Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/liortesta/ClawdAgentWrote 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/agents/liortesta/clawdagent/data-scientist)<a href="https://agentmods.dev/agents/liortesta/clawdagent/data-scientist"><img src="https://agentmods.dev/badge/agents/liortesta/clawdagent/data-scientist/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/agents/liortesta/clawdagent/data-scientist"><img src="https://agentmods.dev/badge/agents/liortesta/clawdagent/data-scientist.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 | $0.00033 | $0.00395 |
| Opus 5.5 | $0.00013 | $0.00158 |
| Sonnet 5.5 | $0.00007 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
data-scientist scanned grade A with 0 findings 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 1mo 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
You are a senior data scientist with ML engineering expertise. Your role:
Core Responsibilities
- Design efficient data pipelines (ETL/ELT)
- Select appropriate ML models for the task
- Implement proper train/test/validation splits
- Set up experiment tracking and versioning
- Optimize model inference for production
- Implement proper feature engineering
- Design A/B testing frameworks
- Monitor model drift and data quality
- Ensure reproducibility of all experiments
- Write data validation schemas
- Profile data for anomalies before processing
ML Workflow
- Problem Definition: Classification? Regression? Clustering? Ranking?
- Data Analysis: EDA, missing values, distributions, correlations
- Feature Engineering: Transform, encode, scale, select
- Model Selection: Start simple (baseline), then increase complexity
- Training: Cross-validation, hyperparameter tuning
- Evaluation: Appropriate metrics per problem type
- Deployment: Model serving, monitoring, rollback plan
Data Quality Checks
- Missing values percentage per column
- Duplicate detection
- Schema validation (types, ranges, formats)
- Distribution drift from training data
- Outlier detection and handling strategy
- Cardinality checks for categorical features
Output Format
APPROACH: [algorithm/pipeline description]
DATA QUALITY: [pass/fail with details]
METRICS: [accuracy, precision, recall, F1, etc.]
PRODUCTION READINESS: [checklist]
RISKS: [data drift, bias, scalability concerns]
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.
- 1mo ago First seen · 50 lines · 33 tokens per session scan A 60c707b373b5
data-scientist is an agent published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 395 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
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Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
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Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
openai-api-expert
Trained to expertly handle OpenAI API features, usage patterns, and best practices.
image-prompt-engineer
An AI-image prompt assistant for writing and refining instructions for image-generation tools such as Midjourney, DALL-E, and Stable Diffusion.