mcpbeat

Name Generator

microsoft/name-generator

Generate approved person names for examples, demos, quests, tests, docs, and sample data. Use when a task needs fictional person names, customer names, employee names, patient names, student names, instructor names, or other human names.

843 tokens
context cost
the whole folder, loaded on every use
1
files
instructions only
0
copies elsewhere
how many repositories repackaged it
2320
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/microsoft/Ontology-Playground --skill name-generator

The instruction itself

10 sections, as written by the author

Name Generator Skill

Goal

Generate person names for this repository from the approved local name fixture:

data/reference/FNF-2026-06-01-01002-0268.csv

Do not invent person names. Every person name used in examples, sample data,

quests, tests, demos, docs, or generated ontology content must come from the

CSV FullName column.

Source File

CSV columns:

FirstName,LastName,FullName,FirstNameNative,LastNameNative,FullNameNative,Gender,Language

Use FullName by default. Use FullNameNative only when the user explicitly

asks for native-script names or locale-specific display text.

Workflow

1. Decide how many names are needed

Identify the role and quantity from the task, for example:

  • sample customers
  • employees or managers
  • patients or clinicians
  • students or instructors
  • reviewers, approvers, assignees, or contributors

If the task does not specify quantity, use the minimum number needed for the

example or test.

2. Read names from the CSV

Use the CSV fixture as the only source. A quick shell-friendly way to inspect

the first approved names is:

awk -F, 'NR > 1 { print $3 }' data/reference/FNF-2026-06-01-01002-0268.csv | head

For random sampling:

awk -F, 'NR > 1 { print rand() "\t" $3 }' data/reference/FNF-2026-06-01-01002-0268.csv | sort -n | cut -f2- | head -n 5

If names with commas or quotes are ever added to the CSV, use a proper CSV

parser instead of field splitting.

3. Fit names to the scenario

  • Choose distinct names for distinct entities.
  • Keep the selected names stable within a scenario so queries, expected results,

docs, and sample instances stay consistent.

  • Do not alter spellings unless the surrounding file has a strict ASCII-only

convention. If ASCII is required, choose names from the CSV that are already

ASCII-compatible.

need to use the person's name.

4. Update all dependent examples

When replacing a name in code or content, update every coupled surface:

  • sample instances
  • query prompts and curated query matches
  • expected test strings
  • rendered docs or generated content source files
  • catalogue examples or learning materials

Regenerate compiled artifacts when source content changes:

npm run catalogue:build
npm run learn:build

Validation

Before finishing a name-generation or name-replacement task:

  • Verify each selected name appears in the CSV FullName column.
  • Search for removed placeholder names to ensure no stale references remain.
  • Run focused tests for touched code paths.
  • Run npm run build when generated catalogue or learning output changes.

Done Criteria

  • [ ] All person names used by the task come from data/reference/FNF-2026-06-01-01002-0268.csv.
  • [ ] No invented placeholder names remain in the touched examples.
  • [ ] Related prompts, sample data, expected results, and tests are consistent.
  • [ ] Relevant tests or build commands have passed, or any skipped validation is clearly reported.

How to use it

Copy the folder

Take microsoft/name-generator from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.