Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vaquarkhan/data-engineering-agent-skills --skill master-data-and-entity-resolutiongit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/master-data-and-entity-resolution)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/master-data-and-entity-resolution"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/master-data-and-entity-resolution/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/master-data-and-entity-resolution"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/master-data-and-entity-resolution.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00041 | $0.00443 |
| Opus 5 | $0.00020 | $0.00221 |
| Sonnet 5 | $0.00008 | $0.00089 |
| Haiku 4.5 | $0.00004 | $0.00044 |
Grade A, and why
master-data-and-entity-resolution scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Master Data And Entity Resolution
Overview
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.
When to Use
- building customer, product, or account golden records
- resolving duplicate identities across systems
- defining master data domains
- publishing canonical reference datasets
Do not collapse entity resolution into ad hoc joins if the result becomes a shared dependency.
Workflow
-
Define the entity contract. Include:
- canonical entity type
- contributing systems
- primary identifiers
- confidence or match logic
- ownership
-
Choose the matching strategy. Common strategies:
- exact key match
- deterministic rule-based match
- probabilistic or scored match
-
Define survivorship rules. Decide which system wins for each attribute and under what conditions.
-
Track unresolved and ambiguous cases.
-
Publish master data with clear confidence and lineage context.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We can just use email as the unique customer key." | Real systems often contain shared, missing, or changing identifiers. |
| "The golden record is obvious once matched." | Attribute-level survivorship and conflict handling still need explicit rules. |
| "Ambiguous matches are edge cases." | They become painful quickly when downstream systems treat them as truth. |
Red Flags
- no explicit canonical entity definition exists
- survivorship logic is implicit in SQL ordering
- unresolved cases are silently dropped or forced
- downstream consumers are not told match confidence assumptions
Verification
- Canonical entity and source systems are explicit
- Match and survivorship rules are documented
- Ambiguous cases have a defined handling path
- Published master data includes lineage or confidence context where needed
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 65 lines · 41 tokens per session scan A ea0cc63439e9
master-data-and-entity-resolution is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 41 tokens to every session and 443 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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