analytic-workbench

name	analytic-workbench
description	Use this skill for analytics and data-science workflow setup, exploratory analysis, notebook-first EDA, repo normalization for analysis projects, experiment comparison, AutoML, causal analysis, and promotion from ad hoc exploration into reusable pipelines. Trigger when the user asks for analysis best practices, how to structure an analytics repo, how to organize notebooks and runs, whether to use marimo or Quarto/qmd, how to handle experiment sweeps, how to compare models, or how to make analysis reproducible. Also trigger on phrases such as analytic workbench, EDA, exploratory analysis, notebook workflow, analytics pipeline, reproducible analysis, experiment sweep, hyperparameter comparison, comparison table, marimo, Quarto, qmd, Hamilton, sf-hamilton, dataflow, DAG driver, Hydra, DVC, Kedro, MLflow, AutoML, PyCaret, causal analysis, feature engineering, or model review.
metadata	{"author":"kundeng","version":"1.3.0"}

Analytic Workbench

Human-directed, AI-operated analysis. The AI computes; the human steers through a review surface (marimo notebook or Quarto doc). This is not a batch pipeline — intermediate findings are presented for redirection at every step.

Start Here

1. Read AGENTS.md or agents.md if present
2. If neither exists, create AGENTS.md and update CLAUDE.md (see below)
3. Inspect the repo before changing structure
4. Separate repo facts from agent assumptions
5. Separate frozen inputs from live pulls

Then establish per area: current mode, likely next mode, review surface, first boundary-sensitive change.

Fresh Repo Bootstrap

When starting on a repo with no AGENTS.md, create one from the initial prompt and project context. The file anchors all agents to the same workflow contract.

AGENTS.md should contain:

• Project purpose — one-paragraph summary derived from the user's request
• Workflow — state that this project uses the analytic workbench skill with modes probe → explore → experiment → operate
• Current mode — the mode established in the first plan declaration
• Review surface — marimo or qmd, chosen at first plan
• Conventions — module layout (src/<project>/analysis/), artifact layout (runs/, rawdata/)
• Steering rules — any project-specific constraints from the user's prompt (e.g., data sources, review expectations, domain context)

Then ensure CLAUDE.md exists and includes a pointer:

See AGENTS.md for project workflow conventions and current mode.

If CLAUDE.md already exists, append the pointer rather than overwriting. Update AGENTS.md at promote time when modes or conventions change.

Modes

A mode is a workflow shape, not what you compute. Different areas of work can be in different modes simultaneously. Every mode has two axes: what executes the computation and where the human reviews it.

Mode	Use when	Execution	Review surface	Read next
probe	first contact, uncertain framing	bare Python (naming discipline encouraged)	chat + inline figures; marimo optional	this file only
explore	work worth keeping formally	Hamilton Driver	marimo explore notebook or Quarto EDA doc	hamilton-guide.md, module-conventions.md, marimo-patterns.md
experiment	reruns, comparisons, sweeps	Hamilton + Hydra	marimo report app or Quarto report + comparison table	hydra-config.md
operate	scale, schedules, team handoff	+ orchestrator + MLflow	deployed marimo app or dashboard	mlflow-guide.md

Execution axis

Each mode adds to the previous — no rewrites.

• probe: throwaway code, direct fetches. No framework required, but Hamilton naming discipline is always encouraged — it costs nothing and eliminates rewriting at promote. Functions defined in marimo cells can become a Hamilton DAG on the fly via ad_hoc_utils.create_temporary_module() for instant visualization and selective execution. See hamilton-guide.md § Mode Bridge.
• explore: pure functions with Hamilton naming discipline. Hamilton Driver for automatic DAG resolution, selective execution, visualization, and @check_output validation.
• experiment: Hydra for config composition and --multirun sweeps. Hamilton @config.when for variant selection. .with_cache() to skip redundant upstream computation across reruns.
• operate: Hamilton runs inside orchestrator tasks (Airflow, Dagster). MLflow for experiment tracking. DVC for data versioning and remote storage.

Review axis

The review surface is what makes this a workbench, not a batch pipeline. Without it, there is no human steering loop.

• probe: findings presented in chat with inline figures and tables. The conversation is the review surface. If working in marimo, the notebook is both authoring surface and review surface from day one.
• explore: marimo notebook for interactive exploration (widgets, filters, drill-down) or Quarto doc for staged narrative EDA. The human opens the notebook, runs it, reads the AI's interpretation, and redirects. See marimo-patterns.md.
• experiment: marimo report app loads pre-computed artifacts from runs/, shows comparison tables, per-run drill-down, and figures. Or a Quarto report for stakeholder-facing narrative. See review-workflow.md.
• operate: deployed marimo app or dashboard for ongoing monitoring.

Surface choice rule: prefer marimo when the human needs to adjust filters, select runs, or explore interactively. Prefer Quarto when the output is a staged document, decision memo, or polished report.

Common mistakes

Mode	Mistake
probe	over-structuring early; but also: skipping naming discipline that makes promote free
explore	logic in cells instead of modules; no review surface; skipping Hamilton
experiment	adding Hydra before repeated runs exist; no comparison table
operate	adding Kedro/DVC before team handoff is real

All references are in references/.

Operators

Five operators drive all work, firing from conversational intent. Default rhythm: plan -> run -> review -> summary, with promote inserting when the current mode's wiring is no longer sufficient.

Each operator must produce a visible declaration.

plan

Set up or revise the workflow. Fires at start of substantive work or direction change.

Plan
- Area: ...          - Mode: ...
- Likely next mode: ...
- Primary surface: marimo | qmd | chat-only | other
- Review surface: ...
- First boundary-sensitive change: ...
- Steering docs read: ...

Add for ML/causal: Sampling strategy: ... and Target framing: binary | multiclass | regression | ranking | causal.

For probe: Area, Mode: probe, First probe, Review surface, Risk.

run

Execute the next planned step.

Run                          Run outcome
- Area: ...                  - What ran: ...
- Step: ...                  - Output: ...
- Expected output: ...       - Anomaly or caveat: ...

review

Sanity-check outputs before presenting — fires automatically, not on request. This is where the human sees intermediate results and can redirect.

Review
- Area: ...
- Artifacts reviewed: ...
- Findings: ...
- Caveats: ...
- Verdict: proceed | revise | back up to [phase]

The review declaration summarizes what the human will see in the review surface. At explore+, always point the human to the notebook or doc where they can inspect results interactively — do not make the chat summary the only artifact.

summary

Report status at natural stopping points.

EDA Update
- Area: ...       - What ran: ...
- Step: ...       - Outcome: ...
- Caveat: ...     - Next step: ...

promote

Advance to a stronger mode. This is where mode transitions happen — probe to explore when work is worth keeping, explore to experiment when reruns matter. Transitions can also emerge from user intent (e.g., asking for comparisons). Transitions are additive wiring, not rewrites.

Promote
- Area: ...             - From mode: ...
- To mode: ...          - What survives: ...
- What changes: ...

Core Workflow

The analytical spine, used within any mode:

Frame -> Acquire -> Profile -> Hypothesize -> Model or Analyze -> Review -> Promote

• Frame: question, decision, constraints, review surface choice
• Acquire: data access as workflow shape, not hidden setup
• Profile: coverage, missingness, slices, label shape — present to human
• Hypothesize: likely drivers before over-engineering
• Model or Analyze: execute, then present intermediate results for steering
• Review: findings early enough to redirect — the human inspects the review surface and gives feedback
• Promote: worth-keeping work into modules, config, runs, reports, and the runbook

Backtrack when stuck:

• Unusable data → Acquire or reframe
• All hypotheses fail → Profile with fresh slices
• Suspicious metrics → check leakage before celebrating
• Wrong assumption surfaced → Frame

Guardrails

• Prefer what is on disk over generic assumptions
• Do not silently change folder layout, artifact semantics, or execution path — breaks resumption and review
• Once structure exists, stop using throwaway snippets — they diverge from the notebook contract
• First live extraction is usually a run artifact, not a frozen input
• Do not split a phase across half-wired surfaces — hides state, blocks promotion
• Do not let AutoML choose the framing — it optimizes whatever metric you hand it
• Match sampling/CV to temporal, grouped, spatial, or event structure — naive splits leak and inflate
• Keep project knowledge in repo docs, not agent memory
• Present intermediate findings in the review surface — do not batch everything to the end

Acquisition and DAG Shape

explore+ follows references/module-conventions.md: pure functions in src/<project>/analysis/, function name = output name, parameter name = dependency name. These conventions are Hamilton's programming model — the Driver replaces manual call ordering with automatic DAG resolution.

Read references/hamilton-guide.md before writing the first explore-mode driver. Key capabilities by mode:

• explore: dr.execute(["node"]) for selective execution, dr.display_all_functions() for DAG review, @check_output for validation
• experiment: .with_cache() for rerun speed, @config.when for variant selection, Hydra feeds config dict to driver.Builder().with_config(cfg)
• operate: Hamilton materializers for I/O separation, Driver called inside orchestrator tasks

Notebooks read saved artifacts, not embed live fetch logic.

ML and Causal Rules

Read references/modeling-guardrails.md before modeling. Key rules:

• Frame target before any model runs — unexamined framing wastes downstream work
• Match sampling/CV to data structure
• AutoML as comparison accelerator after framing, not as problem framing
• Predictive importance is not causal effect — keep claims separate
• Interpretability artifacts for important outputs (references/interpretability.md)
• Causal design discipline: references/causal-analysis.md

Reference Guide

Read only what the current mode or task needs.

Reference	When to read	Key content
hamilton-guide.md	before explore driver	Driver, caching, @config.when, @check_output, visualization
module-conventions.md	before explore code	Pure functions, Hamilton-compatible naming, module layout
marimo-patterns.md	choosing or building review surface	marimo as frontend; explore notebooks; report apps; widgets
review-workflow.md	presenting results to human	Review before presenting; surface choice; self-review checklist
environment-setup.md	packaging, imports, deps	Real package setup; no editor-settings patches
hydra-config.md	entering experiment	Config composition, sweeps, experiment configs
artifact-strategy.md	repeated outputs, comparisons	Materialize clearly; do not mix frozen inputs with run outputs
modeling-guardrails.md	before modeling	Framing, sampling, leakage, validation discipline
dvc-guide.md	data versioning, remote storage	Add at operate for large data or team remotes
kedro-guide.md	team needs YAML data catalog	Optional at operate; Hamilton + Hydra cover most use cases

All references are in references/.

Runbook

The runbook (runbook.md at the repo root) is the human-readable reproduction guide. It is derived from project structure — configs, scripts, notebooks, runs — not maintained as a separate log.

Generate or update at promote time: milestones, handoff requests, or mode transitions. Rewrite stale sections rather than appending.

Contents:

1. Prerequisites — env setup, data acquisition, external tools
2. Pipeline overview — ASCII diagram of data flow
3. Numbered steps — one per major pipeline step, each with: the exact command, expected runtime, and what to inspect afterward (including expected results so the reader knows what "correct" looks like)
4. Configuration reference — table of config files and what they control
5. Key directories — paths, contents, git-tracked or not
6. Troubleshooting — known failure modes and fixes

The runbook ties per-run artifacts together: config.yaml says what params were used, metrics.json says what happened, the runbook says which command produced the run, what results to expect, and how to open the review surface.

If .aw/ bookkeeping is active, note the runbook's last-updated timestamp in .aw/status.json.

Bookkeeping

Maintain .aw/ when work continues across sessions:

.aw/
  status.json          # current state across all areas
  stages/
    <area-name>/       # one folder per area of work
      plan.md          # framing and plan revisions
      status.json      # machine-readable state
      review.md        # review notes and approvals

Create or advance a stage folder only on mode changes or user-requested checkpoints. Plan revisions, reruns, and parameter changes belong in the existing folder, not a new one.

Joining mid-project:

• .aw/status.json exists → authoritative
• stages/ but no root status → reconstruct from folders
• Hydra conf//multirun/ without .aw/ → likely experiment, confirm
• Notebook + modules, no config management → likely explore
• Nothing structured → probe