Crawl4AI Web Scraping
smallnest/goclaw
Scrapes sites, handles JavaScript-heavy pages and extracts structured data with Crawl4AI, through its crwl CLI or Python SDK, including schema-based extraction without an LLM.
A skill your agent uses for Anomalib benchmark pipelines, tiled ensemble workflows, and advanced pipeline orchestration helpers while keeping experimental execution paths explicit.
Install Pipelines And Benchmarks
$ npx skills add VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill pipelines-and-benchmarks --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/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks .claude/skills/pipelines-and-benchmarks && 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 "pipelines-and-benchmarks" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks into .claude/skills/pipelines-and-benchmarks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pipelines-and-benchmarks", 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/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarksType 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 VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill pipelines-and-benchmarks --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks .agents/skills/pipelines-and-benchmarks && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "pipelines-and-benchmarks" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks into .agents/skills/pipelines-and-benchmarks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pipelines-and-benchmarks", 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 VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill pipelines-and-benchmarks --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks .cursor/skills/pipelines-and-benchmarks && 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 "pipelines-and-benchmarks" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks into .cursor/skills/pipelines-and-benchmarks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pipelines-and-benchmarks", 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/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks--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 VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill pipelines-and-benchmarks --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks .gemini/skills/pipelines-and-benchmarks && 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 "pipelines-and-benchmarks" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks into .gemini/skills/pipelines-and-benchmarks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pipelines-and-benchmarks", 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 VectorSpaceLab/AREX-Skill pipelines-and-benchmarksInstalls 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 VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks .github/skills/pipelines-and-benchmarks && 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 "pipelines-and-benchmarks" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks into .github/skills/pipelines-and-benchmarks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pipelines-and-benchmarks", 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 VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill pipelines-and-benchmarks --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks .opencode/skills/pipelines-and-benchmarks && 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 "pipelines-and-benchmarks" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks into .opencode/skills/pipelines-and-benchmarks/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "pipelines-and-benchmarks", 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
pipelines-and-benchmarks- GitHub stars
- 331
- Token cost
- ~952 tokens
- SKILL.md length
- 362 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 157
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
A skill your agent uses for Anomalib benchmark pipelines, tiled ensemble workflows, and advanced pipeline orchestration helpers while keeping experimental execution paths explicit.
- Anomalib benchmark pipelines
- SKILL.md covers Read first, Safe operating boundary, Quick routing and Minimal preflight commands
- Runs Python scripts from its folder; calls python
- Tiled ensemble workflows
What it does
Pipelines And Benchmarks is an agent skill from VectorSpaceLab/AREX-Skill. Use for Anomalib benchmark pipelines, tiled ensemble workflows, and advanced pipeline orchestration helpers while keeping experimental execution paths explicit.
Its SKILL.md is about 950 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/pipelines-and-benchmarks.md`, `references/tiled-ensemble.md` and `references/troubleshooting.md`).
It sits in Data & Analytics, covering Data pipelines and ETL. It works with CUDA. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is Apache-2.0.
When your agent uses it
- Anomalib benchmark pipelines
- Tiled ensemble workflows
- Advanced pipeline orchestration helpers while keeping experimental execution paths explicit
Example prompts
- “/pipelines-and-benchmarks”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. 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
Pipelines And Benchmarks loads about 952 tokens when it runs, and up to ~6.3k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 362 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
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its Apache-2.0 licence (© VectorSpaceLab). 362 words, ~952 tokens.
Download SKILL.mdSave it as
.claude/skills/pipelines-and-benchmarks/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- pipelines-and-benchmarks
- description
- Use for Anomalib benchmark pipelines, tiled ensemble workflows, and advanced pipeline orchestration helpers while keeping experimental execution paths explicit.
- metadata.disco-role
- operating
- disable-model-invocation
- true
- license
- Apache 2.0
Pipelines and Benchmarks
Use this sub-skill when the user asks about Anomalib benchmark configs, pipeline runners/jobs/generators, tiled ensemble training or evaluation, or safe preflight checks for pipeline configuration.
Read first
- For benchmark configs, grid expansion, runner choice, custom pipeline structure, and benchmark-scale caveats, read references/pipelines-and-benchmarks.md.
- For tiled ensemble config, train/eval stages, root directory selection, and cost controls, read references/tiled-ensemble.md.
- For failure recovery, CPU/CUDA fallback, missing config sections, and wrong results paths, read references/troubleshooting.md.
- For a lightweight import/config preflight that does not run training, testing, benchmark jobs, or tiled ensemble jobs, use scripts/pipeline_config_smoke.py.
Safe operating boundary
Prefer read-only parsing and config repair before execution. Pipeline execution can train models, load datasets, spawn processes, create results directories, and run for a long time. Treat these as expensive unless the user explicitly provides dataset paths, runtime budget, and backend/hardware intent.
Route elsewhere when the request is primarily about:
- model or datamodule selection details beyond the pipeline config shell;
- core
Engine.fit,Engine.test, metrics, post-processing, loggers, callbacks, or visualization semantics; - export formats, inferencers, OpenVINO/Torch deployment, or Studio application code.
Show full SKILL.md (188 more words)Show less
Quick routing
| User need | Do this |
|---|---|
| “How do I benchmark several models/categories?” | Explain the benchmark YAML shape, grid keys, and CPU/CUDA runner selection from pipelines-and-benchmarks. |
| “Switch a benchmark from CUDA to CPU without losing the grid.” | Change only the top-level accelerator to cpu or [cpu]; keep the benchmark tree and all grid leaves unchanged. Run the smoke helper before execution. |
| “Create a custom pipeline/job/runner.” | Explain the Pipeline → Runner → JobGenerator → Job contract; keep custom code independent of execution strategy. |
| “Use tiled ensemble on high-resolution images.” | Read the tiled ensemble reference, warn that it is experimental, validate the config, and require a results-root decision before evaluation. |
| “Tiled eval cannot find checkpoints/results.” | Check whether EvalTiledEnsemble(root_dir=...) points at the exact versioned run directory containing the ensemble weights and stats, not the parent default results directory. |
| “Run the MEBin post-processing benchmark.” | Treat it as benchmark-scale and reference-only by default; require explicit dataset path, output path, model/category budget, and runtime approval. |
Minimal preflight commands
From a project where Anomalib is importable:
python sub-skills/pipelines-and-benchmarks/scripts/pipeline_config_smoke.py --import-only
python sub-skills/pipelines-and-benchmarks/scripts/pipeline_config_smoke.py --benchmark-config benchmark.yaml
python sub-skills/pipelines-and-benchmarks/scripts/pipeline_config_smoke.py --tiled-config ensemble.yaml --eval-root results/Padim/MVTecAD/bottle/v0The helper only imports public entrypoints and validates config shape/paths. It does not call Pipeline.run, train models, evaluate models, or create result directories.
© VectorSpaceLab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/pipelines-and-benchmarks.md
- references/tiled-ensemble.md
- references/troubleshooting.md
- scripts/pipeline_config_smoke.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Pipelines And Benchmarks 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 |
|---|---|---|---|---|---|---|
| Pipelines And Benchmarks this skillVectorSpaceLab/AREX-Skill | 331 | — | ~952 | Automated safety check: Pass | Apache-2.0 | |
| Crawl4AI Web Scrapingsmallnest/goclaw | 599 | 1 repos | ~2.5k | Automated safety check: Pass | MIT | |
| Glue 09 10 Migrationaws-samples/aws-glue-samples | 1.5k | — | ~2.4k | Automated safety check: Pass | MIT-0 | |
| Migrate Glue Devendpoint To Interactive Sessionsaws-samples/aws-glue-samples | 1.5k | — | ~3.6k | Automated safety check: Pass | MIT-0 | |
| Dbt Databricks PR Readydatabricks/dbt-databricks | 380 | — | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Apache Spark EngineerJeffallan/claude-skills | 12k | 1 repos | ~1.7k | Automated safety check: Pass | MIT |
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Works with
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Questions about Pipelines And Benchmarks
What does Pipelines And Benchmarks do?
A skill your agent uses for Anomalib benchmark pipelines, tiled ensemble workflows, and advanced pipeline orchestration helpers while keeping experimental execution paths explicit. Pipelines And Benchmarks is an agent skill from VectorSpaceLab/AREX-Skill. Use for Anomalib benchmark pipelines, tiled ensemble workflows, and advanced pipeline orchestration helpers while keeping experimental execution paths explicit.
When should I use Pipelines And Benchmarks?
Pipelines And Benchmarks fits situations like: anomalib benchmark pipelines; tiled ensemble workflows; advanced pipeline orchestration helpers while keeping experimental execution paths explicit.
How do I install Pipelines And Benchmarks in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks in VectorSpaceLab/AREX-Skill) into .claude/skills/pipelines-and-benchmarks in your project. Claude Code loads it when a task matches its description.
How do I install Pipelines And Benchmarks in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a codex`. Or copy the skill folder (skills/repositories/repo-skills/anomalib/sub-skills/pipelines-and-benchmarks in VectorSpaceLab/AREX-Skill) into .agents/skills/pipelines-and-benchmarks in your project. Codex loads it when a task matches its description.
Can I use Pipelines And Benchmarks 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 VectorSpaceLab/AREX-Skill --skill pipelines-and-benchmarks -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pipelines-and-benchmarks, .gemini/skills/pipelines-and-benchmarks, .github/skills/pipelines-and-benchmarks and .opencode/skills/pipelines-and-benchmarks in your project.
What does Pipelines And Benchmarks need to run?
Going by SKILL.md and its folder, Pipelines And Benchmarks needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Pipelines And Benchmarks 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 Pipelines And Benchmarks 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 Pipelines And Benchmarks use?
Pipelines And Benchmarks is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Pipelines And Benchmarks use?
About 952 tokens (SKILL.md is roughly 3.8k 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 5.4k tokens, read only when the agent opens those files.
What are the alternatives to Pipelines And Benchmarks?
Skills that share tags, products or a category with Pipelines And Benchmarks: Crawl4AI Web Scraping (smallnest/goclaw, 599 stars), Glue 09 10 Migration (aws-samples/aws-glue-samples, 1.5k stars), Migrate Glue Devendpoint To Interactive Sessions (aws-samples/aws-glue-samples, 1.5k stars) and Dbt Databricks PR Ready (databricks/dbt-databricks, 380 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Pipelines And Benchmarks?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 331 GitHub stars. The repository holds 157 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.