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/hamzaamjad/cursor-rulesWrote 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/rules/hamzaamjad/cursor-rules/205-datascience-repro)<a href="https://agentmods.dev/rules/hamzaamjad/cursor-rules/205-datascience-repro"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/205-datascience-repro/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/rules/hamzaamjad/cursor-rules/205-datascience-repro"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/205-datascience-repro.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 | $0.00000 | $0.00998 |
| Opus 5.5 | $0.00000 | $0.00399 |
| Sonnet 5.5 | $0.00000 | $0.00200 |
| Haiku 4.5 | $0.00000 | $0.00100 |
Grade A, and why
205-datascience-repro 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reproducibility Checklist
- Environment
- Pin all library versions (
requirements.txt,environment.yml,poetry.lock). - Record Python version (
python --version) and OS info inREADME.md. Use virtual environments.
- Pin all library versions (
- Randomness control
- Set seeds for all sources of randomness early in scripts/notebooks (e.g.,
random.seed(42),numpy.random.seed(42),torch.manual_seed(42)). Document the chosen seed value.
- Set seeds for all sources of randomness early in scripts/notebooks (e.g.,
- Data lineage
- Record source data locations and checksums (e.g., SHA256) in
READMEor logs. - Version control preprocessing scripts. Log script execution commands and arguments.
- Record source data locations and checksums (e.g., SHA256) in
- Config
- Store all hyperparameters and key configuration parameters in separate files (e.g., YAML, JSON,
.env) and commit them to version control. Do not hardcode configuration in scripts.
- Store all hyperparameters and key configuration parameters in separate files (e.g., YAML, JSON,
- Outputs
- Save model artefacts with metadata (git SHA, run timestamp, config file used).
- Log performance metrics to an experiment tracker (MLflow, WandB) or a version-controlled CSV/JSON file. Include examples of using MLflow and WandB for tracking.
- Ensure reproducibility by using Docker or similar containerization tools to encapsulate the environment.
- Notebooks
- Clear all cell outputs before committing
.ipynbfiles. - Consider exporting finalized notebooks to HTML or PDF for easier review without requiring execution.
- Clear all cell outputs before committing
- Ethics & compliance
- Verify data usage complies with privacy regulations (GDPR, CCPA, etc.).
- Anonymize or pseudonymize Personally Identifiable Information (PII) where required.
- Document known biases and limitations of the dataset/model in the
READMEor final report.
Usage: reference @datascience-repro when kicking off an analysis or review.
Validation
- Check (File Existence & Format): Verify
requirements.txtor similar exists and pins versions (e.g.,pandas==1.5.3notpandas>=1.5). CheckREADME.mdfor env info and seeds. Check for config files. Check for output logs/artifacts. - Check (Code Review): Look for hardcoded seeds, paths, or configs. Review data loading and preprocessing steps for lineage tracking. Review notebook diffs for cleared outputs. Check documentation for bias/limitation statements.
- Check (Tooling): Use experiment tracker UI (MLflow, WandB) to verify runs are logged with parameters and metrics.
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 · 89 lines · 0 tokens per session scan A 6184d13a66b0
205-datascience-repro is a cursor rule published in the GitHub repository hamzaamjad/cursor-rules (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 998 tokens. 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 2026-08-31.
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