---
name: enrollment-record-discrepancy-analyzer
description: "Analyzes roster or enrollment datasets to flag rows with missing links, email mismatches, status anomalies, duplicate records, and orphaned entries, then produces a prioritized discrepancy report with row-level findings, severity ratings, and remediation actions. Use when auditing enrollment data, reconciling rosters against source systems, or investigating why records aren't syncing between platforms."
argument-hint: "[roster-data] [expected-schema]"
allowed-tools: ["Read"]
---

# Enrollment Record Discrepancy Analyzer

## Purpose
Scan a roster or enrollment dataset for the specific data-integrity failures that break program operations — missing enrollment links, email mismatches, invalid statuses, duplicates, and orphaned records — and return a row-level discrepancy report with severity, root-cause hypothesis, and a concrete fix for each flagged entry. Turns a messy export into a triaged cleanup queue.

## When to Use
Invoke this skill when the user asks to:
- Audit an enrollment or roster export for data errors before an import or sync
- Investigate why learners aren't appearing, receiving access, or syncing to a downstream system
- Reconcile a roster against a source-of-truth system (CRM, SIS, LMS, billing)
- Find duplicate, orphaned, or status-mismatched enrollment records
- Clean up a spreadsheet or CSV of enrollments before onboarding a new cohort
- Validate roster data quality on a recurring basis (weekly/monthly hygiene check)

## Inputs Required
Before starting, collect:
- **Roster data**: The dataset to analyze — pasted rows, CSV, or table (headers included)
- **Expected schema**: The columns that should exist and which are required (e.g., learner_id, email, enrollment_link, status)
- **Valid status values**: The allowed enrollment statuses (e.g., Active, Pending, Completed, Withdrawn)
- **Unique identifier**: Which field(s) should be unique per record (learner_id, email, or a composite)
- **Source-of-truth reference** (optional): A second dataset to reconcile against, if available
- **Email domain rules** (optional): Expected domains or format constraints (e.g., corporate domain, no free-mail)
- **Volume expectation** (optional): Approximate expected row count, to catch truncated or over-large exports

If valid status values or the unique identifier aren't provided, infer them from the data and state the assumption explicitly before flagging.

## Instructions

### Step 1: Schema & Structural Validation
Before checking values, confirm the shape of the data:
- Confirm all expected columns are present; flag any missing or unexpected columns
- Check header casing/naming inconsistencies that would break a mapped import
- Report total row count vs. expected volume — flag if truncated (suspiciously low) or bloated
- Detect fully blank rows and trailing empty rows from export artifacts
- If the schema is malformed enough to make row-level analysis unreliable, report that first and stop

### Step 2: Missing & Broken Link Detection
Identify records that cannot function downstream:
- **Missing required fields**: Any row where a required column (enrollment_link, learner_id, email) is empty or whitespace-only
- **Malformed links**: Enrollment URLs that don't match expected pattern (missing protocol, truncated, placeholder text like "N/A" or "TBD")
- **Orphaned records**: Rows referencing a program/course/cohort ID that doesn't appear anywhere valid in the dataset
- Tag each with the specific field(s) at fault

### Step 3: Email Validation & Mismatch Detection
Catch the most common access-breaking error:
- **Format errors**: Missing @, missing domain, double @, leading/trailing spaces, non-standard characters
- **Domain rule violations**: Emails outside allowed domains (if rules provided) or free-mail addresses where corporate is expected
- **Internal mismatches**: Same learner_id appearing with two different emails, or same email tied to two different learner_ids
- **Cross-source mismatches**: If a source-of-truth reference is provided, flag emails that differ between roster and reference for the same identifier (case-insensitive comparison, but flag case-only differences separately)

### Step 4: Status Anomaly Analysis
Validate the enrollment lifecycle field:
- **Invalid values**: Any status not in the allowed set (or inferred set) — including casing/spacing variants like "active " vs "Active"
- **Blank statuses**: Required-but-empty status fields
- **Logical contradictions**: Status conflicts with other fields (e.g., "Completed" with no completion date, "Active" with a withdrawal date, "Withdrawn" but still linked to an active cohort)
- **Stale states**: "Pending" records older than a reasonable threshold if a date field is available

### Step 5: Duplicate & Uniqueness Analysis
Find records that violate the unique key:
- Group rows by the unique identifier and flag any identifier appearing more than once
- Distinguish **exact duplicates** (all fields identical — safe to dedupe) from **conflicting duplicates** (same key, different values — require manual resolution)
- For conflicting duplicates, list the specific fields that differ so the operator knows what to reconcile

### Step 6: Severity Rating & Triage
Assign each finding a severity so the operator knows what to fix first:
- **Critical**: Record cannot function — missing required field, malformed email, broken enrollment link, conflicting duplicate on the unique key
- **High**: Record functions but is wrong — status contradiction, cross-source email mismatch, invalid status value
- **Medium**: Record works but is risky — domain rule violation, stale pending status, case-only email difference
- **Low**: Cosmetic — trailing whitespace, casing inconsistency, exact duplicate

### Step 7: Remediation & Summary
- For each flagged row, provide a specific fix action (not "review this" — say what to change it to when determinable)
- Produce a summary table: count of findings by category and by severity
- Surface the top 3 systemic issues (patterns affecting many rows) with a root-cause hypothesis (e.g., "12 email mismatches all trace to a bulk import that lowercased corporate domains")

## Output Format
- **Structure**: Executive summary → discrepancy counts table → row-level findings table → systemic issues → remediation checklist
- **Length**: 700–1,100 words, scaling with dataset size and finding count
- **Format**: Structured document with a findings table (Row / Identifier / Category / Severity / Issue / Fix Action) plus prose for summary and systemic issues
- **Sections**: Summary → Counts by Category & Severity → Row-Level Findings Table → Top 3 Systemic Issues → Prioritized Remediation Checklist
- **Tone**: Data operations analyst — precise, unemotional, action-first; every flag names the exact field and the exact fix

## Quality Checklist
Before delivering output, verify:
- [ ] Every flagged row cites the specific field(s) at fault — no vague "bad data" flags
- [ ] Every finding has a severity rating from the defined four-level scale
- [ ] The counts table totals reconcile with the number of rows in the findings table
- [ ] Duplicates are split into exact vs. conflicting — not lumped together
- [ ] Any inferred assumptions (status set, unique key) are stated explicitly before flagging begins
- [ ] Each remediation action specifies what to change, with the corrected value when determinable

## Notes
- The single most common access-breaking issue is email mismatch — check Step 3 findings first even when other severities are present
- When a source-of-truth reference is provided, treat the reference as authoritative and describe mismatches as "roster differs from source," not the reverse
- Case-only and whitespace-only differences are low severity but high volume — grouping them into a single systemic finding keeps the report readable
- If the dataset exceeds what can be analyzed exhaustively, analyze in full where possible and clearly state the row range covered rather than silently sampling

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