To Pytorch in Claude Code?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-tf-to-pytorch -a claude-code`. Or copy the skill folder (skills/ai-for-science/tf-framework/tf-to-pytorch in ascend-ai-coding/awesome-ascend-skills) into .claude/skills/ai-for-science-tf-to-pytorch in your project. Claude Code loads it when a task matches its description.
How do I install AI For Science Tf To Pytorch in Codex?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ai-for-science-tf-to-pytorch -a codex`. Or copy the skill folder (skills/ai-for-science/tf-framework/tf-to-pytorch in ascend-ai-coding/awesome-ascend-skills) into .agents/skills/ai-for-science-tf-to-pytorch in your project. Codex loads it when a task matches its description.
Can I use AI For Science Tf To Pytorch 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-tf-to-pytorch -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-tf-to-pytorch, .gemini/skills/ai-for-science-tf-to-pytorch, .github/skills/ai-for-science-tf-to-pytorch and .opencode/skills/ai-for-science-tf-to-pytorch in your project.
What does AI For Science Tf To Pytorch need to run?
Going by SKILL.md and its folder, AI For Science Tf To Pytorch needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does AI For Science Tf To Pytorch 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 AI For Science Tf To Pytorch 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 Tf To Pytorch use?
No licence was found for AI For Science Tf To Pytorch or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does AI For Science Tf To Pytorch use?
About 3.4k tokens (SKILL.md is roughly 14k 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 508 tokens, read only when the agent opens those files.
What are the alternatives to AI For Science Tf To Pytorch?
Skills that share tags, products or a category with AI For Science Tf To Pytorch: Formatting (brendanhasz/probflow, 175 stars), Ray Train Distributed Training (Orchestra-Research/AI-Research-SKILLs, 13k stars), Embedded AI Deployment (matlab/agent-skills-playground, 184 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 Tf To Pytorch?
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.