by eddyzzl
MARVIS-Agent: all-purpose credit risk agent for model development, validation, data processing, feature engineering, and strategy workflows.
# Add to your Claude Code skills
git clone https://github.com/eddyzzl/marvis-risk-agentGuides for using ai agents skills like marvis-risk-agent.
Last scanned: 6/23/2026
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"status": "PASSED",
"scannedAt": "2026-06-23T07:50:09.082Z",
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}See how marvis-risk-agent compares with popular alternatives.
marvis-risk-agent is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by eddyzzl. MARVIS-Agent: all-purpose credit risk agent for model development, validation, data processing, feature engineering, and strategy workflows. It has 473 GitHub stars.
Yes. marvis-risk-agent passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/eddyzzl/marvis-risk-agent" and add it to your Claude Code skills directory (see the Installation section above).
marvis-risk-agent is primarily written in Python. It is open-source under eddyzzl on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh marvis-risk-agent against similar tools.
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MARVIS is a local-first, governed credit-risk Agent platform—not a chatbot wrapped around a collection of scripts.
Describe the business outcome in natural language. MARVIS asks for missing files and definitions, builds a reviewable plan, pauses at responsibility gates, runs deterministic tools, and returns real datasets, evidence, models, strategy code, and reports.
flowchart LR
A["Describe the risk goal"] --> B["Agent clarifies inputs and definitions"]
B --> C["Validated workflow plan"]
C --> D{"Human confirmation<br/>where required"}
D --> E["Deterministic tools execute"]
E --> F["Evidence, artifacts, and reports"]
F --> G["Review, adopt, and iterate"]
Agent mode and the Manual Workbench share the same validated workflows, tools, schemas, and deterministic calculation kernels.
| A fragmented workflow | With MARVIS |
|---|---|
| Requirements, scripts, notebooks, screenshots, and reports live in different places. | The request, plan, execution evidence, decisions, and deliverables stay in one task. |
| Analysts manually reconnect data, feature, model, strategy, and report steps. | Governed workflows carry task ownership, data fingerprints, parameters, and artifacts forward. |
| AI can explain an answer, but it is hard to prove where the number came from. | Agent explanations point back to deterministic evidence and auditable memory references. |
| A result is copied into a document and loses its lineage. | Reports and code are generated from structured, versioned platform results. |
| Module | What MARVIS can do | Typical deliverables |
|---|---|---|
| Data processing | Register CSV/Excel files, infer schemas, profile data, align columns, propose and confirm joins, diagnose match rate, fan-out and row inflation, deduplicate explicitly, run governed transformations, and export safely. | Derived datasets, join evidence, profiling summaries, CSV/XLSX exports |
| Labels, samples, and features | Define bad labels from DPD plus observation and performance windows, check cohort maturity, design development/validation/OOT samples, calculate IV/KS/AUC/PSI/Lift/Coverage, bin numeric and categorical features, analyze correlation and collinearity, encode, impute, cap, and derive features. | Feature evidence, governed sample definitions, selected feature sets, Excel reports |
| Model development | Build binary, regression, and multiclass recipes; check modeling readiness; run governed reject inference with explicit assumptions and sample weights; prepare leakage-aware splits; resolve special values; select features; tune and train multiple recipes; compare experiments; select and calibrate a model; assess segment value; score datasets; and create monitoring handoffs. | Experiments, score evidence, model reports, scored data, PMML for supported recipes, model cards and handoff packages |
| Model validation | Scan Notebook, sample, PMML, and dictionary materials; score submitted PMML and calculate performance, stability, score consistency, binning, and stress evidence, while retaining legacy Notebook model-score comparison. One entry accepts 1–10 models with individual evidence and editable report drafts. Both manual and Agent-assisted paths remain available; two or more models also receive a batch summary workbook. | Structured validation evidence, individual Excel and Word reports, multi-model summary Excel |
| Strategy development | Design approval and risk populations; analyze variables and models; build and refine approval, reject, limit, pricing, and segmentation rules; use automatic and interactive trees, 2D Cross Matrix, 2D/3D cross-threshold search, scorecard cutoffs, and Voting/n-of-k combinations; compile Strategy Pools; measure impact and stability; validate on independent partitions; and adopt local versions through human gates. | Canonical strategies, backtests, ImpactCube evidence, Python/DuckDB SQL/JSON code, JSON/Markdown/XLSX/DOCX reports |
| Vintage and risk analysis | After confirming fields, units, cut-off dates, scenarios, and assumptions, run the selected VTG-terminal/annualized-bad-rate or profitability calculation. Run Standard Vintage and roll-rate as separate governed analyses with bounded cohort/segment evidence. | Audited risk-analysis Excel reports; structured Vintage and roll-rate evidence, charts, assumptions, conclusions, and red flags |
| Monitoring and portfolio analytics | Monitor score and feature stability, strategy thresholds and disposition, turn red monitoring evidence into a governed new-version task, and use implemented portfolio tools for flow rate, bucket migration, segments, concentration, Expected Loss, stability trends, and limit/pricing trade-offs. | Monitoring evidence, portfolio reports, migration tables, pricing matrices |
| Agent, governance, and memory | Clarify intent, instantiate validated workflows, enforce task ownership and confirmation gates, preserve hashes and provenance, and reuse bounded memories about preferences, field definitions, prior performance, and known pitfalls—with source and audit metadata. | Reviewable plans, evidence envelopes, audit history, traceable memory references |
The seven primary desktop entries are Data Processing, Feature Analysis, Risk Analysis, Portfolio Analysis, Model Development, Model Validation, and Strategy Development. Monitoring is integrated into model and strategy workflows. Add multiple models from the same Model Validation dialog: each model keeps its own contract, progress, evidence, and reports in the shared Agent workbench. Automatic review can confirm unambiguous contracts; conflicts still require a user decision. A single model never produces an empty batch summary. Portfolio now has a governed first-screen and conversational Agent journey with typed field, balance/EAD, segment, loss-state, LGD, horizon, and report gates; its verified public slice is currently the no-trend workflow, not a production portfolio-monitoring service. Label construction is exposed inside Data Processing rather than as a separate top-level task. See docs/capability-status.md for the canonical, layer-by-layer acceptance status; implementation is not treated as browser, real-data, sign-off, or production proof.
Strategy work is where MARVIS goes furthest beyond “AI assistance.” The current V2 workflow follows the real seven-step development process: