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Compare CSV/JSON by key. Find missing rows, duplicates and value differences with local reports.
$14.99
Migration Parity & Exceptions Verifier
Example session with this skill installed
Compare the bundled synthetic equal-totals exports using config.json. Run locally in a fresh directory, preserve inputs, and report missing keys, duplicates and value differences. Do equal totals prove parity?
- Read your context and instructions
- Compiled the migration parity
- Generated the data export
Actual CLI evidence — synthetic fixtures
The tool ran locally. This explanation is manually written from its generated artifacts, not a recorded AI session.
python3 migration-parity-verifier/examples/run_demo.py --out /tmp/parity-demo-new
The runner exited 0 after verifying all four expected comparisons:
| Fixture | Comparison exit | Exceptions |
|---|---|---|
| parity | 0 | 0 |
| equal-totals | 1 | 3 |
| duplicates | 1 | 1 |
| mapped JSON-to-CSV | 0 | 0 |
Equal-totals result
EXCEPTIONS, exit 1. Left: 2 data rows. Right: 2 data rows.
| Issue | Count |
|---|---|
| duplicate_left | 0 |
| duplicate_right | 0 |
| missing_left | 1 |
| missing_right | 1 |
| value_difference | 1 |
Left has A100=40.00, B200=60.00. Right has A100=45.00, C300=55.00. Both total 100.00. A100 amount differs; B200 is missing on the right; C300 is missing on the left. Equal totals do not establish parity.
The complete exception CSV, Markdown report and hash manifest ship in migration-parity-verifier/examples/demo-output/equal-totals/. New runs write the same three report types in your fresh output directory.
Key: id (text). Compared fields: id and amount (exact decimal). Null tokens: none. Trimming: off. Naive times: reject. Tool version: 1.0.0.
Review locally
Inspect exceptions.csv using its documented JSON-encoded cell format. manifest.json records original input/config/script hashes, normalized rules and limits. Repeated reports are byte-identical with identical inputs, config, tool, limits and environment. Inputs were unchanged.
This configured result does not approve the whole migration or cutover. No data was repaired or transmitted.
exceptions.csv
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
About this skill
Find the records behind the totals
Compare local CSV or JSON exports by explicit business keys. Find missing rows, duplicate keys and changed values, then keep reproducible exception reports for review.
USD 14.99 — one-time purchase.
What you get
- A reusable SKILL.md workflow for agreeing the comparison contract and reviewing evidence.
- A deterministic Python 3.10+ tool using only the standard library.
- Composite keys, left/right field mappings, exact decimals, configurable null tokens and documented timezone behavior.
- exceptions.csv, a Markdown report and a manifest with input, configuration and script SHA-256 hashes.
- Synthetic examples, installation instructions, a worked demo and automated tests.
Equal totals can hide different records
The synthetic source has A100 = 40.00 and B200 = 60.00. The target has A100 = 45.00 and C300 = 55.00. Both totals are 100.00, yet the tool finds three exceptions: one changed amount, one missing-right key and one missing-left key.
Explicit comparison rules
Choose fields as text, decimal, timestamp or boolean. Decimal values compare exactly: 1.00 equals 1, while 0.10 differs from 0.1001. No tolerance, automatic rounding, currency conversion or unit scaling. Null tokens are opt-in; JSON null differs from empty text by default. Offset-bearing timestamps compare as UTC instants. Naive timestamps require an explicit timezone policy; ambiguous or nonexistent IANA local times are rejected.
Duplicate keys are reported. The tool does not guess which rows to pair. Unconfigured scalar fields are outside scope; nested JSON values are rejected.
Run locally and review
Agree keys, mappings and normalization rules, then run the script against authorized local files in a fresh output directory. Inspect the exception CSV, Markdown summary and manifest locally. Repeat using identical inputs, configuration, tool, limits and runtime/timezone environment.
The tool has no database connector, network calls, telemetry or automatic repair. It never changes input data. The AI workflow defaults to metadata and counts. Reports may contain configured source values and should stay local unless you explicitly authorize sharing them. Your AI host privacy behavior is separate.
Requirements and limits
Python 3.10+ and a local shell. Guided skill use requires a host that reads local SKILL.md and supports tool execution; AI usage is separate. CSV and flat JSON row arrays only. Processing is in memory, with defaults of 25 MiB per file and 100,000 rows per input, configurable through CLI limits. No large-scale performance benchmark is claimed.
Results cover the supplied exports, keys, fields and rules. They do not establish export completeness, referential integrity, application behavior or overall cutover readiness. A zero-exception result is evidence for the configured comparison; human owners decide next steps. No consulting or managed migration service is included.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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Python