Perforatedai Analyze
PerforatedAI/PerforatedAI
Analyze PerforatedAI training results and provide optimization recommendations.
Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV.
Install Anomalib Benchmarking
$ npx skills add open-edge-platform/anomalib --skill anomalib-benchmarking -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install open-edge-platform/anomalib anomalib-benchmarking --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/open-edge-platform/anomalib.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/anomalib-benchmarking .claude/skills/anomalib-benchmarking && 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 "anomalib-benchmarking" agent skill from https://github.com/open-edge-platform/anomalib/tree/main/.agents/skills/anomalib-benchmarking into .claude/skills/anomalib-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anomalib-benchmarking", 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/open-edge-platform/anomalib/tree/main/.agents/skills/anomalib-benchmarkingType 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 open-edge-platform/anomalib --skill anomalib-benchmarking -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install open-edge-platform/anomalib anomalib-benchmarking --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/anomalib.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/anomalib-benchmarking .agents/skills/anomalib-benchmarking && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "anomalib-benchmarking" agent skill from https://github.com/open-edge-platform/anomalib/tree/main/.agents/skills/anomalib-benchmarking into .agents/skills/anomalib-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anomalib-benchmarking", 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 open-edge-platform/anomalib --skill anomalib-benchmarking -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install open-edge-platform/anomalib anomalib-benchmarking --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/anomalib.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/anomalib-benchmarking .cursor/skills/anomalib-benchmarking && 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 "anomalib-benchmarking" agent skill from https://github.com/open-edge-platform/anomalib/tree/main/.agents/skills/anomalib-benchmarking into .cursor/skills/anomalib-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anomalib-benchmarking", 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/open-edge-platform/anomalib.git --path .agents/skills/anomalib-benchmarking--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 open-edge-platform/anomalib --skill anomalib-benchmarking -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install open-edge-platform/anomalib anomalib-benchmarking --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/anomalib.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/anomalib-benchmarking .gemini/skills/anomalib-benchmarking && 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 "anomalib-benchmarking" agent skill from https://github.com/open-edge-platform/anomalib/tree/main/.agents/skills/anomalib-benchmarking into .gemini/skills/anomalib-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anomalib-benchmarking", 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 open-edge-platform/anomalib anomalib-benchmarkingInstalls 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 open-edge-platform/anomalib --skill anomalib-benchmarking -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/open-edge-platform/anomalib.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/anomalib-benchmarking .github/skills/anomalib-benchmarking && 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 "anomalib-benchmarking" agent skill from https://github.com/open-edge-platform/anomalib/tree/main/.agents/skills/anomalib-benchmarking into .github/skills/anomalib-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anomalib-benchmarking", 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 open-edge-platform/anomalib --skill anomalib-benchmarking -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install open-edge-platform/anomalib anomalib-benchmarking --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/open-edge-platform/anomalib.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/anomalib-benchmarking .opencode/skills/anomalib-benchmarking && 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 "anomalib-benchmarking" agent skill from https://github.com/open-edge-platform/anomalib/tree/main/.agents/skills/anomalib-benchmarking into .opencode/skills/anomalib-benchmarking/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "anomalib-benchmarking", 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
anomalib-benchmarking- GitHub stars
- 6.2k
- Token cost
- ~1k tokens
- SKILL.md length
- 352 words
- Files
- 2
- Skills in repo
- 17
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV.
- Comparing multiple models/datasets/categories in one sweep
- SKILL.md covers Code locations, Running it, Config structure and Where results go, plus 2 more sections
- Calls python
- Authoring/editing a benchmark config YAML
What it does
Anomalib Benchmarking is an agent skill from open-edge-platform/anomalib. Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV. Use when comparing multiple models/datasets/categories in one sweep, or authoring/editing a benchmark config YAML. Do not use for training a single model (see anomalib-training) or the tiled-ensemble pipeline (see anomalib-tiled-ensemble). For turning measured results into README/docs benchmark tables, see the benchmark-and-docs-refresh skill.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `evals/evals.json`).
It sits in AI & LLM Engineering, covering CSV and tabular files. The repository describes itself as: An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference. The licence is Apache-2.0.
When your agent uses it
- Comparing multiple models/datasets/categories in one sweep
- Authoring/editing a benchmark config YAML
- Training a single model (see anomalib-training)
- The tiled-ensemble pipeline (see anomalib-tiled-ensemble)
Example prompts
- “/anomalib-benchmarking”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit dc087d5. 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
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
Anomalib Benchmarking loads about 1k tokens when it runs. Until then it costs about 129 tokens; SKILL.md has 352 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); files beside SKILL.md are not scanned.
SKILL.md
The full file from open-edge-platform/anomalib at commit dc087d5, republished under its Apache-2.0 licence (© open-edge-platform). 352 words, ~1,046 tokens.
Download SKILL.mdSave it as
.claude/skills/anomalib-benchmarking/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- anomalib-benchmarking
- description
- Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV. Use when comparing multiple models/datasets/categories in one sweep, or authoring/editing a benchmark config YAML. Do not use for training a single model (see anomalib-training) or the tiled-ensemble pipeline (see anomalib-tiled-ensemble). For turning measured results into README/docs benchmark tables, see the benchmark-and-docs-refresh skill.
- license
- Apache-2.0
Using the Benchmarking Pipeline
The benchmarking pipeline runs a grid of model/dataset/category combinations end-to-end (train + test) and writes measured metrics to a CSV — use it to produce real, reproducible numbers rather than hand-editing benchmark tables.
Code locations
src/anomalib/pipelines/benchmark/pipeline.py—Benchmark: top-level pipeline; picksSerialRunnerorParallelRunnerbased on configured accelerators andtorch.cuda.device_count().src/anomalib/pipelines/benchmark/generator.py—BenchmarkJobGenerator: expands the config (includinggrid:entries) into individual jobs.src/anomalib/pipelines/benchmark/job.py—BenchmarkJob: runs one model/dataset combination, times it, and saves results.tools/experimental/benchmarking/benchmark.py— thin CLI wrapper aroundBenchmark.tools/experimental/benchmarking/sample.yaml— example config to copy from.
Running it
# Via the tools wrapper
python tools/experimental/benchmarking/benchmark.py --config tools/experimental/benchmarking/sample.yaml
# Via the anomalib CLI (registered pipeline subcommand)
anomalib benchmark --config tools/experimental/benchmarking/sample.yamlConfig structure
accelerator:
- cuda
- cpu
benchmark:
seed: 42
model:
class_path:
grid: [Padim, Patchcore]
data:
class_path: MVTecAD
init_args:
category:
grid:
- bottle
- capsuleAny field can use grid: [...] to sweep multiple values — the generator produces the Cartesian
product of every grid field as separate jobs (here: 2 models × 2 categories = 4 jobs). Non-grid
fields are held constant across all jobs. data.class_path / model.class_path follow the same
anomalib.data.* / anomalib.models.* resolution as everywhere else in the repo (see
anomalib-training).
Where results go
BenchmarkJob.save(...) writes one row per job into:
runs/benchmark/<timestamp>/results.csv(<timestamp> is generated when results are saved via BenchmarkJob.save(), e.g.
2026-08-24-10_30_00.) Each row includes the
model/dataset/category combination and the measured metrics — this is the file to consume when
building or refreshing README/docs benchmark tables.
There is also a separate, narrower helper tools/benchmark_mebin.py that writes to
results/mebin_benchmark.csv for a specific benchmarking use case — prefer the pipeline above unless
you specifically need that script's behavior.
Show full SKILL.md (137 more words)Show less
Gotchas
- A
gridsweep multiplies job count fast — check the Cartesian product size before launching a large sweep (e.g. 5 models × 10 categories = 50 full train+test runs). accelerator: [cuda, cpu]creates one runner per entry, so every model/category combination runs once per accelerator (doubling the total job count). This is not a device-pool selector — if you only want to benchmark on GPU, useaccelerator: [cuda].- Never hand-write or infer numbers into README/docs benchmark tables — always source them from a
results.csvproduced by an actual run of this pipeline.
Reviewer / self-check
- Config's
gridfields produce the intended, bounded set of jobs (no accidental huge sweep). -
model.class_path/data.class_pathvalues resolve to real exported classes. - Benchmark run completed and
runs/benchmark/<timestamp>/results.csvexists before citing numbers anywhere else. - Test reference:
tests/integration/pipelines/test_benchmark.pyfor how the pipeline is invoked programmatically if debugging job generation.
© open-edge-platform, 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 1 other file in .agents/skills/anomalib-benchmarking of open-edge-platform/anomalib.
- SKILL.md
- evals/evals.json
Open the folder on GitHubat commit dc087d5
Compare with similar skills
Anomalib Benchmarking 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 |
|---|---|---|---|---|---|---|
| Anomalib Benchmarking this skillopen-edge-platform/anomalib | 6.2k | — | ~1k | Automated safety check: Pass | Apache-2.0 | |
| Perforatedai AnalyzePerforatedAI/PerforatedAI | 237 | — | ~5.1k | Automated safety check: Pass | Apache-2.0 | |
| Codemie Analyticscodemie-ai/codemie-code | 294 | — | ~7.5k | Automated safety check: Pass | Apache-2.0 | |
| Perforatedai PlotPerforatedAI/PerforatedAI | 237 | — | ~1.6k | Automated safety check: Pass | Apache-2.0 | |
| Yolo Trainingfcakyon/claude-codex-settings | 1.2k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Extracting Clinical Entitiesmaziyarpanahi/openmed | 5.5k | — | ~1.9k | Automated safety check: Pass | Apache-2.0 |
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More from open-edge-platform/anomalib
All 17 skills in this repoAnomalib Adding A Model
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Adds a new anomaly-detection model to anomalib under src/anomalib/models/.
Anomalib Tiled Ensemble
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Runs and configures the anomalib tiled-ensemble pipeline, which trains/evaluates one model per image tile and merges results (with optional seam smoothing) for high-resolution anomaly detection.
Anomalib Training
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Trains an anomalib model on a dataset via the Python API or CLI, including training on a custom folder-structured dataset with the Folder datamodule.
Model Sample Image Export
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Export, validate, and publish model sample-result images into docs/source/images and reference them from README/docs pages.
UI Test Utils
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A skill your agent uses when writing or updating Anomalib Studio UI component or hook tests that need the shared render/renderHook helpers, React Router paths or parameters, React Query, theme…
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Related
Questions about Anomalib Benchmarking
What does Anomalib Benchmarking do?
Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV. Anomalib Benchmarking is an agent skill from open-edge-platform/anomalib. Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV.
When should I use Anomalib Benchmarking?
Anomalib Benchmarking fits situations like: comparing multiple models/datasets/categories in one sweep; authoring/editing a benchmark config YAML; training a single model (see anomalib-training); the tiled-ensemble pipeline (see anomalib-tiled-ensemble).
How do I install Anomalib Benchmarking in Claude Code?
Run `npx skills add open-edge-platform/anomalib --skill anomalib-benchmarking -a claude-code`. Or copy the skill folder (.agents/skills/anomalib-benchmarking in open-edge-platform/anomalib) into .claude/skills/anomalib-benchmarking in your project. Claude Code loads it when a task matches its description.
How do I install Anomalib Benchmarking in Codex?
Run `npx skills add open-edge-platform/anomalib --skill anomalib-benchmarking -a codex`. Or copy the skill folder (.agents/skills/anomalib-benchmarking in open-edge-platform/anomalib) into .agents/skills/anomalib-benchmarking in your project. Codex loads it when a task matches its description.
Can I use Anomalib Benchmarking 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 open-edge-platform/anomalib --skill anomalib-benchmarking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/anomalib-benchmarking, .gemini/skills/anomalib-benchmarking, .github/skills/anomalib-benchmarking and .opencode/skills/anomalib-benchmarking in your project.
What does Anomalib Benchmarking need to run?
Going by SKILL.md and its folder, Anomalib Benchmarking needs the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Anomalib Benchmarking 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 Anomalib Benchmarking 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. Review the folder before installing.
What licence does Anomalib Benchmarking use?
Anomalib Benchmarking 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 Anomalib Benchmarking use?
About 1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Anomalib Benchmarking?
Skills that share tags, products or a category with Anomalib Benchmarking: Perforatedai Analyze (PerforatedAI/PerforatedAI, 237 stars), Codemie Analytics (codemie-ai/codemie-code, 294 stars), Perforatedai Plot (PerforatedAI/PerforatedAI, 237 stars) and Yolo Training (fcakyon/claude-codex-settings, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Anomalib Benchmarking?
open-edge-platform (a GitHub organization) maintains it in open-edge-platform/anomalib, which has 6,230 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on October 8, 2026.
Source: open-edge-platform/anomalib on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.