analyst

name	analyst
type	skill
description	Support academic research data analysis with technology-agnostic principles — research-data immutability, a versioned/tested/reproducible transformation layer, statistical methodology, and self-documenting research. Use this skill for any computational research project with an empirical data pipeline. The skill enforces academicOps best practices for reproducible, transparent research with a collaborative single-step workflow. Tech-specific how-to (dbt, Streamlit, Python plotting/stats) lives in the aops-extras package.
category	instruction
triggers	["data analysis","research pipeline","empirical data","research data analysis"]
modifies_files	true
needs_task	true
mode	execution
domain	["academic","development"]
allowed-tools	Read,Grep,Glob,Edit,Write,Bash,Skill
version	2.1.0
permalink	skills-analyst-skill

Analyst

Taxonomy note: This skill provides tech-agnostic domain principles (HOW) for research data analysis. Technology-specific how-to (dbt, Streamlit, Python plotting/stats) lives in the aops-extras package skills. See [[aops-core/skills/remember/references/TAXONOMY.md]] for the skill/workflow distinction.

Overview

Support academic research data analysis through technology-agnostic principles: reproducible data pipelines, automated testing, self-documenting code, and fail-fast validation. The principles here hold regardless of which transformation engine or dashboard tool you use. When you have settled on specific tooling, pair this skill with the relevant aops-extras skill (dbt, streamlit, python-viz) for the concrete commands.

Core principle: Take ONE action at a time (generate a chart, update database, create a test), then yield to the user for feedback before proceeding.

🚨 CRITICAL: Research Data is Immutable

Source datasets, ground truth labels, experimental records, and research configurations are SACRED. NEVER modify, reformat, or "fix" them. If infrastructure doesn't support a format: HALT and report. Violations are scholarly misconduct.

Data directory separation: Local data files (data/) and build output directories (output/, _book/, etc.) MUST NOT overlap. Build tools clean their output directories — any data stored there will be destroyed. See [[instructions/research-documentation.md#data-directory-separation-critical]] for the full convention.

🚨 CRITICAL: Transformation Layer vs Presentation Layer

ALL data transformation happens in a versioned, tested, reproducible transformation layer. The presentation layer ONLY displays pre-computed data. Period.

This is non-negotiable for academic integrity, reproducibility, and auditability. It is a property of the architecture, not of any particular tool. (e.g. the transformation layer might be a dbt project, a SQL pipeline, or scripted notebooks under version control; the presentation layer might be a Streamlit dashboard, a static report, or a notebook viewer. See the aops-extras dbt and streamlit skills for those concrete implementations.)

Layer	Allowed	Prohibited
Transformation	ALL transformations, joins, aggregations, filtering, business logic	-
Presentation	Display, formatting, interactive filtering of PRE-COMPUTED data	Any operation that transforms, joins, aggregates, or applies logic

Why This Matters (Academic Integrity)

1. Reproducibility: Anyone can re-run the transformation layer and get identical results
2. Auditability: Transformation logic is version-controlled and testable
3. Transparency: Reviewers see exactly how data was processed
4. Testing: Tests in the transformation layer PROVE transformations work correctly

The Rule in Practice

Need a new metric? → Add it to the transformation layer with tests Need to filter data? → Pre-compute the filtered view in the transformation layer OR filter on EXISTING columns in the presentation layer (no new calculations) Need to join tables? → Do the join in the transformation layer Need aggregations? → Compute them in the transformation layer

Presentation Layer: Display ONLY

The presentation layer may:

• ✅ Read pre-computed outputs (SELECT * FROM precomputed_table)
• ✅ Filter on EXISTING columns (WHERE column = :user_selection)
• ✅ Format numbers, dates for display
• ✅ Create interactive widgets that filter existing data
• ✅ Render charts from pre-computed metrics

The presentation layer must NEVER:

• ❌ Aggregate (SUM(...) GROUP BY ... = transformation)
• ❌ Join (a.*, b.* FROM a JOIN b = transformation)
• ❌ Apply business logic (CASE WHEN ... END = transformation)
• ❌ Calculate derived metrics inline
• ❌ Apply any formula that changes the meaning of data

If You're Tempted to Transform in the Presentation Layer

STOP. Move the transformation into the transformation layer instead:

1. Add the transformation as a versioned model/script
2. Add tests proving it works
3. Build/run the transformation layer
4. THEN read the pre-computed output from the presentation layer

This takes more time. That's the point. Transformations deserve scrutiny.

Documentation Index

Instructions (_CHUNKS/)

• Investigation: [[instructions/data-investigation.md]], [[instructions/exploratory-analysis.md]]
• Research docs: [[instructions/research-documentation.md]] (REQUIRED), [[instructions/methodology-files.md]], [[instructions/methods-vs-methodology.md]], [[instructions/experiment-logging.md]]

References

[[references/context-discovery.md]], [[references/quick-reference-commands.md]]

Statistical Analysis (references/)

Start with [[references/statistical-analysis.md]] (complete guide). Also: [[references/test_selection_guide.md]], [[references/assumptions_and_diagnostics.md]], [[references/effect_sizes_and_power.md]], [[references/bayesian_statistics.md]], [[references/reporting_standards.md]].

Technology-Specific Skills (aops-extras)

The concrete how-to for particular tools lives in the aops-extras package, so it can be swapped for official/community-consensus skills:

• dbt — transformation-layer implementation (models, tests, marts).
• streamlit — presentation-layer implementation (display-only dashboards).
• python-viz — Python plotting & statistical-modelling libraries (matplotlib, seaborn, statsmodels). Use the python-dev skill for code standards.

When to Use This Skill

Invoke this skill when:

1. Working in computational research projects - An empirical data pipeline, analytical database, or transformation/presentation layer is present
2. User requests data analysis - "Analyze X", "Create a chart showing Y", "Explore the relationship between Z"
3. Building or updating dashboards - Presentation-layer visualization work (see the aops-extras streamlit skill for that engine)
4. Creating or modifying transformations - Transformation-layer pipeline work (see the aops-extras dbt skill for that engine)
5. Validating data quality - Adding tests, checking consistency

Key indicators in project structure:

• A version-controlled transformation layer (e.g. a dbt/models/ directory — staging, intermediate, marts)
• A presentation layer (e.g. a streamlit/ directory or dashboard .py files)
• data/warehouse.db or similar analytical database
• Academic research focus (papers, empirical analysis)

Workflow Decision Tree

START
│
├─ Is this a new analysis task?
│  ├─ YES → Go to: Context Discovery
│  └─ NO → Is context already loaded?
│     ├─ YES → Go to: Task Execution
│     └─ NO → Go to: Context Discovery
│
Context Discovery (REQUIRED FIRST STEP)
│
├─ Read project context files:
│  ├─ README.md (current directory + all parents to project root)
│  ├─ data/README.md (if exists)
│  └─ data/projects/[project-name].md (if exists)
│
├─ Identify project conventions:
│  ├─ Research questions
│  ├─ Data sources and access patterns
│  ├─ Existing transformation-layer models (list them)
│  ├─ Testing strategy
│  └─ Project-specific rules
│
└─ Proceed to: Task Execution
│
Task Execution
│
├─ What type of task?
│  ├─ Data access → Go to: Data Access Workflow
│  ├─ Visualization → Go to: Visualization Workflow
│  ├─ Transformation model → Go to: Transformation Model Workflow
│  ├─ Testing → Go to: Testing Workflow
│  └─ Exploration → Go to: Exploratory Analysis
│
└─ After completing ONE step:
   ├─ Report results to user
   ├─ Explain what was done
   └─ STOP and wait for user feedback

Context Discovery

CRITICAL FIRST STEP: Before any analysis work, automatically discover and read project context.

Required Context Files

1. Project README files

   • Current working directory README.md
   • All parent directories up to project root (e.g., papers/automod/, projects/buttermilk/)
   • Purpose: Understand research questions, conventions, project structure

2. Data README

   • data/README.md in the project
   • Purpose: Understand data sources, schema, access patterns

3. Project overview

   • data/projects/[project-name].md corresponding to current project
   • Purpose: Strategic context, goals, status

Context Extraction

From these files, identify:

• Research questions - What is this project investigating?
• Data sources - Where does data come from? (BigQuery, APIs, files?)
• Existing transformation models - What models already exist in the transformation layer?
• Conventions - Naming patterns, coding standards, project-specific rules
• Testing strategy - What tests exist? What quality expectations?
• Tools and technologies - Which transformation engine and presentation tool? (e.g. dbt + Streamlit — see the aops-extras skills.) DuckDB? PostgreSQL? Specific Python packages?

Example context discovery:

# List existing transformation-layer models (engine-specific; e.g. dbt)
ls -1 dbt/models/staging/*.sql dbt/models/marts/*.sql

# Check for presentation-layer apps (engine-specific; e.g. Streamlit)
ls -1 streamlit/*.py

# Understand project structure
cat README.md
cat data/README.md

The example commands above assume a dbt + Streamlit stack. For the concrete per-engine discovery commands, see the aops-extras dbt and streamlit skills.

After context discovery, summarize findings to user:

"I've reviewed the project context. This is a <research topic> project investigating <questions>. The transformation layer has <N> staging models and <M> mart models. I see existing work on <areas>. What would you like me to help with?"

Follow Data Access Workflow

🚨 CRITICAL RULE: ALL data access MUST go through the modelled transformation layer. NEVER query raw upstream sources directly.

🚨 REMINDER: If you need to transform data, that transformation MUST live in the transformation layer with tests. See "Transformation Layer vs Presentation Layer" above.

Decision Tree

Need data for analysis?
│
├─ Does required data exist in the modelled (mart) layer?
│  ├─ YES → Read it (e.g. `SELECT * FROM mart_name`)
│  │         └─ Done! Use this data in analysis.
│  │
│  └─ NO → Does it exist in staging models?
│     ├─ YES → Should this become a new mart?
│     │  ├─ YES → Go to: Transformation Model Workflow (create mart)
│     │  └─ NO → Use staging model for exploratory work
│     │
│     └─ NO → Data doesn't exist in the transformation layer yet
│        └─ Ask user: "Should I create a model for [data source]?"
│           ├─ YES → Go to: Transformation Model Workflow (create staging model)
│           └─ NO → Stop. Cannot proceed without a modelled source.

Prohibited Actions

❌ NEVER do this:

# Direct BigQuery query against raw source - PROHIBITED
df = client.query("SELECT * FROM bigquery.raw.cases").to_dataframe()

# Direct database query against raw schema - PROHIBITED
df = pd.read_sql("SELECT * FROM raw_schema.table", engine)

# Direct API call for analysis data - PROHIBITED
response = requests.get("https://api.example.com/data")

✅ ALWAYS do this:

# Query through the modelled layer - CORRECT
import duckdb

conn = duckdb.connect("data/warehouse.db")
df = conn.execute("SELECT * FROM fct_case_decisions").df()  # fct_* = a tested mart

Why This Matters

• Reproducibility: Queries are version-controlled in the transformation layer
• Data governance: The modelled layer is the single source of truth
• Quality: Data passes through a validated, tested transformation pipeline
• Consistency: All analysts use the same transformations

See: the aops-extras dbt skill for the dbt implementation of this policy.

Follow Transformation Model Workflow

Create or modify transformation-layer models following academicOps layered architecture. The layering below is engine-neutral; the aops-extras dbt skill gives the dbt-specific commands and file layout.

Quick Reference: Model Layers

1. Staging (stg_*) - Clean and standardize raw data (no business logic)
2. Intermediate (int_*) - Business logic transformations (can be ephemeral)
3. Marts (fct_*, dim_*) - Analysis-ready datasets (materialized)

Quick Reference: Workflow Pattern

1. Create model file → STOP, show user
2. Add documentation → STOP, show user
3. Add tests → STOP, show user
4. Run model and tests → STOP, report results

ALWAYS check for duplicate models before creating new ones.

See: the aops-extras dbt skill for complete workflow details and comprehensive patterns.

Follow Visualization Workflow

Create presentation-layer visualizations following the single-step collaborative pattern.

🚨 REMINDER: The presentation layer is DISPLAY ONLY. No transformations. See "Transformation Layer vs Presentation Layer" above.

For the detailed engine-specific workflow (structure, single-step patterns, examples), see the aops-extras streamlit skill.

Quick Reference: Presentation Pattern

Load data → STOP → Create chart → STOP → Add interactivity → STOP. One change at a time. See the aops-extras streamlit skill for engine-specific tips (e.g. Streamlit hot-reload).

Follow Testing Workflow

Add tests to validate data quality at every pipeline stage.

Testing Strategy

Use appropriate test type for the validation:

Test Type	Use For	Example
Schema tests	Column-level checks	not_null, unique, accepted_values
Singular tests	Multi-column logic	Date range validation, cross-table consistency
Package tests	Common patterns	Recency checks, multi-column uniqueness
Diagnostic models	Quality monitoring	Aggregated metrics for manual review

Follow Single-Step Testing Workflow

Step 1: Identify what to test

Review the model and ask:

• Which columns should never be null?
• Which columns should be unique?
• Are there accepted value lists?
• Any date range logic to validate?

STOP. Discuss with user which tests to add.

Step 2: Add schema tests (after user agrees on test plan)

The examples below use dbt's schema.yml syntax to illustrate the principle — column-level tests declared alongside the model. See the aops-extras dbt skill for the full engine-specific testing reference; any transformation engine should provide an equivalent declarative test layer.

# dbt/schema.yml (dbt example)
models:
  - name: stg_cases
    columns:
      - name: case_id
        tests:
          - unique
          - not_null
      - name: status
        tests:
          - accepted_values:
              values: ["pending", "reviewed", "published"]

STOP. Show to user.

Step 3: Run tests (after user approves test definitions)

dbt test --select stg_cases

STOP. Report results. If failures, discuss with user before fixing.

Step 4: Add singular test if needed (complex validation)

-- tests/assert_decision_dates_logical.sql
select
    case_id,
    submission_date,
    decision_date
from {{ ref('stg_cases') }}
where decision_date < submission_date

STOP. Show test SQL to user.

Step 5: Run singular test

dbt test --select test_name:assert_decision_dates_logical

STOP. Report results.

Test Severity

Use severity: warn for known issues or aspirational standards:

tests:
  - not_null:
      severity: warn # Don't fail build, just warn

Pipeline/Template Validation Tests

When testing LLM pipelines or templated content, validate substantive content not just error patterns:

• ✅ Check content length minimums (e.g., criteria block > 100 chars)
• ✅ Verify required sections exist AND have content
• ✅ Use position-based length for multiline content (regex .*? doesn't cross newlines)
• ❌ Don't just check for specific error strings - upstream bugs are unpredictable

See: the aops-extras dbt skill for complete engine-specific testing patterns.

Follow Data Investigation Workflow

When investigating data quality issues (missing values, unexpected patterns, join coverage), create REUSABLE investigation scripts in analyses/ directory. Never use throwaway one-liners for data investigation.

For complete workflow, script templates, and when to create investigation scripts, see [[instructions/data-investigation.md]]

Exploratory Analysis

When exploring data patterns and relationships, follow collaborative discovery process. Take one analytical step at a time, yielding to user after each finding.

For complete exploration workflow and anti-patterns, see [[instructions/exploratory-analysis.md]]

NOTE: For data quality issues (missing values, unexpected nulls), use Data Investigation Workflow instead.

Documentation Philosophy

Self-documenting work: Do NOT create separate analysis reports or random documentation files.

🚨 CRITICAL: Research projects must follow STRICT documentation structure. See [[instructions/research-documentation.md]] for complete requirements.

Required Documentation Structure

Research projects MUST maintain:

• README.md - Project overview and quick start
• METHODOLOGY.md - Research design and approach (see [[instructions/methodology-files.md]])
• methods/*.md - Technical implementation details (see [[instructions/methods-vs-methodology.md]])
• data/README.md - Data sources and schema
• Transformation-layer schema/docs - Model and column documentation (e.g. dbt/schema.yml)
• experiments/YYYYMMDD-description/ - Experimental work (see [[instructions/experiment-logging.md]])

Where Analysis Documentation Lives

1. Presentation-layer dashboards - Interactive exploration and validation (e.g. Streamlit)
2. Jupyter notebooks - Detailed analysis with inline markdown (in experiments/ if exploratory)
3. GitHub issues - Track analysis tasks and decisions
4. Code comments - Explain analytical decisions in transformation-layer models
5. Commit messages - Document why changes were made
6. Transformation-layer schema docs - Document model purposes and column meanings (e.g. dbt/schema.yml)
7. methods/*.md - Technical method specifications

Prohibited

❌ Create analysis_report.md]] or any random markdown files ❌ Createfindings_summary.docx` ❌ Proliferate documentation files without defined structure ❌ Leave documentation stale when code changes

✅ Follow strict structure defined in [[instructions/research-documentation.md]] ✅ Update documentation in SAME commit as code changes ✅ One source of truth for each piece of information

Collaborative Workflow Principles

One step at a time:

1. Perform ONE action (create chart, write model, run test)
2. Show results to user
3. Explain what was done and what it means
4. STOP and wait for user feedback
5. Proceed based on user direction

Never:

• Create multiple artifacts without checkpoints
• Make assumptions about next steps
• Implement complex workflows end-to-end without user input

Always:

• Explain options and ask for user preference
• Show intermediate results
• Yield control back to user frequently

Quick Reference

See [[references/quick-reference-commands.md]] for common data-pipeline and DuckDB commands. For engine-specific commands, see the aops-extras dbt and streamlit skills.