master-data-and-entity-resolution

Name: master-data-and-entity-resolution
Author: vaquarkhan

19stars4forks

How to install master-data-and-entity-resolution?

vaquarkhan/data-engineering-agent-skills/master-data-and-entity-resolution

$npx -y skills add vaquarkhan/data-engineering-agent-skills --skill master-data-and-entity-resolution

Installs into the current project.

Summary

Guides agents through master data and entity resolution workflows. Use when matching identities across systems, defining canonical entities, resolving duplicates, or building golden records for shared downstream use.

Files · 1

View on GitHub

SKILL.md

1	# Master Data And Entity Resolution
2
3	## Overview
4
5	Use this skill when the data problem is "who or what is this really?" across multiple systems. It helps agents define canonical entities, matching logic, survivorship rules, and downstream-safe resolution behavior.
6
7	## When to Use
8
9	- building customer, product, or account golden records
10	- resolving duplicate identities across systems
11	- defining master data domains
12	- publishing canonical reference datasets
13
14	Do not collapse entity resolution into ad hoc joins if the result becomes a shared dependency.
15
16	## Workflow
17
18	1. Define the entity contract.
19	Include:
20	- canonical entity type
21	- contributing systems
22	- primary identifiers
23	- confidence or match logic
24	- ownership
25
26	2. Choose the matching strategy.
27	Common strategies:
28	- exact key match
29	- deterministic rule-based match
30	- probabilistic or scored match
31
32	3. Define survivorship rules.
33	Decide which system wins for each attribute and under what conditions.
34
35	4. Track unresolved and ambiguous cases.
36
37	5. Publish master data with clear confidence and lineage context.
38
39	## Common Rationalizations
40
41	\| Rationalization \| Reality \|
42	\| --- \| --- \|
43	\| "We can just use email as the unique customer key." \| Real systems often contain shared, missing, or changing identifiers. \|
44	\| "The golden record is obvious once matched." \| Attribute-level survivorship and conflict handling still need explicit rules. \|
45	\| "Ambiguous matches are edge cases." \| They become painful quickly when downstream systems treat them as truth. \|
46
47	## Red Flags
48
49	- no explicit canonical entity definition exists
50	- survivorship logic is implicit in SQL ordering
51	- unresolved cases are silently dropped or forced
52	- downstream consumers are not told match confidence assumptions
53
54	## Verification
55
56	- [ ] Canonical entity and source systems are explicit
57	- [ ] Match and survivorship rules are documented
58	- [ ] Ambiguous cases have a defined handling path
59	- [ ] Published master data includes lineage or confidence context where needed

People who liked this also viewed

6 picks

Type

master-data-and-entity-resolution

19stars4forks

View on GitHub

How to install master-data-and-entity-resolution?

vaquarkhan/data-engineering-agent-skills/master-data-and-entity-resolution

$npx -y skills add vaquarkhan/data-engineering-agent-skills --skill master-data-and-entity-resolution

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Master Data And Entity Resolution
2
3	## Overview
4
5	Use this skill when the data problem is "who or what is this really?" across multiple systems. It helps agents define canonical entities, matching logic, survivorship rules, and downstream-safe resolution behavior.
6
7	## When to Use
8
9	- building customer, product, or account golden records
10	- resolving duplicate identities across systems
11	- defining master data domains
12	- publishing canonical reference datasets
13
14	Do not collapse entity resolution into ad hoc joins if the result becomes a shared dependency.
15
16	## Workflow
17
18	1. Define the entity contract.
19	Include:
20	- canonical entity type
21	- contributing systems
22	- primary identifiers
23	- confidence or match logic
24	- ownership
25
26	2. Choose the matching strategy.
27	Common strategies:
28	- exact key match
29	- deterministic rule-based match
30	- probabilistic or scored match
31
32	3. Define survivorship rules.
33	Decide which system wins for each attribute and under what conditions.
34
35	4. Track unresolved and ambiguous cases.
36
37	5. Publish master data with clear confidence and lineage context.
38
39	## Common Rationalizations
40
41	\| Rationalization \| Reality \|
42	\| --- \| --- \|
43	\| "We can just use email as the unique customer key." \| Real systems often contain shared, missing, or changing identifiers. \|
44	\| "The golden record is obvious once matched." \| Attribute-level survivorship and conflict handling still need explicit rules. \|
45	\| "Ambiguous matches are edge cases." \| They become painful quickly when downstream systems treat them as truth. \|
46
47	## Red Flags
48
49	- no explicit canonical entity definition exists
50	- survivorship logic is implicit in SQL ordering
51	- unresolved cases are silently dropped or forced
52	- downstream consumers are not told match confidence assumptions
53
54	## Verification
55
56	- [ ] Canonical entity and source systems are explicit
57	- [ ] Match and survivorship rules are documented
58	- [ ] Ambiguous cases have a defined handling path
59	- [ ] Published master data includes lineage or confidence context where needed

People who liked this also viewed

6 picks

Type