SageMaker Serving Image Selection
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
Agent skill
by ascend-ai-coding in ascend-ai-coding/awesome-ascend-skills
模型层 Tensor 打点与精度对比工具。用于在模型 forward 过程中捕获模型各层中间 tensor,实现 GPU/NPU 精度对比调试。支持 vLLM、SGLang 推理框架。When to use: When you need to debug precision issues between GPU and NPU,or validate layer-wise tensor…
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill inference-precision-tensor-dump-compare -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills inference-precision-tensor-dump-compare --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/inference/inference-precision/inference-precision-tensor-dump-compare .claude/skills/inference-precision-tensor-dump-compare && 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 "inference-precision-tensor-dump-compare" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/inference-precision/inference-precision-tensor-dump-compare into .claude/skills/inference-precision-tensor-dump-compare/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-precision-tensor-dump-compare", 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/inference/inference-precision/inference-precision-tensor-dump-compareType 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 inference-precision-tensor-dump-compare -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills inference-precision-tensor-dump-compare --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/inference/inference-precision/inference-precision-tensor-dump-compare .agents/skills/inference-precision-tensor-dump-compare && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "inference-precision-tensor-dump-compare" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/inference-precision/inference-precision-tensor-dump-compare into .agents/skills/inference-precision-tensor-dump-compare/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-precision-tensor-dump-compare", 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 inference-precision-tensor-dump-compare -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills inference-precision-tensor-dump-compare --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/inference/inference-precision/inference-precision-tensor-dump-compare .cursor/skills/inference-precision-tensor-dump-compare && 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 "inference-precision-tensor-dump-compare" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/inference-precision/inference-precision-tensor-dump-compare into .cursor/skills/inference-precision-tensor-dump-compare/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-precision-tensor-dump-compare", 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/inference/inference-precision/inference-precision-tensor-dump-compare--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 inference-precision-tensor-dump-compare -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills inference-precision-tensor-dump-compare --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/inference/inference-precision/inference-precision-tensor-dump-compare .gemini/skills/inference-precision-tensor-dump-compare && 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 "inference-precision-tensor-dump-compare" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/inference-precision/inference-precision-tensor-dump-compare into .gemini/skills/inference-precision-tensor-dump-compare/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-precision-tensor-dump-compare", 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 inference-precision-tensor-dump-compareInstalls 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 inference-precision-tensor-dump-compare -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/inference/inference-precision/inference-precision-tensor-dump-compare .github/skills/inference-precision-tensor-dump-compare && 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 "inference-precision-tensor-dump-compare" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/inference-precision/inference-precision-tensor-dump-compare into .github/skills/inference-precision-tensor-dump-compare/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-precision-tensor-dump-compare", 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 inference-precision-tensor-dump-compare -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 inference-precision-tensor-dump-compare --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/inference/inference-precision/inference-precision-tensor-dump-compare .opencode/skills/inference-precision-tensor-dump-compare && 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 "inference-precision-tensor-dump-compare" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/inference-precision/inference-precision-tensor-dump-compare into .opencode/skills/inference-precision-tensor-dump-compare/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "inference-precision-tensor-dump-compare", 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.
inference-precision-tensor-dump-compare模型层 Tensor 打点与精度对比工具。用于在模型 forward 过程中捕获模型各层中间 tensor,实现 GPU/NPU 精度对比调试。支持 vLLM、SGLang 推理框架。When to use: When you need to debug precision issues between GPU and NPU,or validate layer-wise tensor…
Inference Precision Tensor Dump Compare is an agent skill from ascend-ai-coding/awesome-ascend-skills. 模型层 Tensor 打点与精度对比工具。用于在模型 forward 过程中捕获模型各层中间 tensor,实现 GPU/NPU 精度对比调试。支持 vLLM、SGLang 推理框架。When to use: When you need to debug precision issues between GPU and NPU,or validate layer-wise tensor outputs during inference.
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including scripts and reference files (for example `references/checklist.md`, `references/framework-integration.md` and `references/review-guide.md`).
It sits in AI & LLM Engineering, covering LLM inference and serving. It works with vLLM and SGLang. 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/.
Read from SKILL.md and the folder at commit 62a4ecb. It shows what the files ask for, not the result of running them.
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.
Ships 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pythonFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Inference Precision Tensor Dump Compare loads about 2.4k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 65 tokens; SKILL.md has 483 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.
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.
Without a licence we can't republish the file, so here is its outline and opening line. It has 483 words (~2,416 tokens).
“在模型 forward 过程中打点,捕获中间 tensor,用于 GPU/NPU 精度对比。”
SKILL.md and 9 other files (scripts, references) in skills/inference/inference-precision/inference-precision-tensor-dump-compare of ascend-ai-coding/awesome-ascend-skills.
Open the folder on GitHubat commit 62a4ecb
Inference Precision Tensor Dump Compare 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 |
|---|---|---|---|---|---|---|
| Inference Precision Tensor Dump Compare this skillascend-ai-coding/awesome-ascend-skills | 174 | — | ~2.4k | Automated safety check: Pass | None | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Dstack Prototypingdstackai/dstack | 2.3k | — | ~1.6k | Automated safety check: Pass | MPL-2.0 | |
| Debug InferenceNVIDIA/OpenShell | 15k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 | |
| Graphsignalgraphsignal/graphsignal | 257 | — | ~6.2k | Automated safety check: Pass | Apache-2.0 | |
| One EvalOpenDCAI/One-Eval | 165 | — | ~2.4k | Automated safety check: Pass | Apache-2.0 |
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
dstackai/dstack
Use with the dstack skill for model-serving work when the image, serving command, resources, backend/fleet choice, or service behavior is not proven.
NVIDIA/OpenShell
Debug inference clients that use an attached provider and its native endpoint, including hosted APIs and host-local Ollama, vLLM, SGLang, TRT-LLM, LM Studio, or NIM.
graphsignal/graphsignal
Profile AI inference workloads (vLLM, SGLang, TensorRT-LLM, PyTorch, any GPU application) with the Graphsignal profiler and read the results from its local /signals JSON endpoint.
OpenDCAI/One-Eval
驱动 One-Eval 对 API 或本地模型做端到端评测,覆盖纯文本、多模态、代码生成、函数调用和 Agent benchmark。当用户想评测模型在一个或多个 benchmark 上的表现、比较分数、补充 metric,或生成图文评测报告时使用本 skill。
BBuf/AI-Infra-Auto-Driven-SKILLS
Analyzes Torch Profiler traces from SGLang, vLLM and TensorRT-LLM servers into kernel attribution, overlap and fusion tables.
ascend-ai-coding/awesome-ascend-skills
当用户需要对华为昇腾 NPU 进行硬件层面的管理、测试或诊断时使用此 skill。典型场景: - 查看 NPU 卡的状态、温度、利用率 - 测试内存带宽(h2d/d2h/d2d/p2p) - 跑算力/功耗基准测试(TFLOPS、TOPS) - 诊断 NPU 硬件故障或做健康检查 - 对 NPU 卡做压力测试(aicore、内存) - 复位/恢复卡住或异常的 NPU 卡 典型用户问题(即使不提…
ascend-ai-coding/awesome-ascend-skills
End-to-end AscendC custom operator development for Ascend NPU in an ascend-kernel (csrc/ops + build.sh + torchnpu PyTorch custom op) project.
ascend-ai-coding/awesome-ascend-skills
Complete toolkit for Huawei Ascend NPU model conversion and end-to-end inference adaptation.
ascend-ai-coding/awesome-ascend-skills
当需要编写 PyPTO 算子实现时使用此 skill。基于需求规格、设计方案和参考实现,生成完整可运行的 PyPTO 算子实现与配套测试、文档。Triggers: 实现算子、写 kernel、编写实现、写 impl、算子编码、开始编码、code the op、写 test、生成测试、写实现代码、op develop、kernel 实现。
ascend-ai-coding/awesome-ascend-skills
Analyze official Megatron-LM commits, PRs, and branch change sets to identify feature evolution, candidate breaking changes, and migration-relevant events.
ascend-ai-coding/awesome-ascend-skills
Track and normalize change requests against the official Megatron-LM repository by branch, PR, commit, commit range, or time window.
Categories
模型层 Tensor 打点与精度对比工具。用于在模型 forward 过程中捕获模型各层中间 tensor,实现 GPU/NPU 精度对比调试。支持 vLLM、SGLang 推理框架。When to use: When you need to debug precision issues between GPU and NPU,or validate layer-wise tensor…. Inference Precision Tensor Dump Compare is an agent skill from ascend-ai-coding/awesome-ascend-skills. 模型层 Tensor 打点与精度对比工具。用于在模型 forward 过程中捕获模型各层中间 tensor,实现 GPU/NPU 精度对比调试。支持 vLLM、SGLang 推理框架。When to use: When you need to debug precision issues between GPU and NPU,or validate layer-wise tensor outputs during inference.
Inference Precision Tensor Dump Compare fits situations like: tasks that involve LLM inference and serving.
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill inference-precision-tensor-dump-compare -a claude-code`. Or copy the skill folder (skills/inference/inference-precision/inference-precision-tensor-dump-compare in ascend-ai-coding/awesome-ascend-skills) into .claude/skills/inference-precision-tensor-dump-compare in your project. Claude Code loads it when a task matches its description.
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill inference-precision-tensor-dump-compare -a codex`. Or copy the skill folder (skills/inference/inference-precision/inference-precision-tensor-dump-compare in ascend-ai-coding/awesome-ascend-skills) into .agents/skills/inference-precision-tensor-dump-compare in your project. Codex loads it when a task matches its description.
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 inference-precision-tensor-dump-compare -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/inference-precision-tensor-dump-compare, .gemini/skills/inference-precision-tensor-dump-compare, .github/skills/inference-precision-tensor-dump-compare and .opencode/skills/inference-precision-tensor-dump-compare in your project.
Going by SKILL.md and its folder, Inference Precision Tensor Dump Compare needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
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.
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.
No licence was found for Inference Precision Tensor Dump Compare or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
About 2.4k tokens (SKILL.md is roughly 9.7k 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 17k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Inference Precision Tensor Dump Compare: SageMaker Serving Image Selection (huggingface/skills, 11k stars), Dstack Prototyping (dstackai/dstack, 2.3k stars), Debug Inference (NVIDIA/OpenShell, 15k stars) and Graphsignal (graphsignal/graphsignal, 257 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
ascend-ai-coding (a GitHub organization) maintains it in ascend-ai-coding/awesome-ascend-skills, which has 174 GitHub stars. The repository holds 69 skills in this directory. The repository was last updated on October 7, 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.