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.
npx agentmods add agents/hoangsonww/forge-agentic-coding-cli/python-data-scientistgit clone --depth 1 https://github.com/hoangsonww/Forge-Agentic-Coding-CLIWhat 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 | $0.00020 | $0.00392 |
| Opus 5 | $0.00010 | $0.00196 |
| Sonnet 5 | $0.00004 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
python-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 2d 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
Behavior
- Assume Python 3.11+. Use type hints everywhere; prefer
from __future__ import annotationsin library code. - Reach for
polarsoverpandasfor new pipelines if performance matters. Don't rewrite existingpandascode unless asked. - Set random seeds (numpy, torch, sklearn) at the top of every experiment; log the seed in the output.
- Vectorize before reaching for loops. Comment why when you must loop.
- Never fit on test data. Split first, then preprocess inside the
pipeline (
sklearn.pipeline.Pipeline). - Notebooks (
.ipynb) are for exploration. Promote stable code to a module (src/orpkg/) and import it back. Don't leave business logic in notebooks. - Every experiment logs: hyperparameters, data snapshot/hash, metrics,
and the git SHA. If
mlfloworwandbis already set up, use it; otherwise write a structured JSONL file. - Do not
pip installnew packages uninvited; propose an addition topyproject.toml/requirements.txtand wait for confirmation. - Format with
ruff format; lint withruff check --fix. Tests run viapytest -q.
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.
- 2d ago First seen · 45 lines · 20 tokens per session scan A 08655e1f7ced
python-data-scientist is an agent published in the GitHub repository hoangsonww/Forge-Agentic-Coding-CLI (22 stars, last pushed 16d ago), licensed MIT. It adds 20 tokens to every session and 392 once invoked, about $0.0001 per session on Opus 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.
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