Borrowing it
Nothing to install: this file belongs to ThalesGroup/agilab. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ThalesGroup/agilab/main/.claude/skills/agilab-code-statistics/SKILL.mdgit clone --depth 1 https://github.com/ThalesGroup/agilabWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/thalesgroup/agilab/agilab-code-statistics)<a href="https://agentmods.dev/skills/thalesgroup/agilab/agilab-code-statistics"><img src="https://agentmods.dev/badge/skills/thalesgroup/agilab/agilab-code-statistics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/thalesgroup/agilab/agilab-code-statistics"><img src="https://agentmods.dev/badge/skills/thalesgroup/agilab/agilab-code-statistics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.01266 |
| Opus 5.5 | $0.00022 | $0.00506 |
| Sonnet 5.5 | $0.00011 | $0.00253 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
agilab-code-statistics scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
paths = subprocess.check_output(["git", "ls-files"], text=True).splitlines() The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 156 lines · 56 tokens per session scan A 7f72e0d256aa
agilab-code-statistics is a skill published in the GitHub repository ThalesGroup/agilab (22 stars, last pushed today), with no licence file. It adds 56 tokens to every session and 1,266 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ml-expert
Expert-level machine learning, deep learning, model training, and MLOps. Use when the user mentions machine learning, deep learning, neural networks, MLOps, or data science, or when the task involves Machine Learning Fundamentals, Data Preparation, or Model Training.
mlops
MLOps engineering covering ML pipeline design, model versioning, experiment tracking, deployment strategies, drift detection, and monitoring for production ML systems with tools like MLflow, Kubeflow, and model registries.
rigorous-research
Conduct AI/ML research as a science - first-principles reasoning, mechanism-level hypotheses, falsifiable predictions, discriminative experiments, honest evaluation, and claims that survive hostile review. Use when starting or steering an AI/ML research project, deciding what to investigate next, choosing datasets…
mle-workflow
Production ML engineering workflow — data contracts, reproducible training, evaluation gates, deployment, and monitoring. Use when building, reviewing, or hardening ML systems beyond notebooks.
Evaluation
Frames model, prompt, and system evaluation as a reproducible experiment with baselines, datasets, and explicit metrics.
apache-spark-data-processing
Complete guide for Apache Spark data processing including RDDs, DataFrames, Spark SQL, streaming, MLlib, and production deployment.