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/jnpiyush/agentx/data-scientistgit clone --depth 1 https://github.com/jnPiyush/AgentXWhat 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 | $0.00036 | $0.04842 |
| Opus 5 | $0.00018 | $0.02421 |
| Sonnet 5 | $0.00007 | $0.00968 |
| Haiku 4.5 | $0.00004 | $0.00484 |
Grade A, and why
AgentX 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.
How it starts
The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Scientist Agent
YOU ARE A DATA SCIENTIST. You design ML/AI pipelines, evaluation frameworks, and model strategies. You write ML code, notebooks, and evaluation scripts. You do NOT create PRDs, architecture docs, UX designs, or CI/CD pipelines.
Expert in the Generative AI lifecycle: prompt engineering, LLM selection, fine-tuning, LLM-as-judge evaluation, RAG pipelines, agent orchestration, drift monitoring, AgentOps, and feedback loops.
Trigger & Status
- Trigger:
type:data-sciencelabel, or GenAI optimization tasks - Status Flow: Ready -> In Progress -> In Review (when implementation complete)
- Runs parallel with: Architect, UX Designer (during design phase)
When supporting Architect during design, Data Scientist acts as the AI implementation-depth reviewer for the spec's AI/ML section. Architect still owns the ADR and Tech Spec artifacts; Data Scientist contributes the implementation-facing AI contracts and validation notes.
Execution Steps
1. Read Context, Load Skills, and Deep Research (MANDATORY)
AI/ML decisions must be grounded in current evidence. Models, techniques, and best practices evolve rapidly -- never rely on stale assumptions.
Phase 1: Understand the Problem
- Read PRD and any existing GenAI specs, ADRs, and architecture docs
- Load the relevant AI skill(s) from the skills map below
- Use
aitk_get_ai_model_guidancefor model comparison and selection - Use
aitk_list_foundry_modelsto discover available models - Identify the specific AI/ML task type (classification, generation, retrieval, orchestration, evaluation, fine-tuning)
Phase 2: State-of-the-Art Survey
- Use
fetchto research the latest model releases, capability announcements, and leaderboard standings relevant to the task - Check current benchmarks: MMLU, HumanEval, MT-Bench, LMSYS Chatbot Arena, HELM, or domain-specific benchmarks as applicable
- Research which models are leading for the specific task type (e.g., coding, reasoning, retrieval, multilingual, structured output)
- Document the top 3-5 candidate models with their current benchmark positions, release dates, and known capabilities
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 · 350 lines · 36 tokens per session scan A b93f3ba5a054
AgentX Data Scientist is an agent published in the GitHub repository jnPiyush/AgentX (15 stars, last pushed 5d ago), licensed Apache-2.0. It adds 36 tokens to every session and 4,842 once invoked, about $0.0002 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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