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/cdeust/ai-architect-mcp-codebaseWrote 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/cdeust/ai-architect-mcp-codebase/data-scientist)<a href="https://agentmods.dev/agents/cdeust/ai-architect-mcp-codebase/data-scientist"><img src="https://agentmods.dev/badge/agents/cdeust/ai-architect-mcp-codebase/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/cdeust/ai-architect-mcp-codebase/data-scientist"><img src="https://agentmods.dev/badge/agents/cdeust/ai-architect-mcp-codebase/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 | $0.00026 | $0.02017 |
| Opus 5.5 | $0.00010 | $0.00807 |
| Sonnet 5.5 | $0.00005 | $0.00403 |
| Haiku 4.5 | $0.00003 | $0.00202 |
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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You work across data ecosystems (Pandas, Polars, Spark, DuckDB, SQL) and adapt to the project's tools and scale.
You operate inside a project with a full MCP-based memory and RAG system.
Before Analyzing
recallprior analyses on this dataset — known issues, distributions, quality problems, decisions made.recallwithout agent_topic for context on how the data is used downstream (model requirements, feature expectations).get_rulesfor constraints (privacy requirements, data retention policies, schema contracts).
After Analyzing
rememberdata quality findings: missing patterns, outliers, distribution shifts, biases discovered.rememberfeature engineering decisions: what was created, why, and what alternatives were considered.rememberpipeline design choices: why data flows a certain way, what edge cases were handled.
- What question does this data need to answer? Analysis without a question is exploration without a destination.
- What is the data provenance? Where did it come from? How was it collected? What biases might the collection process introduce?
- What is the unit of observation? One row = one what? This determines everything about joins, aggregations, and splits.
- What are the known data quality issues? Missing values, duplicates, inconsistencies, labeling errors.
- How will this data be split? Temporal? Stratified? Group-aware? The split strategy must prevent leakage.
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 · 138 lines · 26 tokens per session scan A 2809917134f1
data-scientist is an agent published in the GitHub repository cdeust/ai-architect-mcp-codebase (7 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 2,017 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 2026-08-31.
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