Claude-skill-registry ds-delegate

Contents

• The Iron Law of Delegation
• Core Principle
• The Process
• Honesty Requirement
• Rationalization Prevention

<EXTREMELY-IMPORTANT> ## The Iron Law of Delegation

YOU MUST route EVERY ANALYSIS STEP THROUGH A TASK AGENT. This is not negotiable.

You MUST NOT:

• Write analysis code directly
• Run "quick" data checks
• Edit notebooks or scripts
• Make "just this one plot"

If you're about to write analysis code in main chat, STOP. Spawn a Task agent instead. </EXTREMELY-IMPORTANT>

Core Principle

Fresh subagent per task + output-first verification = reliable analysis

• Analyst subagent does the work
• Must produce visible output at each step
• Methodology reviewer checks approach
• Loop until output verified

When to Use

Called by

ds-implement

for each task in PLAN.md. Don't invoke directly.

The Process

For each task:
    1. Dispatch analyst subagent
       - If questions → answer, re-dispatch
       - Implements with output-first protocol
    2. Verify outputs are present and reasonable
    3. Dispatch methodology reviewer (if complex)
    4. Mark task complete, log to LEARNINGS.md

Step 1: Dispatch Analyst

Pattern: Use structured delegation template from

lib/references/delegation-template.md

Every delegation MUST include:

1. TASK - What to analyze
2. EXPECTED OUTCOME - Success criteria
3. REQUIRED SKILLS - Statistical/ML methods needed
4. REQUIRED TOOLS - Data access and analysis tools
5. MUST DO - Output-first verification
6. MUST NOT DO - Methodology violations
7. CONTEXT - Data sources and previous work
8. VERIFICATION - Output requirements

Use this Task invocation (fill in brackets):

Task(subagent_type="general-purpose", prompt="""
# TASK

Analyze: [TASK NAME]

## EXPECTED OUTCOME

You will have successfully completed this task when:
- [ ] [Specific analysis output 1]
- [ ] [Specific analysis output 2]
- [ ] Output-first verification at each step
- [ ] Results documented with evidence

## REQUIRED SKILLS

This task requires:
- [Statistical method]: [Why needed]
- [Programming language]: Data manipulation
- Output-first verification (mandatory)

## REQUIRED TOOLS

You will need:
- Read: Load datasets and existing code
- Write: Create analysis scripts/notebooks
- Bash: Run analysis and verify outputs

**Tools denied:** None (full analysis access)

## MUST DO

- [ ] Print state BEFORE each operation (shape, head)
- [ ] Print state AFTER each operation (nulls, sample)
- [ ] Verify outputs are reasonable at each step
- [ ] Document methodology decisions

## MUST NOT DO

- ❌ Skip verification outputs
- ❌ Proceed with questionable data without flagging
- ❌ Guess on methodology (ask if unclear)
- ❌ Claim completion without visible outputs

## CONTEXT

### Task Description
[PASTE FULL TASK TEXT FROM PLAN.md]

### Analysis Context
- Analysis objective: [from SPEC.md]
- Data sources: [list with paths]
- Previous steps: [summary from LEARNINGS.md]

## Output-First Protocol (MANDATORY)
For EVERY operation:
1. Print state BEFORE (shape, head)
2. Execute operation
3. Print state AFTER (shape, nulls, sample)
4. Verify output is reasonable

Example:
```python
print(f"Before: {df.shape}")
df = df.merge(other, on='key')
print(f"After: {df.shape}")
print(f"Nulls introduced: {df.isnull().sum().sum()}")
df.head()

Required Outputs by Operation

Operation	Required Output
Load data	shape, dtypes, head()
Filter	shape before/after, % removed
Merge/Join	shape, null check, sample
Groupby	result shape, sample groups
Model fit	metrics, convergence

If Unclear

Ask questions BEFORE implementing. Don't guess on methodology.

Output

Report: what you did, key outputs observed, any data quality issues found. """)

**If analyst asks questions:** Answer clearly, especially about methodology choices.

**If analyst completes task:** Verify outputs, then proceed or review.

## Step 2: Verify Outputs

Confirm before proceeding:
- [ ] Output files/variables exist
- [ ] Shapes are reasonable (no unexpected row loss)
- [ ] No silent null introduction
- [ ] Sample output matches expectations

Upon verification failure, re-dispatch analyst with specific fix instructions.

## Step 3: Dispatch Methodology Reviewer (Complex Tasks)

For statistical analysis, modeling, or methodology-sensitive tasks, dispatch a methodology reviewer:

Task(subagent_type="general-purpose", prompt=""" Review methodology for: [TASK NAME]

What Was Done

[SUMMARY FROM ANALYST OUTPUT]

Original Requirements

[FROM SPEC.md - especially any replication requirements]

CRITICAL: Do Not Trust the Report

The analyst may have:

• Reported success without actually running the code
• Cherry-picked output that looks correct
• Glossed over data quality issues
• Made methodology choices without justification

DO:

• Read the actual code or notebook cells
• Verify outputs exist and match claims
• Check for silent failures (empty DataFrames, all nulls)
• Confirm statistical assumptions were checked

Review Checklist

1. Is the statistical method appropriate for the data type?
2. Are assumptions documented and checked?
3. Is sample size adequate for conclusions?
4. Are there data leakage concerns?
5. Is the approach reproducible (seeds, versions)?

Confidence Scoring

Rate each issue 0-100. Only report issues >= 80 confidence.

Output Format

• APPROVED: Methodology sound (after verifying code/outputs yourself)
• ISSUES: List concerns with confidence scores and file:line references """)

## Step 4: Log to LEARNINGS.md

Append to `.claude/LEARNINGS.md` after each task:

```markdown
## Task N: [Name] - COMPLETE

**Input:** [describe input state]

**Operation:** [what was done]

**Output:**
- Shape: [final shape]
- Key findings: [observations]

**Verification:**
- [how you confirmed it worked]

**Next:** [what comes next]

Honesty Requirement

<EXTREMELY-IMPORTANT> **Claiming "analysis done" without output verification is LYING.**

When you say "Step complete", you are asserting:

• A Task agent ran the analysis
• Output was visible and verified by you
• You personally checked it (not just trusting the agent's word)
• Methodology reviewer approved (for statistical tasks)

If ANY of these didn't happen, you are not "summarizing" - you are LYING about the state of the analysis.

Your dishonest claims corrupt research. Your honest "investigating" maintains integrity. </EXTREMELY-IMPORTANT>

Rationalization Prevention

Recognize these thoughts as signals to stop and delegate instead:

Thought	Reality
"I'll just check the shape quickly"	You'll skip the output-first protocol. Delegate instead.
"It's just a simple merge"	Your merges fail silently. Delegate with verification.
"I already know this data"	Your knowing ≠ verified. Delegate anyway.
"The subagent will be slower"	You're wrong—wrong results are slower than slow results. Delegate.
"Just this one plot"	You're hiding data issues with one plot. Delegate.
"User wants results fast"	They want CORRECT results. You're optimizing for wrong metric. Delegate.
"Skip methodology review, it's standard"	Your "standard" assumptions often fail. Review anyway.
"Output looked reasonable"	You didn't verify—"looked reasonable" ≠ verified. Check numbers.

Red Flags

If you catch yourself thinking these, STOP immediately:

• "I can skip output verification this time"
• "I'll chain operations together, it's fine"
• "Unexpected nulls are probably okay"
• "Methodology review takes too long, skip it"
• "The merge probably worked"
• "Output-first protocol is overkill here"
• "I'll just summarize PLAN.md for the analyst" (STOP—provide full text)

When analyst produces no visible output:

• You must re-dispatch with explicit output requirements
• Treat this as a hard failure, not something to work around

When analyst fails a task:

• You must dispatch a fix subagent with specific instructions
• Don't fix it yourself in main chat—you'll pollute context and hide the real issue

Example Flow

Me: Implementing Task 1: Load and clean transaction data

[Dispatch analyst with full task text]

Analyst:
- Loaded transactions.csv: (50000, 12)
- Found 5% nulls in amount column
- "Should I drop or impute nulls?"

Me: "Impute with median, flag imputed rows"

[Re-dispatch with answer]

Analyst:
- Imputed 2,500 rows with median ($45.50)
- Added is_imputed flag column
- Final shape: (50000, 13)
- Sample output: [shows head with flag]

[Verify: shapes match, flag exists, no unexpected changes]

[Log to LEARNINGS.md]

[Mark Task 1 complete, move to Task 2]

Integration

This skill is invoked by

ds-implement

during the output-first implementation phase. After all tasks complete,

ds-implement

proceeds to

ds-review

.