pyspark-datagen: Skill for Claude Code

.agents/skills/add-datagen-generator/SKILL.md

add-datagen-generator is a skill for Claude Code, Codex from newfront/pyspark-datagen. It costs 74 tokens per session (1,286 once invoked), scanned A, original, Apache-2.0.

A project-specific procedure for adding a new fake-data generator backed by Protocol Buffers to the learning-spark-datagen project. Protocol Buffers are a structured format for defining messages exchanged by software.

In plain words
What is it for?
Use it when adding an entity such as Shipment or Payment. It covers the message definition, validation rules, generator class, command-line interface, and tests.
Why use it?
It keeps a new entity consistent with the project’s existing User and Order generators. Validation rules help ensure generated messages contain the required fields and valid values.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is newfront/pyspark-datagen's own configuration. It tells Claude Code and Codex how to work on pyspark-datagen itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything pyspark-datagen configures →

Reuse

Borrowing it

Nothing to install: this file belongs to newfront/pyspark-datagen. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/newfront/pyspark-datagen/main/.agents/skills/add-datagen-generator/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/newfront/pyspark-datagen

Made for: Claude Code, Codex.

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README.md
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Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,286 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00074 $0.01286
Opus 5.5 $0.00030 $0.00514
Sonnet 5.5 $0.00015 $0.00257
Haiku 4.5 $0.00007 $0.00129

Measured 2mo ago against content hash c31db573511e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

add-datagen-generator 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 2mo 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.

.agents/skills/add-datagen-generator/SKILL.md · 66 lines

How it starts

The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Add a New Data Generator

Use this skill when adding a new message type and generator to learning-spark-datagen/ (proto + Python generator + CLI + tests). Follow the existing User and Order generators as the reference pattern.

1. Proto and validation

  • Add protos/<domain>/v1/<message>.proto (e.g. order/v1/order.proto).
  • Use buf.validate so every generated message passes protovalidate.validate(message):
    • IDs / FKs: (buf.validate.field).string.uuid = true and (buf.validate.field).required = true for string IDs that link to other entities.
    • Required fields: (buf.validate.field).required = true on message fields and on scalars where zero is invalid.
    • Nested messages: required = true on any message field that must be set (e.g. total, unit_cost).
    • Strings: (buf.validate.field).string = { min_len: 1 } for non-empty (e.g. currency).
    • Repeated: (buf.validate.field).repeated = { min_items: 1 } when at least one item is required.
    • Numbers: e.g. (buf.validate.field).uint32 = { gte: 1 } for counts.
  • Import buf/validate/validate.proto and keep syntax = "proto3" and package/options consistent with existing protos.

2. Generate and package layout

  • Run buf generate (or make build) from the project root so gen/python/<domain>/v1/<message>_pb2.py is created.
  • If a new top-level package is created under gen/python/, add __init__.py in each directory (e.g. gen/python/order/__init__.py, gen/python/order/v1/__init__.py) so from order.v1 import order_pb2 works.

3. Generator class

  • Add src/learning_spark_datagen/datagen/gen_<name>.py (e.g. gen_order.py).
  • Mirror the structure of GenUser / GenOrder:
    • __init__(self, seed, <parent_ids>=None) — e.g. user_ids so child records can reference parent UUIDs.
    • generate_one(self, index) — deterministic from seed + index (same index ⇒ same message). Use Random(seed + index) and a fixed pool of IDs (e.g. product UUIDs) when you need deterministic sub-messages.
    • generate(self, count) — return [self.generate_one(i) for i in range(count)].
    • @staticmethod to_dict(msg), write_ndjson(path, messages), read_ndjson(path) using json_format.MessageToDict / ParseDict and preserving_proto_field_name=True.
  • If the message has a parent link (e.g. user_id), accept an optional list of parent IDs and assign parent_ids[index % len(parent_ids)] (or generate deterministic UUIDs when the list is not provided).
  • Ensure every generated message passes protovalidate.validate(message) (required fields set, UUIDs valid, min_items satisfied, etc.).

Read the full file on GitHub · 66 lines

Changes

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.

  1. 2mo ago First seen · 66 lines · 74 tokens per session scan A c31db573511e

Subscribe to this mod's changes

add-datagen-generator is a skill published in the GitHub repository newfront/pyspark-datagen (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 1,286 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-08-31.

Related

add-datagen-generator compared with skills from other repositories

add-datagen-generator next to 6 skills from other repositories
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