---
name: enrollment-discrepancy-detector
description: "Analyzes pasted roster or enrollment data across two or more source systems to flag mismatches, missing records, duplicate entries, and linkage anomalies, then outputs a prioritized discrepancy list with severity, likely root cause, and a recommended resolution for each. Use when reconciling enrollment records between an SIS, LMS, CRM, or payment system, investigating count mismatches, or preparing a roster for census/audit."
argument-hint: "[source-a-data] [source-b-data]"
allowed-tools: ["Read"]
---

# Enrollment Discrepancy Detector

## Purpose
Compare enrollment records across two or more source systems (SIS, LMS, CRM, billing, or a manual roster) and surface every mismatch, orphaned record, duplicate, and broken identity linkage — then rank each finding by severity so operations staff know exactly what to fix first before census, invoicing, or reporting deadlines.

## When to Use
Invoke this skill when the user asks to:
- Reconcile a student roster between two systems (e.g., SIS vs. LMS) that show different headcounts
- Investigate why an enrollment count doesn't match across a CRM and billing system
- Find students who are enrolled in one system but missing from another
- Detect duplicate enrollments, split records, or mismatched identity fields before a census or audit date
- Clean an enrollment export before uploading it to a downstream system or state reporting file

## Inputs Required
Before starting, collect:
- **Source A data**: Pasted roster/enrollment records from the first system (CSV, table, or delimited text) with a header row
- **Source B data**: Pasted records from the second system to compare against (same format expectation)
- **Match key(s)**: The field(s) used to link records across systems — e.g., student ID, email, or first+last+DOB
- **Systems named**: What each source represents (e.g., "Source A = Banner SIS, Source B = Canvas LMS")
- **Reconciliation goal**: What "correct" looks like — should the two match exactly, or is A the system of record?
- **Status filters**: Which statuses count as enrolled vs. dropped/withdrawn/waitlisted (if a status field exists)
- **Additional sources (optional)**: A third dataset (e.g., payment system) for three-way reconciliation

If no explicit match key is given, default to student ID; if that field is absent, fall back to a composite of email + last name and flag the reduced match confidence in the output.

## Instructions

### Step 1: Normalize and Profile Both Datasets
Before comparing, standardize the data to prevent false discrepancies:
- Trim whitespace, normalize case on IDs and emails (lowercase emails), and strip formatting from ID fields (leading zeros, hyphens, spaces)
- Standardize name fields for comparison (trim, collapse double spaces) without altering the displayed value
- Note the row count of each source and the count of unique match-key values
- Flag any rows with a **null or blank match key** immediately — these cannot be linked and become their own discrepancy category

Report the profile up front: Source A rows, Source B rows, unique keys in each, and any duplicate keys within a single source.

### Step 2: Perform the Cross-System Match
Link records using the match key. Bucket every record into one of four match states:
- **Matched cleanly**: Present in both sources with consistent identity fields
- **Present in A, missing in B**: Orphaned in Source A
- **Present in B, missing in A**: Orphaned in Source B
- **Matched key, mismatched fields**: Same key links the records, but name, email, status, or section differs

For three-way reconciliation, extend the match matrix to show which of the three systems each record appears in (A/B/C presence pattern).

### Step 3: Run the Discrepancy Checks
Systematically test for each anomaly type:

**Missing records** — a student in the system of record absent from a downstream system, or vice versa. Note direction (which system is missing the record).

**Duplicate enrollments** — the same match key appearing more than once within a single source, or the same person appearing under two different keys (detect via matching email/name across differing IDs).

**Identity/linkage mismatches** — same key but conflicting name spelling, email, date of birth, or program/section. Distinguish likely data-entry typos (one-character name differences) from genuine record collisions (completely different people sharing an ID).

**Status conflicts** — enrolled in one system but withdrawn/dropped/inactive in another. These are high-risk because they distort headcount and billing.

**Section/program misalignment** — same student enrolled in different sections, cohorts, or programs across systems.

**Format/data-quality issues** — malformed IDs, missing required fields, emails failing basic pattern validation, impossible values.

### Step 4: Assign Severity to Each Finding
Rate every discrepancy on a three-level scale with explicit criteria:
- **Critical**: Affects billing, official headcount, census, or compliance reporting — e.g., a paying student missing from the SIS, or a status conflict on an active enrollee
- **High**: Data integrity issue that will cause downstream errors if propagated — e.g., duplicate records, identity collisions, orphaned records in the system of record
- **Medium**: Cleanup needed but no immediate operational impact — e.g., formatting inconsistencies, minor name-spelling mismatches, section misalignment on inactive records

### Step 5: Determine Likely Root Cause and Resolution
For each discrepancy, state the most probable cause (e.g., "sync lag between SIS and LMS," "manual entry typo," "student re-registered under a new ID," "record dropped but not synced") and the specific corrective action, naming which system to update and what value it should hold.

### Step 6: Assemble the Prioritized Discrepancy List
Sort all findings Critical → High → Medium. Number them for tracking. Lead with a one-line reconciliation summary (matched count, total discrepancies by severity).

## Output Format
- **Structure**: Reconciliation summary at top → data profile → prioritized discrepancy table → resolution notes → next-step checklist
- **Length**: 500–900 words plus the discrepancy table (scales with volume of discrepancies found)
- **Format**: Table for the discrepancy list; short prose for summary and profile; bulleted next-step checklist
- **Sections**: Reconciliation Summary → Data Profile → Prioritized Discrepancy List (table) → Resolution Guidance → Recommended Next Steps
- **Tone**: Precise, non-alarmist, operations-analyst register — every claim tied to a specific record or field, no speculation beyond the data

The discrepancy table columns: `#` | `Match Key` | `Discrepancy Type` | `Severity` | `Systems Affected` | `Detail` | `Likely Cause` | `Recommended Action`

## Quality Checklist
Before delivering output, verify:
- [ ] The reconciliation summary states matched count and a discrepancy count for each severity level (Critical/High/Medium)
- [ ] Every record appearing in only one source is accounted for in the discrepancy list — none silently dropped
- [ ] Each discrepancy names the specific match key and the specific field(s) in conflict — not a vague "mismatch"
- [ ] Severity assignments follow the Step 4 criteria and the list is sorted Critical → High → Medium
- [ ] Every finding includes both a likely cause and a system-specific recommended action
- [ ] Normalization assumptions (case, trimming, match-key fallback) are stated so the user can verify no false positives were introduced

## Notes
- False discrepancies most often come from formatting, not real data problems — always normalize (Step 1) before comparing, and report what was normalized.
- If the two datasets use different header names for the same field, map them explicitly and state the mapping rather than guessing silently.
- Duplicate detection is strongest when both an ID field and an email/name field are present, because it catches the same person under two IDs — request both fields if only one is supplied.
- When the system of record is specified, frame every resolution as "update the non-authoritative system to match" unless the record is missing from the system of record itself, which is escalated to Critical.
- For large rosters, if the pasted data appears truncated, note the row counts and flag that the analysis covers only the visible records.

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## How to use this skill

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**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
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