ds-plan

name	ds-plan
description	REQUIRED Phase 2 of /ds workflow. Profiles data and creates analysis task breakdown. Requires docs/SPEC.md from Phase 1.

Skill: Data Science Planning (Data Profiling + Task Breakdown)

Announce: "Using ds-plan (Phase 2) to profile data and create task breakdown."

Contents

• The Iron Law of DS Planning
• What Plan Does
• Process
• Red Flags - STOP If You're About To
• Output

Planning (Data Profiling + Task Breakdown)

Profile the data and create an analysis plan based on the spec. Requires docs/SPEC.md from /ds-brainstorm first.

The Iron Law of DS Planning

SPEC MUST EXIST BEFORE PLANNING. This is not negotiable.

Before exploring data or creating tasks, you MUST have:

1. docs/SPEC.md with objectives and constraints
2. Clear success criteria
3. User-approved spec

If docs/SPEC.md doesn't exist, run /ds-brainstorm first.

Rationalization Table - STOP If You Think:

Excuse	Reality	Do Instead
"Data looks clean, profiling unnecessary"	Your data is never clean	PROFILE to discover issues
"I can profile as I go"	You'll miss systemic issues	PROFILE comprehensively NOW
"Quick .head() is enough"	Your head hides tail problems	RUN full profiling checklist
"Missing values won't affect my analysis"	They always do	DOCUMENT and plan handling
"I'll handle data issues during analysis"	Your issues will derail your analysis	FIX data issues FIRST
"User didn't mention data quality"	They assume YOU'LL check	QUALITY check is YOUR job
"Profiling takes too long"	Your skipping it costs days later	INVEST time now

Honesty Framing

Creating an analysis plan without profiling the data is LYING about understanding the data.

You cannot plan analysis steps without knowing:

• Your data's shape and types
• Your missing value patterns
• Your data quality issues
• Your cleaning requirements

Profiling costs you minutes. Your wrong plan costs hours of rework and incorrect results.

No Pause After Completion

After writing docs/PLAN.md, IMMEDIATELY invoke Phase 3: ~/.config/opencode/skills/ds-implement/SKILL.md

DO NOT:

• Ask "should I proceed with implementation?"
• Summarize the plan
• Wait for user confirmation (they approved SPEC already)
• Write status updates

The workflow phases are SEQUENTIAL. Complete plan → immediately start implement.

What Plan Does

DO	DON'T
Read docs/SPEC.md	Skip brainstorm phase
Profile data (shape, types, stats)	Skip to analysis
Identify data quality issues	Ignore missing/duplicate data
Create ordered task list	Write final analysis code
Write docs/PLAN.md	Make completion claims

Brainstorm answers: WHAT and WHY Plan answers: HOW and DATA QUALITY

Process

1. Verify Spec Exists

cat docs/SPEC.md  # verify-spec: read SPEC file to confirm it exists

If missing, stop and run /skill ds-brainstorm first.

2. Data Profiling

For multiple data sources: Use parallel background tasks if available in OpenCode.

Single Data Source (Direct Profiling)

MANDATORY profiling steps:

import pandas as pd

# Basic structure
df.shape                    # (rows, columns)
df.dtypes                   # Column types
df.head(10)                 # Sample data

# Summary statistics
df.describe()               # Numeric summaries
df.describe(include='object')  # Categorical summaries
df.info()                   # Memory, non-null counts

# Data quality checks
df.isnull().sum()           # Missing values per column
df.duplicated().sum()       # Duplicate rows

3. Identify Data Quality Issues

CRITICAL: Document ALL issues before proceeding:

• Missing values: Null counts, patterns.
• Duplicates: Exact and key-based.
• Outliers: Extreme or impossible values.
• Type issues: Strings in numeric columns, date parsing.

4. Create Task Breakdown

Break analysis into ordered tasks:

• Each task should produce visible output.
• Order by data dependencies.
• Include data cleaning tasks FIRST.

5. Write Plan Doc

Write to docs/PLAN.md:

Analysis Plan: [Analysis Name]

For OpenCode: Phase 3 implementation logic found in ~/.config/opencode/skills/ds-implement/SKILL.md.

Spec Reference

See: docs/SPEC.md

Data Profile

Source 1: [name]

• Shape: [rows] x [columns]
• Data Quality Issues: [list and strategy]

Task Breakdown

Task 1: Data Cleaning (required first)

• [description]
• Output: Clean DataFrame, log of rows removed

Task 2: [Analysis Step]

• Output: [specific output to verify]
• Dependencies: Task 1

Output Verification Plan

Define what output proves completion for each task.

Red Flags - STOP If You're About To:

• Skip data profiling.
• Ignore missing values.
• Start analysis immediately without characterizing data.

Output

Complete the plan when:

• docs/SPEC.md is understood.
• All data sources are profiled.
• docs/PLAN.md is written and tasks are ordered.

Phase Complete

IMMEDIATELY invoke Phase 3: /skill ds-implement