Regulatory Templates - Gate 1: Placeholder Mapping (Post Gate 0)

Overview

UPDATED: Gate 1 now maps placeholders from Gate 0 template to data sources. NO structure creation, NO logic addition.

Parent skill: regulatory-templates

Prerequisites:

• Context from regulatory-templates-setup
• Template base from regulatory-templates-setup

Output: Mapping of placeholders to backend data sources

---

Foundational Principle

Field mapping errors compound through Gates 2-3 and into production.

Gate 1 is the foundation of regulatory template accuracy:

• snake_case conversion: Python/Django ecosystem standard (PEP 8) - mixed conventions cause maintenance nightmares
• Data source prefixes: BACEN audits require data lineage traceability - "where did this value come from?"
• Interactive validation: No dictionary = no ground truth - user approval prevents assumption errors
• Confidence thresholds: Quality gates prevent low-confidence mappings from reaching production
• Dictionary checks: Consistency across team, audit trail for regulatory reviews

Every "shortcut" in Gate 1 multiplies through downstream gates:

• Skip snake_case → Gate 3 templates have mixed conventions → maintenance debt
• Skip prefixes → Gate 2 cannot trace data sources → debugging nightmares
• Auto-approve mappings → Gate 2 validates wrong assumptions → compliance violations
• Skip optional fields → Gate 1 fails confidence threshold → rework loops
• Lower thresholds → Low-confidence fields reach Gate 3 → production errors

Technical correctness in Gate 1 = foundation for compliance in production.

---

When to Use

Called by: regulatory-templates skill after Gate 0 template structure copy

Purpose: Map each placeholder to its data source - structure already defined in Gate 0

---

NO EXCEPTIONS - Technical Requirements Are Mandatory

Gate 1 field mapping requirements have ZERO exceptions. Every requirement exists to prevent specific failure modes.

Common Pressures You Must Resist

Pressure	Your Thought	Reality
Speed	"camelCase works, skip conversion"	PEP 8 violation creates maintenance debt. 30 min now vs 75+ min debugging later
Simplicity	"Prefix is verbose, omit it"	BACEN audits require data lineage. Implicit resolution = debugging nightmares
Efficiency	"AUTO-approve obvious mappings"	No dictionary = no ground truth. "Obvious" assumptions cause compliance violations
Pragmatism	"Skip optional fields"	Confidence calculated across ALL fields. 64% coverage = FAIL
Authority	"75% confidence is enough"	Threshold erosion: 75% → 70% → 60%. LOW confidence fields = high-risk mappings
Experience	"I memorized these, skip dictionary"	Memory is fallible. 1-min check prevents 20-40 min error correction

Technical Requirements (Non-Negotiable)

snake_case Conversion:

• ✅ REQUIRED: Convert ALL field names to snake_case
• ❌ FORBIDDEN: Use camelCase, PascalCase, or mixed conventions
• Why: Python/Django PEP 8 standard, grep-able patterns, maintenance

Data Source Prefixes:

• ✅ REQUIRED: {{ midaz_onboarding.organization.0.legal_document }}
• ❌ FORBIDDEN: {{ organization.legal_document }}
• Why: Data lineage traceability, multi-source disambiguation, audit compliance

Interactive Validation:

• ✅ REQUIRED: AskUserQuestion for EACH field mapping
• ❌ FORBIDDEN: Auto-approve HIGH confidence fields
• Why: No dictionary = no ground truth, user provides domain knowledge

Confidence Threshold:

• ✅ REQUIRED: Overall confidence ≥ 80%
• ❌ FORBIDDEN: Lower threshold or skip fields
• Why: Quality gate for Gate 2/3, prevents low-confidence mappings in production

Dictionary Check:

• ✅ REQUIRED: Check ~/.claude/docs/regulatory/dictionaries/ first
• ❌ FORBIDDEN: Skip check and use memory
• Why: Consistency, audit trail, error prevention

The Bottom Line

Shortcuts in field mapping = errors in production regulatory submissions.

Gate 1 creates the foundation for Gates 2-3. Technical correctness here prevents compliance violations downstream.

If you're tempted to skip ANY requirement, ask yourself: Am I willing to debug production BACEN submission failures caused by this shortcut?

---

Rationalization Table - Know the Excuses

Every rationalization below has been used to justify skipping requirements. ALL are invalid.

Excuse	Why It's Wrong	Correct Response
"camelCase works fine in Django"	PEP 8 violation, maintenance debt, inconsistent conventions	Convert ALL to snake_case
"Prefix is verbose and ugly"	Audit trail required, multi-source disambiguation critical	Prefix ALL fields
"HIGH confidence = obvious, no approval needed"	No dictionary = no ground truth, assumptions fail	Ask approval for EACH field
"Optional fields don't affect compliance"	Confidence calculated across ALL fields, 64% = FAIL	Map ALL fields
"75% is close to 80%, good enough"	Threshold erosion, LOW confidence = high risk	Research to ≥80%
"I know these mappings by heart"	Memory fallible, experience creates overconfidence	Check dictionary first
"Everyone knows where organization comes from"	Implicit tribal knowledge, new team members lost	Explicit beats implicit
"User approval wastes their time"	User provides domain knowledge we lack	Interactive validation mandatory
"Conversion is unnecessary busywork"	Dismissing requirements without understanding cost	Technical correctness prevents debt
"This is simple, process is overkill"	Simple tasks accumulate into complex problems	Follow workflow completely

If You Find Yourself Making These Excuses

STOP. You are rationalizing.

The requirements exist to prevent these exact thoughts from causing errors. If a requirement seems "unnecessary," that's evidence it's working - preventing shortcuts that seem reasonable but create risk.

---

CRITICAL CHANGE

❌ OLD Gate 1 (Over-engineering)

• Created complex field mappings
• Added transformation logic
• Built nested structures
• Result: 90+ line templates

✅ NEW Gate 1 (Simple)

• Takes template from Gate 0
• Maps placeholders to single data source
• NO structural changes
• Result: <20 line templates

🔴 CRITICAL: NAMING CONVENTION - SNAKE_CASE STANDARD

ALL field names MUST be converted to snake_case:

• ✅ If API returns legalDocument → convert to legal_document
• ✅ If API returns taxId → convert to tax_id
• ✅ If API returns openingDate → convert to opening_date
• ✅ If API returns naturalPerson → convert to natural_person
• ✅ If API returns tax_id → keep as tax_id (already snake_case)

ALWAYS convert camelCase, PascalCase, or any other convention to snake_case.

🔴 CRITICAL: DATA SOURCES - ALWAYS USE CORRECT DOMAIN PREFIX

REFERENCE: See /docs/regulatory/DATA_SOURCES.md for complete documentation.

Available Data Sources (Reporter Platform):

Data Source	Descrição	Entidades Principais
midaz_onboarding	Dados cadastrais	organization, account
midaz_transaction	Dados transacionais	operation_route, balance, operation
midaz_onboarding_metadata	Metadados cadastro	custom fields
midaz_transaction_metadata	Metadados transações	custom fields

Field Path Format: {data_source}.{entity}.{index?}.{field}

Examples: {{ midaz_onboarding.organization.0.legal_document }} | {{ midaz_transaction.operation_route.code }} | {{ midaz_transaction.balance.available }}

Common Mappings: CNPJ→organization.0.legal_document, COSIF→operation_route.code, Saldo→balance.available

RULE: Always prefix with data source! ❌ {{ organization.legal_document }} → ✅ {{ midaz_onboarding.organization.0.legal_document }}

---

Gate 1 Process

STEP 1: Check for Data Dictionary (FROM/TO Mappings)

HIERARCHICAL SEARCH - Dictionary first, Interactive Validation second:

Dictionary Path: ~/.claude/docs/regulatory/dictionaries/{category}-{code}.yaml

Step	If Dictionary EXISTS	If Dictionary NOT EXISTS
1	Load YAML, use field_mappings	Query MCP: mcp__apidog_midaz/crm__read_project_oas()
2	Apply transformations	Analyze schemas, SUGGEST mappings (preserve casing)
3	Use existing mappings	AskUserQuestion for EACH field (user approval required)
4	Return	Create dictionary with APPROVED mappings only
5	—	Save to dictionary path for future use

Dictionary contains: field_mappings (FROM→TO), transformations, pitfalls, validation_rules

---

🔴 CRITICAL: INTERACTIVE VALIDATION FOR TEMPLATES WITHOUT DICTIONARY

Data Dictionaries Location

Dicionários de dados disponíveis em: ~/.claude/docs/regulatory/dictionaries/

Consulte os dicionários existentes antes de iniciar o mapeamento de campos.

---

Interactive Validation Process (MANDATORY for templates without dictionary)

Step	Action	Details
A	Discover Fields	Read regulatory spec (XSD/PDF) → Extract ALL required fields + types + formats
B	Query API Schemas	mcp__apidog-midaz/crm__read_project_oas() → Extract available fields from both systems
C	Interactive Validation	For EACH field: AskUserQuestion with top 3-4 suggestions + "Skip" + "Other" (custom path)
D	Validate Transformations	If field needs transform: AskUserQuestion with options (e.g., slice:':8', "No transformation")
E	Generate Dictionary	Create YAML with APPROVED mappings only → Save to DICTIONARY_BASE_PATH/[template].yaml

---

AskUserQuestion Implementation for Field Mapping

CRITICAL: Use AskUserQuestion tool with these patterns:

Pattern	Question Format	Options
Field Source	Map '${field.name}' (${type}, ${required})?	Top 3 suggestions (with confidence %) + "Skip for now" + "Other" (auto)
Transformation	Transformation for '${field.name}'?	Suggested filters (with examples) + "No transformation" + "Other" (auto)
Batch Approval	Approve mapping for '${name}'? Suggested: ${path}, Confidence: ${%}	"Approve ✓" / "Reject ✗" (max 4 questions per batch)

Note: "Other" option automatically added by AskUserQuestion for custom input.

---

Complete Interactive Validation Flow

Process: Read spec → Query MCP schemas → For EACH field: AskUserQuestion → Process response → Track approved/skipped

Response Type	Action
"Skip"	Add to skippedFields (resolve in Gate 2)
Custom input ("Other")	Add with approved_by: "user_custom_input", confidence: 100
Suggested option	If needs transformation: ask for filter (slice, floatformat, etc.) → Add with approved_by: "user_selection"

Output: { approvedMappings: [...], skippedFields: [...] }

---

Validation Rules for User Input

Valid path patterns for custom input:

Source	Pattern	Example
midaz_onboarding	midaz_onboarding.(organization|account).N.field	midaz_onboarding.organization.0.legal_document
midaz_transaction	midaz_transaction.(operation_route|balance|operation).field	midaz_transaction.balance.available
crm	crm.(holder|alias).field	crm.holder.document
metadata	(midaz|crm).entity.metadata.field	midaz.account.metadata.branch

Validation: If path doesn't match patterns → warn user but allow (may be valid custom path).

NAMING CONVENTION IN FIELD DISCOVERY

CRITICAL: ALWAYS CONVERT TO SNAKE_CASE!

API Returns	Map As	✅/❌
legalDocument	organization.legal_document	✅
taxId / TaxID	organization.tax_id	✅
openingDate	organization.opening_date	✅
legalDocument	organization.legalDocument	❌ NEVER

Search patterns help FIND fields. Once found, CONVERT TO SNAKE_CASE!

Hierarchical Search Strategy

CRITICAL: Convert ALL discovered fields to snake_case!

Step	Action	Priority Paths
1	Query MCP schemas	mcp__apidog_crm/midaz__read_project_oas()
2	Search CRM first	holder.document, holder.name, holder.type, holder.addresses., holder.contact., holder.naturalPerson., holder.legalPerson., alias.bankingDetails., alias.metadata.
3	Search Midaz second	account.name, account.alias, account.metadata., account.status, transaction.metadata., balance.amount, organization.legalDocument
4	Check metadata	crm.holder/alias.metadata., midaz.account/transaction.metadata.
5	Mark as uncertain	If not found → document searched locations + suggest closest matches + indicate confidence

Confidence Scoring System

Level	Score	Criteria
HIGH (90-100%)	Base(30) + Name(25) + System(25) + Type(20) + Validation(20)	Exact name match, type matches, primary system, validation passes, simple/no transform
MEDIUM (60-89%)	Base(30) + partial matches	Partial name or pattern match, compatible type needs transform, secondary system, some uncertainty
LOW (30-59%)	Base(30) only	Synonym/fuzzy match, significant transform, metadata only, cannot validate

Formula: Score = Base(30) + NameMatch(0-25) + SystemMatch(0-25) + TypeMatch(0-20) + ValidationMatch(0-20)

Component	Values
NameMatch	exact=25, partial=15, pattern=5
SystemMatch	primary=25, secondary=15, metadata=5
TypeMatch	exact=20, compatible=10, needs_transform=5
ValidationMatch	validated=20, partial=10, cannot_validate=0

Validation with Examples

Process: Fetch sample → Apply transformation → Validate format

Pattern	Regex
CPF	/^\d{11}$/
CNPJ	/^\d{14}$/
CNPJ_BASE	/^\d{8}$/
DATE_BR	/^\d{2}\/\d{2}\/\d{4}$/
DATE_ISO	/^\d{4}-\d{2}-\d{2}$/
PHONE_BR	/^\+?55?\s?\(?\d{2}\)?\s?\d{4,5}-?\d{4}$/
CEP	/^\d{5}-?\d{3}$/

Example: CNPJ Base: "12345678000190" → slice:':8' → "12345678" → /^\d{8}$/ → ✓ valid (+20 confidence)

Agent Dispatch

Dispatch: Task(subagent_type: "finops-analyzer", model: "opus")

Pre-dispatch: Check dictionary at ~/.claude/docs/regulatory/dictionaries/{category}-{code}.yaml

Mode	Condition	Instructions
Dictionary Mode	File exists	USE dictionary data ONLY. NO MCP calls. Validate mappings.
MCP Discovery Mode	File missing	Query MCP APIs → Suggest mappings → AskUserQuestion for EACH → Create dictionary with APPROVED only

Prompt includes: Template info, dictionary status/content (if exists), snake_case requirement, validation steps, output format

CRITICAL REQUIREMENTS:

✅ DO	❌ NEVER
Check dictionary FIRST	Skip dictionary check
MCP only if no dictionary	Call MCP when dictionary exists
AskUserQuestion for ALL mappings	Auto-approve without asking
Save APPROVED mappings only	Save unapproved guesses
Validate all transformations	Guess field mappings

Report Output: dictionary_status, field_mappings (code, name, required, source, transformation, confidence, validated, examples), validation_summary (total, mapped, coverage%, avg_confidence)

COMPLETION STATUS: COMPLETE, INCOMPLETE, or NEEDS_DISCUSSION

---

Capture Gate 1 Response

Response structure:

Section	Fields
Template Info	template_name, regulatory_standard, authority, submission_frequency, submission_deadline
Field Counts	total_fields, mandatory_fields, optional_fields
Discovery Summary	crm_fields_available, midaz_fields_available, metadata_fields_used, unmapped_fields
Field Mappings (per field)	field_code, field_name, required, type, format, mappings_found[], selected_mapping, confidence_score, confidence_level, reasoning, transformation, validation_passed, status
Uncertainties (per field)	field_code, field_name, mappings_attempted[], best_match, doubt, suggested_resolution
Confidence Summary	high/medium/low_confidence_fields, overall_confidence
Compliance Risk	LOW/MEDIUM/HIGH (based on confidence levels)
Documentation Used	official_regulatory URL, implementation_reference URL, regulatory_framework

---

Documentation Sources

Official Regulatory Sources (SOURCE OF TRUTH)

---

Red Flags - STOP Immediately

If you catch yourself thinking ANY of these, STOP and re-read the NO EXCEPTIONS section:

Skip Patterns

• "Skip snake_case conversion for..."
• "Omit prefix for obvious fields"
• "Use camelCase this time"
• "Mixed conventions are fine"
• "Dictionary check is ceremony"

Partial Compliance

• "Convert only mandatory fields"
• "Prefix only ambiguous fields"
• "Auto-approve HIGH confidence"
• "Map only mandatory fields"
• "75% is close enough to 80%"

Experience-Based Shortcuts

• "I memorized these mappings"
• "I know where this comes from"
• "We've done this 50 times"
• "The pattern is obvious"
• "Dictionary won't exist anyway"

Justification Language

• "Unnecessary busywork"
• "Verbose and ugly"
• "Wasting user time"
• "Process over outcome"
• "Being pragmatic"
• "Close enough"
• "Everyone knows"

If You See These Red Flags

1. Acknowledge the rationalization ("I'm trying to skip snake_case")
2. Read the NO EXCEPTIONS section (understand why it's required)
3. Read the Rationalization Table (see your exact excuse refuted)
4. Follow the requirement completely (no modifications)

Technical requirements are not negotiable. Field mapping errors compound through Gates 2-3.

---

Pass/Fail Criteria

PASS Criteria

• ✅ COMPLETION STATUS: COMPLETE
• ✅ 0 Critical gaps (unmapped mandatory fields)
• ✅ Overall confidence score ≥ 80%
• ✅ All mandatory fields mapped (even if LOW confidence)
• ✅ < 10% of fields with LOW confidence
• ✅ Dynamic discovery via MCP executed
• ✅ Documentation was consulted (both official and implementation)
• ✅ CRM checked first for banking/personal data

FAIL Criteria

• ❌ COMPLETION STATUS: INCOMPLETE
• ❌ Critical gaps exist (mandatory fields unmapped)
• ❌ Overall confidence score < 60%
• ❌ > 20% fields with LOW confidence
• ❌ Documentation not consulted
• ❌ MCP discovery not performed
• ❌ Only checked one system (didn't check CRM + Midaz)

---

State Tracking

Status	Output Fields
PASS	STATUS: PASSED, FIELDS: total/mandatory, UNCERTAINTIES: count, COMPLIANCE_RISK, NEXT: Gate 2, EVIDENCE: docs consulted + all mandatory mapped
FAIL	STATUS: FAILED, CRITICAL_GAPS: count, HIGH_UNCERTAINTIES: count, NEXT: Fix gaps, BLOCKERS: Critical mapping gaps

---

Critical Validations

Ensure these patterns are followed:

• Use EXACT patterns from Lerian documentation
• Apply filters like slice, floatformat as shown in docs
• Follow tipoRemessa rules: "I" for new/rejected, "S" for approved only
• Date formats must match regulatory requirements (YYYY/MM, YYYY-MM-DD)
• CNPJ/CPF formatting rules must be exact

---

Output to Parent Skill

Return to regulatory-templates: { gate1_passed: bool, gate1_context: {...}, uncertainties_count: N, critical_gaps: [], next_action: "proceed_to_gate2" | "fix_gaps_and_retry" }

---

Common Issues and Solutions

Issue	Solution
Documentation not accessible	Try alternative URLs or cached versions
Field names don't match Midaz	Mark as uncertain for Gate 2 validation
Missing mandatory fields	Mark as Critical gap, must resolve
Format specifications unclear	Consult both Lerian docs and government specs

---

Dynamic Discovery Example

Finding "Agência" field for CADOC 4010:

Step	Action	Result
1	Pattern search	["branch", "agency", "agencia", "branch_code"]
2	Query CRM first	crm.alias.bankingDetails.branch ✓ (exact, 95%)
3	Query Midaz fallback	midaz.account.metadata.branch_code ⚠ (metadata, 45%)
4	Select highest	crm.alias.bankingDetails.branch (HIGH confidence)

Remember

1. CONVERT TO SNAKE_CASE - All fields must be snake_case (legal_document not legalDocument)
2. Use MCP for dynamic discovery - Never hardcode field paths
3. CRM first for banking/personal data - It has the most complete holder info
4. Official specs are SOURCE OF TRUTH - Regulatory requirements from government
5. Lerian docs show IMPLEMENTATION - How to create templates in their system
6. Template-specific knowledge is valuable - Always check for existing sub-skills
7. Confidence scoring is key - Always calculate and document confidence
8. Be conservative with mappings - Mark uncertain rather than guess
9. Capture everything - Gate 2 needs complete context with all attempted mappings
10. Reference both sources - Note official specs AND implementation examples
11. Risk assessment based on confidence - Low confidence = higher compliance risk

Important Distinction

⚠️ Regulatory Compliance vs Implementation

• WHAT (Requirements) = Official government documentation
• HOW (Implementation) = Lerian documentation examples
• When validating compliance → Use official specs
• When creating templates → Use Lerian patterns
• Never confuse implementation examples with regulatory requirements