ML Research Lab
AnastasiyaW/codex-claude-code-config
Machine-learning research loop for dataset curation, fine-tuning, evaluation, inference deployment, experiment tracking, and model explainability.
AISBench Benchmark - AI model evaluation tool for Ascend NPU.
Install Ais Bench
$ npx skills add ascend-ai-coding/awesome-ascend-skills --skill ais-bench -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ais-bench --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/ais-bench .claude/skills/ais-bench && 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 "ais-bench" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/ais-bench into .claude/skills/ais-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ais-bench", 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/ais-benchType 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 ais-bench -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ais-bench --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/ais-bench .agents/skills/ais-bench && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ais-bench" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/ais-bench into .agents/skills/ais-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ais-bench", 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 ais-bench -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ais-bench --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/ais-bench .cursor/skills/ais-bench && 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 "ais-bench" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/ais-bench into .cursor/skills/ais-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ais-bench", 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/ais-bench--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 ais-bench -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install ascend-ai-coding/awesome-ascend-skills ais-bench --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/ais-bench .gemini/skills/ais-bench && 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 "ais-bench" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/ais-bench into .gemini/skills/ais-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ais-bench", 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 ais-benchInstalls 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 ais-bench -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/ais-bench .github/skills/ais-bench && 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 "ais-bench" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/ais-bench into .github/skills/ais-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ais-bench", 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 ais-bench -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 ais-bench --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/ais-bench .opencode/skills/ais-bench && 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 "ais-bench" agent skill from https://github.com/ascend-ai-coding/awesome-ascend-skills/tree/main/skills/inference/ais-bench into .opencode/skills/ais-bench/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ais-bench", 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
ais-bench- GitHub stars
- 174
- Token cost
- ~2.7k tokens
- SKILL.md length
- 572 words
- Files
- 12 (incl. scripts, references, assets)
- Skills in repo
- 69
- Repo updated
- First seen
- Licence
- None found
At a glance
AISBench Benchmark - AI model evaluation tool for Ascend NPU.
- Works in 4 steps: Start vLLM inference service (follow… → Prepare dataset → Modify model configuration… → …
- Tasks that involve LLM inference and serving
- SKILL.md covers Overview, Installation, Quick Start and Model Task Types, plus 5 more sections
- Runs Shell and Python scripts from its folder; calls pip3, bash and conda; reaches github.com
What it does
Ais Bench is an agent skill from ascend-ai-coding/awesome-ascend-skills. AISBench Benchmark - AI model evaluation tool for Ascend NPU. Supports accuracy evaluation (service/local models on text, multimodal datasets), performance evaluation (latency, throughput, stress testing, steady-state, real traffic simulation), vLLM/Triton inference services, 15+ benchmarks (MMLU, GSM8K, MMMU, docvqa, ocrbenchv2, etc.), multi-turn dialogue, Function Call (BFCL), and custom datasets.
Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts, reference files and assets (for example `assets/custom_meta_template.json`, `assets/model_config_template.py` and `references/cli-reference.md`).
It sits in AI & LLM Engineering, covering LLM inference and serving, Load testing and Machine learning. It works with vLLM. 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 LLM inference and serving
- Tasks that involve Load testing
- Tasks that involve Machine learning
Example prompts
- “/ais-bench”
Requirements
- Python 3
- A Bash shell
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Start vLLM inference service (follow vLLM documentation)
- Prepare dataset
- Modify model configuration (vllm_api_general_chat.py)
- Run evaluation
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 4 files in scripts/ (Shell and Python), which the agent can run.
Shell commands in SKILL.md call:
pip3bashcondagitpython
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.com
Also links to:
opencompass.oss-cn-shanghai.aliyuncs.comais-bench-benchmark-rf.readthedocs.io
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
Ais Bench loads about 2.7k tokens when it runs, and up to ~5.2k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 572 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 572 words (~2,655 tokens).
“AISBench Benchmark is a model evaluation tool built based on OpenCompass. It supports evaluation scenarios for both accuracy and performance testing of AI models on Ascend NPU.”
- name
- ais-bench
- keywords
- ais-bench, aisbench, model evaluation, benchmark, accuracy evaluation, performance evaluation, vllm, multimodal, llm evaluation
- github_url
- https://github.com/AISBench/benchmark
- github_hash
- 03f0f43383c3efdb6ecdcc845eb24bc7a1537575
- version
- 1.0.0
- created_at
- 2026-02-25
- entry_point
- ais_bench
Files
SKILL.md and 11 other files (scripts, references, assets) in skills/inference/ais-bench of ascend-ai-coding/awesome-ascend-skills.
- SKILL.md
- assets/custom_mcq_template.csv
- assets/custom_meta_template.json
- assets/custom_multimodal_template.jsonl
- assets/custom_qa_template.jsonl
- assets/model_config_template.py
- references/cli-reference.md
- references/model-configs.md
- scripts/check_env.sh
- scripts/parse_results.py
- scripts/run_accuracy_test.sh
- scripts/run_performance_test.sh
Open the folder on GitHubat commit 62a4ecb
Compare with similar skills
Ais Bench 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 |
|---|---|---|---|---|---|---|
| Ais Bench this skillascend-ai-coding/awesome-ascend-skills | 174 | — | ~2.7k | Automated safety check: Pass | None | |
| ML Research LabAnastasiyaW/codex-claude-code-config | 154 | — | ~794 | Automated safety check: Pass | MIT | |
| Model Servingancoleman/ai-design-components | 526 | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Hugging Face Evaluation Managermajiayu000/claude-skill-registry | 666 | 3 repos | ~5.6k | Automated safety check: Notes | MIT | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| LLM Benchmarking with lm-evaluation-harnessOrchestra-Research/AI-Research-SKILLs | 13k | 8 repos | ~3k | Automated safety check: Pass | MIT |
Similar skills
Model Serving
ancoleman/ai-design-components
LLM and ML model deployment for inference. An agent skill from ancoleman/ai-design-components.
Hugging Face Evaluation Manager
majiayu000/claude-skill-registry
Add and manage evaluation results in Hugging Face model cards.
- Official
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.
LLM Benchmarking with lm-evaluation-harness
Orchestra-Research/AI-Research-SKILLs
Runs lm-evaluation-harness to benchmark language models on academic suites such as MMLU, GSM8K and HumanEval, compare models and track training checkpoints.
- Official
Hugging Face Local Model Evals
huggingface/skills
Runs evaluations of Hugging Face Hub models on local hardware with inspect-ai or lighteval, and helps choose between vLLM, Transformers and accelerate backends.
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Works with
Categories
Questions about Ais Bench
What does Ais Bench do?
AISBench Benchmark - AI model evaluation tool for Ascend NPU. Ais Bench is an agent skill from ascend-ai-coding/awesome-ascend-skills. AISBench Benchmark - AI model evaluation tool for Ascend NPU.
When should I use Ais Bench?
Ais Bench fits situations like: tasks that involve LLM inference and serving; tasks that involve Load testing; tasks that involve Machine learning.
How do I install Ais Bench in Claude Code?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ais-bench -a claude-code`. Or copy the skill folder (skills/inference/ais-bench in ascend-ai-coding/awesome-ascend-skills) into .claude/skills/ais-bench in your project. Claude Code loads it when a task matches its description.
How do I install Ais Bench in Codex?
Run `npx skills add ascend-ai-coding/awesome-ascend-skills --skill ais-bench -a codex`. Or copy the skill folder (skills/inference/ais-bench in ascend-ai-coding/awesome-ascend-skills) into .agents/skills/ais-bench in your project. Codex loads it when a task matches its description.
Can I use Ais Bench 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 ais-bench -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ais-bench, .gemini/skills/ais-bench, .github/skills/ais-bench and .opencode/skills/ais-bench in your project.
What does Ais Bench need to run?
Going by SKILL.md and its folder, Ais Bench needs a shell and Python for the scripts in its folder and the command-line tools its instructions call (pip3, bash, conda, git and python). Our summary lists: Python 3; A Bash shell.
Does Ais Bench access the network?
SKILL.md names 3 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: opencompass.oss-cn-shanghai.aliyuncs.com and ais-bench-benchmark-rf.readthedocs.io. This is read from the text; nothing was executed.
Is Ais Bench 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 Ais Bench use?
No licence was found for Ais Bench or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Ais Bench use?
About 2.7k tokens (SKILL.md is roughly 11k 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 2.5k tokens, read only when the agent opens those files.
What are the alternatives to Ais Bench?
Skills that share tags, products or a category with Ais Bench: ML Research Lab (AnastasiyaW/codex-claude-code-config, 154 stars), Model Serving (ancoleman/ai-design-components, 526 stars), Hugging Face Evaluation Manager (majiayu000/claude-skill-registry, 666 stars) and SageMaker Serving Image Selection (huggingface/skills, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Ais Bench?
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.