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 agentmods add commands/romilly/claude-code-helpers/buildersgit clone --depth 1 https://github.com/romilly/claude-code-helpersWhat 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 | $0.00000 | $0.01101 |
| Opus 5 | $0.00000 | $0.00550 |
| Sonnet 5 | $0.00000 | $0.00220 |
| Haiku 4.5 | $0.00000 | $0.00110 |
Grade A, and why
builders 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 yesterday.
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 — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Data Builders Guide
When to Create Builders
Create test data builders when:
- Verbose setup code - Tests have multi-line object construction that obscures intent
- Nested hierarchies - Objects contain other objects several levels deep
- Repeated boilerplate - Same structure appears across many tests with small variations
Don't create builders for simple dataclasses with 2-3 fields.
Spotting Opportunities
Test smell: Deeply nested construction that buries the test's intent:
# Before - 12 lines to set up one task
mm = MindMap(root=Node(
id="ID_ROOT", title="Project",
children=[Node(id="ID_TASKS", title="tasks", children=[
Node(id="ID_LANE", title="Team", children=[
Node(id="ID_1", title="Task A"),
])
])]
))
# After - intent is clear
mm = simple_mindmap(task("ID_1", "Task A"))
The Pattern
1. Builder Class with Fluent API
class NodeBuilder:
"""Fluent builder for Node objects."""
def __init__(self, id: str, title: str):
self._id = id
self._title = title
self._description: str | None = None
self._children: list[Node] = []
def with_description(self, description: str) -> "NodeBuilder":
self._description = description
return self
def with_children(self, *children: "NodeBuilder | Node") -> "NodeBuilder":
for child in children:
if isinstance(child, NodeBuilder):
self._children.append(child.build())
else:
self._children.append(child)
return self
def build(self) -> Node:
return Node(
id=self._id,
title=self._title,
description=self._description,
children=self._children,
)
2. Factory Function (Entry Point)
def task(id: str, title: str) -> NodeBuilder:
"""Factory to start building a task node."""
return NodeBuilder(id, title)
3. Convenience Function for Common Cases
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.
- yesterday First seen · 170 lines · 0 tokens per session scan A e0867e60c488
builders is a command published in the GitHub repository romilly/claude-code-helpers (2 stars, last pushed 6mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,101 tokens. 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.