ds-plan

name	ds-plan
description	REQUIRED Phase 2 of /ds workflow. Profiles data and creates analysis task breakdown.
user-invocable	false
disable-model-invocation	true
hooks	{"PostToolUse":[{"matcher":"Agent","hooks":[{"type":"command","command":"python3 ${CLAUDE_PLUGIN_ROOT}/hooks/ds-post-subagent-guard.py"}]}],"PreToolUse":[{"matcher":"Agent","hooks":[{"type":"command","command":"python3 ${CLAUDE_PLUGIN_ROOT}/hooks/ds-pre-subagent-clear.py"}]},{"matcher":"Read","hooks":[{"type":"command","command":"python3 ${CLAUDE_PLUGIN_ROOT}/hooks/ds-read-after-subagent-guard.py"}]},{"matcher":"Grep","hooks":[{"type":"command","command":"python3 ${CLAUDE_PLUGIN_ROOT}/hooks/ds-read-after-subagent-guard.py"}]},{"matcher":"Glob","hooks":[{"type":"command","command":"python3 ${CLAUDE_PLUGIN_ROOT}/hooks/ds-read-after-subagent-guard.py"}]}]}

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

Context Monitoring

Level	Remaining Context	Action
Normal	>35%	Proceed normally
Warning	25-35%	Complete current profiling task, then trigger ds-handoff
Critical	≤25%	Immediately trigger ds-handoff — do not start new profiling

Planning (Data Profiling + Task Breakdown)

Profile the data and create an analysis plan based on the spec. Requires .planning/SPEC.md from /ds 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. .planning/SPEC.md with objectives and constraints
2. Clear success criteria
3. User-approved spec

If .planning/SPEC.md doesn't exist, run /ds 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

Drive-Aligned Framing

Skipping data profiling is NOT HELPFUL — your plan will be built on assumptions, not facts, and the user wastes hours on rework.

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.

Drive-Aligned Framing (Consequences)

Shortcut	Consequence
Skipping data profiling	You skipped profiling to save time. NaN columns and type mismatches crash the pipeline 3 tasks in — your shortcut tripled the work.
Thin task breakdown	You wrote vague tasks to move faster. The implementer guesses wrong — your speed created confusion.

No Pause After Completion

After writing .planning/PLAN.md and initializing .planning/LEARNINGS.md, IMMEDIATELY discover and load ds-implement: Read ${CLAUDE_SKILL_DIR}/../../skills/ds-implement/SKILL.md and follow its instructions.

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 .planning/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 .planning/PLAN.md	Make completion claims

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

Process

1. Verify Spec Exists

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

If missing, stop and run /ds first.

2. Data Profiling

For multiple data sources: Profile in parallel using background Task agents.

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
df.tail(5)                  # End of 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
df[col].value_counts()      # Distribution of categories

# For time series
df[date_col].min(), df[date_col].max()  # Date range
df.groupby(date_col).size()              # Records per period

Multiple Data Sources (Parallel Profiling)

**Pattern from oh-my-opencode: Launch ALL profiling agents in a SINGLE message.**

Use run_in_background: true for parallel execution.

When profiling 2+ data sources, launch agents in parallel:

# PARALLEL + BACKGROUND: All Task calls in ONE message

Task(
    subagent_type="general-purpose",
    description="Profile dataset 1",
    run_in_background=true,
    prompt="""
Profile this dataset and return a data quality report.

Dataset: /path/to/dataset1.csv

Required checks:
1. Shape: rows x columns
2. Data types: df.dtypes
3. Missing values: df.isnull().sum()
4. Duplicates: df.duplicated().sum()
5. Summary statistics: df.describe()
6. Unique value counts for categorical columns
7. Date range if time series
8. Memory usage: df.info()

Output format:
- Markdown table with column summary
- List of data quality issues found
- Recommendations for cleaning

Tools denied: Write, Edit, NotebookEdit (read-only profiling)
""")

Task(
    subagent_type="general-purpose",
    description="Profile dataset 2",
    run_in_background=true,
    prompt="""
[Same template for dataset 2]
""")

Task(
    subagent_type="general-purpose",
    description="Profile dataset 3",
    run_in_background=true,
    prompt="""
[Same template for dataset 3]
""")

After launching agents:

• Continue to other work (don't wait)
• Check status with /tasks command
• Collect results with TaskOutput when ready

# Collect profiling results
TaskOutput(task_id="task-abc123", block=true, timeout=30000)
TaskOutput(task_id="task-def456", block=true, timeout=30000)
TaskOutput(task_id="task-ghi789", block=true, timeout=30000)

Benefits:

• 3x faster profiling for 3 datasets
• Each agent focused on single source
• Results consolidated in main chat

3. Identify Data Quality Issues

CRITICAL: Document ALL issues before proceeding:

Check	What to Look For
Missing values	Null counts, patterns of missingness
Duplicates	Exact duplicates, key-based duplicates
Outliers	Extreme values, impossible values
Type issues	Strings in numeric columns, date parsing
Cardinality	Unexpected unique values
Distribution	Skewness, unexpected patterns

4. ETL Strategy Assessment (Conditional)

Triggers when profiling reveals ANY of:

• Total rows > 1M across all sources
• Multiple data sources requiring joins/merges
• Data sourced from remote databases (WRDS, SQL servers, APIs)

If triggered, assess these three dimensions before creating the task breakdown:

A. Filter Push-Down Strategy

The anti-pattern: Pull entire tables into memory, then filter in pandas/R/SAS.

AskUserQuestion(questions=[{
  "question": "Where should filtering happen for this data?",
  "header": "Filtering",
  "options": [
    {"label": "Database-level (Recommended)", "description": "SQL WHERE clauses filter at source. Only matching rows transfer. Required for >1M row tables."},
    {"label": "Application-level", "description": "Pull full dataset, filter in code. Only acceptable for small tables (<100K rows) or when database access is read-once."},
    {"label": "Hybrid", "description": "Coarse filter at database (date range, key columns), fine filter in code (complex logic, cross-table conditions)."}
  ],
  "multiSelect": false
}])

Document in PLAN.md: For each data source, specify WHERE the filtering happens and WHY.

B. Parallelism Assessment

The anti-pattern: Process years/groups sequentially when they're embarrassingly parallel.

Identify parallelizable dimensions from profiling:

• Time: year-by-year, month-by-month processing
• Groups: firm-by-firm, sector-by-sector processing
• Sources: independent data sources profiled/cleaned in parallel

AskUserQuestion(questions=[{
  "question": "How should parallelizable tasks be executed?",
  "header": "Parallelism",
  "options": [
    {"label": "Background Task agents (Recommended)", "description": "Spawn parallel Task agents for independent groups/years. Best for in-session work with Claude."},
    {"label": "SGE array jobs", "description": "Submit as array jobs to grid scheduler. Best for WRDS/HPC cluster workloads."},
    {"label": "Sequential", "description": "Process one at a time. Only when tasks have dependencies or parallelism isn't worth the overhead."}
  ],
  "multiSelect": false
}])

Document in PLAN.md: For each task, note if it can be parallelized, on what dimension, and the chosen execution method.

C. Intermediate Result Caching

The anti-pattern: Re-read and re-process the same large source file in every task.

If multiple tasks read from the same large source:

1. Task 1 reads and cleans the source → saves intermediate result
2. Tasks 2-N read from the intermediate result, not the raw source

AskUserQuestion(questions=[{
  "question": "What format should be used for intermediate results?",
  "header": "Cache format",
  "options": [
    {"label": "Parquet (Recommended)", "description": "Columnar, compressed, preserves dtypes. Best for tabular data. ~10x smaller than CSV."},
    {"label": "CSV", "description": "Universal, human-readable. Use when downstream tools require CSV or data is small."},
    {"label": "SQLite", "description": "Queryable intermediate storage. Best when downstream tasks need filtered reads from the same intermediate."},
    {"label": "No caching needed", "description": "Each task reads from a different source, or sources are small enough to re-read."}
  ],
  "multiSelect": false
}])

Document in PLAN.md: Data flow diagram showing which tasks produce intermediates, which consume them, and the storage format.

D. Incremental Scale-Up Strategy

The anti-pattern: Submit the full batch (21K documents, 50M rows, $500 API call) without testing at small scale first. One bad schema, wrong prompt, or misconfigured parameter = entire batch wasted.

This is TDD for ETL: fail at 10 items in minutes, not at 21,000 items in hours.

Triggers when ANY task involves:

• External API batch processing (Gemini, OpenAI, Bedrock, etc.)
• Irreversible operations (database writes, file transformations)
• Operations costing > $10 or > 30 minutes at full scale
• Processing > 500 items through any external service

For each expensive task, ask the user how to scale up:

AskUserQuestion(questions=[{
  "question": "How should we scale up testing for this batch/ETL operation?",
  "header": "Scale-up",
  "options": [
    {"label": "Full scale-up (Recommended)", "description": "4 stages: 10 → 100 → 1,000 → full. Maximum safety for large batches (>5,000 items)."},
    {"label": "Standard scale-up", "description": "3 stages: 10 → 100 → full. Good for medium batches (500-5,000 items)."},
    {"label": "Minimal scale-up", "description": "2 stages: 10 → full. Quick validation for small batches (<500 items) or low-cost operations."},
    {"label": "Custom stages", "description": "Define custom batch sizes and gate criteria for this specific pipeline."}
  ],
  "multiSelect": false
}])

Then define the plan:

1. Set stage sizes based on user choice and total items
2. Define gate criteria per stage — what must be true before scaling up:
   • Output schema/format matches expectations (non-empty, correct structure)
   • Success rate above threshold (≥90% for test, ≥95% for intermediate)
   • Spot-check: manually inspect N outputs for quality/correctness
   • Cost/time extrapolation is acceptable for next stage
3. Document in PLAN.md: Scale-up testing plan table for each expensive task.

ETL Strategy Section for PLAN.md

## ETL Strategy
<!-- Include this section when data > 1M rows or multiple sources -->

### Filter Strategy
| Source | Rows | Filter Location | Filter Columns | Justification |
|--------|------|-----------------|----------------|---------------|
| source1 | 5M | Database (SQL WHERE) | date, type | Too large for full pull |
| source2 | 50K | Application (pandas) | — | Small enough for full load |

### Parallelism Plan
| Task | Parallelizable? | Dimension | Method |
|------|----------------|-----------|--------|
| Task 1 | Yes | By year (2003-2023) | Background Task agents / SGE array |
| Task 2 | No | — | Sequential (depends on Task 1 output) |

### Data Flow
source1.csv → [Task 1: Clean] → clean_source1.parquet → [Task 2: Merge]
source2.csv → [Task 1: Clean] → clean_source2.parquet ↗
                                                        → [Task 3: Analyze] → results

### ETL Rationalization Table - STOP If You Think:

Excuse	Reality	Do Instead
"I'll just read the whole table, it's easier"	50M rows × 200 columns = OOM crash or 30-minute wait	Filter at source with SQL WHERE
"Sequential processing is simpler to write"	20 years × 5 minutes = 100 minutes vs 5 minutes parallel	Use background agents or SGE arrays
"I'll re-read the source in each task"	Re-parsing 5GB CSV five times wastes hours	Save intermediate parquet after first read
"Filtering in pandas is more flexible"	Pandas loads ALL rows before filtering — you've already paid the cost	Push coarse filters to database, fine filters to pandas
"The data isn't that big"	You just profiled it. Check the row count. If >1M, it IS that big.	Follow the ETL strategy, don't guess
"I'll optimize later if it's slow"	Later never comes. The pipeline runs once and everyone moves on.	Design efficient ETL NOW
"I'll just run the full batch, it's faster"	One bad schema = 21K wasted requests. One wrong prompt = hours of queue time for garbage.	Test at 10 first. Always.
"The API validates my input anyway"	APIs validate format, not correctness. Empty responses are "successful."	Verify output content, not just HTTP 200
"Testing a small batch takes too long to set up"	Setting up a 10-item test takes 5 minutes. Resubmitting 21K items takes hours.	Build the test batch into the pipeline

5. Identify Implementation Language

Checkpoint type: decision (user chooses approach — cannot auto-advance)

Before creating tasks, determine the implementation language for ETL and analysis:

AskUserQuestion(questions=[{
  "question": "What language will be used for data processing / ETL?",
  "header": "Language",
  "options": [
    {"label": "Python (Recommended)", "description": "pandas/polars in notebooks or scripts. Default for most analysis."},
    {"label": "SAS", "description": "SAS on WRDS grid (qsas/qsub). For large-scale WRDS ETL with hash merges and SGE parallelism."},
    {"label": "R", "description": "R scripts or notebooks. For statistical modeling."},
    {"label": "Mixed", "description": "SAS for ETL, Python/R for analysis. Common for WRDS pipelines."}
  ],
  "multiSelect": false
}])

If SAS or Mixed is selected:

1. Record Implementation Language: SAS (or Mixed: SAS ETL + Python analysis) in PLAN.md header
2. Load WRDS SAS enforcement (discover path first):Read ${CLAUDE_SKILL_DIR}/../../skills/wrds/references/sas-etl.md and follow its instructions.
3. All SAS tasks in the plan MUST include performance annotations:
   • Merge strategy: hash or sort-merge (with justification if sort-merge)
   • WHERE pattern: range-based date literals (document that no function-wrapped filters are used)
   • Parallelism: SGE array or sequential (with justification if sequential)
4. Add ## SAS Performance Constraints section to PLAN.md (see template below)

6. Create Task Breakdown

Break analysis into ordered tasks:

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

7. Write Plan Doc

Write to .planning/PLAN.md:

---
phase: ds-plan
status: completed
implements: [all requirement IDs from SPEC.md]
requires: [.planning/SPEC.md]
provides: [.planning/PLAN.md, .planning/LEARNINGS.md]
affects: [.planning/]
tags: [planning, data-profiling]
---

# Analysis Plan: [Analysis Name]

> **For Claude:** REQUIRED SUB-SKILL: Discover and load ds-implement for output-first verification:
>Read `${CLAUDE_SKILL_DIR}/../../skills/ds-implement/SKILL.md` and follow its instructions.
>
> **Delegation:** Main chat orchestrates, Task agents implement. Discover and load ds-delegate:
>Read `${CLAUDE_SKILL_DIR}/../../skills/ds-delegate/SKILL.md` and follow its instructions.

## Spec Reference
See: .planning/SPEC.md

## Data Profile

### Source 1: [name]
- Location: [path/connection]
- Shape: [rows] x [columns]
- Date range: [start] to [end]
- Key columns: [list]

#### Column Summary
| Column | Type | Non-null | Unique | Notes |
|--------|------|----------|--------|-------|
| col1 | int64 | 100% | 50 | Primary key |
| col2 | object | 95% | 10 | Category |

#### Data Quality Issues
- [ ] Missing: col2 has 5% nulls - [strategy: drop/impute/flag]
- [ ] Duplicates: 100 duplicate rows on [key] - [strategy]
- [ ] Outliers: col3 has values > 1000 - [strategy]

### Source 2: [name]
[Same structure]

## Task Breakdown

### Task 1: Data Cleaning (required first)
- Handle missing values in col2
- Remove duplicates
- Fix data types
- Output: Clean DataFrame, log of rows removed

### Task 2: [Analysis Step]
- Input: Clean DataFrame
- Process: [description]
- Output: [specific output to verify]
- Dependencies: Task 1

### Task 3: [Next Step]
[Same structure]


For each task, define what output proves completion:
 Task 1: "X rows cleaned, Y rows dropped"
 Task 2: "Visualization showing [pattern]"
 Task 3: "Model accuracy >= 0.8"


<!-- Include when any source > 1M rows or multiple sources require joins -->


| Source | Rows | Filter Location | Filter Columns | Justification |
|--------|------|-----------------|----------------|---------------|


| Task | Parallelizable? | Dimension | Method |
|------|----------------|-----------|--------|


[source] → [task] → [intermediate] → [task] → [output]



Every PLAN.md with data processing MUST include an ASCII flowchart showing data sources, transformations, and outputs with annotations (FILTER/PARALLEL/CACHE):

Example: source.csv ──→ [Task 1: Clean] ──→ clean.parquet ──→ [Task 2: Analyze] ──→ results.csv FILTER: SQL WHERE CACHE: parquet PARALLEL: disabled (rows: 5M → 3M) (rows: 3M) (join key unique)

This flowchart IS the specification. If PLAN.md narrative and flowchart disagree, the flowchart wins.

### Scale-Up Testing Plan
<!-- Include when any task involves batch APIs, irreversible operations, or >500 items through external services -->

| Task | Total Items | Stage 1 (test) | Stage 2 | Stage 3 | Gate Criteria |
|------|-------------|-----------------|---------|---------|---------------|
| Batch extraction | 21,000 | 10 | 100 | 1,000 | ≥95% success, schema valid, non-empty responses |
| DB write | 5M rows | 100 | 1,000 | — | No constraint violations, row counts match |

## Implementation Language
[Python / SAS / R / Mixed]

<!-- If SAS or Mixed, include this section: -->
## SAS Performance Constraints
> **For Claude:** REQUIRED: Load SAS ETL enforcement before writing ANY SAS code:
>Read `${CLAUDE_SKILL_DIR}/../../skills/wrds/references/sas-etl.md` and follow its instructions.
> Validate ALL SAS code against the SAS Code Validation Checklist in the WRDS skill.

### Per-Task SAS Annotations
| Task | Merge Strategy | WHERE Pattern | Parallelism |
|------|---------------|---------------|-------------|
| Task 1 | Hash (lookup < 500K rows) | BETWEEN date literals | SGE array by year |
| Task 2 | Sort-merge (both tables > 50M) | No date filter | Sequential (single output) |

## Reproducibility Requirements
- Random seed: [value if needed]
- Package versions: [key packages]
- Data snapshot: [date/version]

Red Flags - STOP If You're About To:

Action	Why It's Wrong	Do Instead
Skip data profiling	Your data issues will break your analysis	Always profile first
Ignore missing values	You'll corrupt your results	Document and plan handling
Start analysis immediately	You haven't characterized your data	Complete profiling
Assume your data is clean	Never assume, you must verify	Run quality checks
Pull entire tables without WHERE clauses	OOM on large data, wastes time/memory	Filter at database level for >1M row sources
Process years sequentially	Embarrassingly parallel = free speedup	Use background agents or SGE arrays
Re-read same source in multiple tasks	Redundant I/O multiplies runtime	Save intermediate results after first read
Submit full batch without test batch	One bad schema/prompt = entire batch wasted	Plan scale-up testing stages for expensive operations

Output

Complete the plan when:

• Read and understand .planning/SPEC.md
• Profile all data sources (shape, types, stats)
• Document data quality issues
• Define cleaning strategy for each issue
• Assess ETL strategy (if data > 1M rows or multiple sources)
• Order tasks by dependency
• Define output verification criteria
• Write .planning/PLAN.md
• Initialize .planning/LEARNINGS.md
• Pass Exit Gate
• Confirm ready for implementation

Initialize LEARNINGS.md

After writing .planning/PLAN.md, create .planning/LEARNINGS.md:

---
phase: ds-implement
status: in_progress
implements: []
requires: [.planning/PLAN.md]
provides: [analysis outputs]
affects: []
deviations: {r1: 0, r2: 0, r3: 0, r4: 0}
tags: [implementation, data-quality]
---

# Analysis Learnings: [Analysis Name]

## Data Quality Pipeline
[To be populated during implementation]

## Key Findings
[To be populated during implementation]

This file is populated by ds-implement as tasks complete. Initializing it here ensures the file exists before implementation begins.

Exit Gate: PLAN.md Verification

Checkpoint type: human-verify (PLAN.md content is machine-verifiable)

Before proceeding to ds-implement, execute this gate:

1. IDENTIFY: PLAN.md exists at .planning/PLAN.md
2. RUN: Read(".planning/PLAN.md")
3. READ: Verify it contains: Data Profile section, Task Breakdown section, Output Verification Plan
4. VERIFY: If any data source > 1M rows, confirm ETL Strategy section exists
5. CLAIM: Only proceed to ds-implement if ALL checks pass

Skipping this gate is NOT HELPFUL — an incomplete plan wastes the user's time when implementation hits missing sections. The 30 seconds this gate takes saves hours.

Phase Complete

After passing the exit gate, dispatch the plan reviewer before proceeding:

Phase 2: ds-plan -> PLAN.md written -> exit gate passed
  -> Dispatch ds-plan-reviewer subagent
  -> If APPROVED -> proceed to ds-implement
  -> If ISSUES_FOUND -> fix PLAN.md -> re-dispatch reviewer (max 5 iterations)

Step 1: Discover and load the plan reviewer skill: Read ${CLAUDE_SKILL_DIR}/../../skills/ds-plan-reviewer/SKILL.md and follow its instructions.

Step 2: Only after reviewer returns APPROVED, discover and load the next phase: Read ${CLAUDE_SKILL_DIR}/../../skills/ds-implement/SKILL.md and follow its instructions.

CRITICAL: Do not skip plan review. An unreviewed plan means subagents struggling with incomplete task definitions and missing verification steps.