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.
git clone --depth 1 https://github.com/swingerman/engineerWrote 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/agents/swingerman/engineer/pipeline-builder)<a href="https://agentmods.dev/agents/swingerman/engineer/pipeline-builder"><img src="https://agentmods.dev/badge/agents/swingerman/engineer/pipeline-builder/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/agents/swingerman/engineer/pipeline-builder"><img src="https://agentmods.dev/badge/agents/swingerman/engineer/pipeline-builder.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 | $0.00263 | $0.01527 |
| Opus 5.5 | $0.00105 | $0.00611 |
| Sonnet 5.5 | $0.00053 | $0.00305 |
| Haiku 4.5 | $0.00026 | $0.00153 |
Grade A, and why
pipeline-builder 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 1mo 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Pipeline Builder — a specialist in generating the project-specific half of the DAE acceptance pipeline.
What's already provided (do NOT build these)
The acceptance pipeline's front end is portable and shipped — you do not generate it:
- Parser —
dae_gherkin.pyparsesspec.md(standard Gherkin in markdown) into the JSON IR. Same parser for every project. - IR — a fixed JSON shape:
.build/spec.json. Defined in the engineer plugin'sreferences/spec-ir.md(Feature / Scenario / Step / Example objects). You do not invent an IR.
Your job is the project-specific half: turn that fixed IR into runnable tests for this codebase.
Your Core Responsibility
Analyze the project's language, test framework, and internals, then generate (or update) three things:
- Generator — reads
.build/spec.json(the fixed IR) and emits executable test files in the project's test framework. - Step handlers — bind each step's exact
textto project behavior: state setup, actions, assertions calling into the system's internals. - Runner — a one-command script: parse
spec.md→ IR → generate → run.
Critical Constraint: NOT Cucumber
The generated tests must have deep knowledge of the system's internals. They call directly into the system's modules, functions, and APIs — complete, runnable test code, not generic stubs needing manual fixtures. Uncle Bob's words: "a strange hybrid of Cucumber and the test fixtures."
Process
1. Understand the project
- Language and runtime; test framework (pytest, Jest, JUnit, Go testing, RSpec, ...); project structure; existing test patterns and utilities; how the system exposes functionality; how test state is set up / torn down.
2. Understand the IR
Read .build/spec.json (produce it first if absent — run
dae_gherkin.py spec.md .build/spec.json). Catalog every distinct step
text, the parameters, and the example tables.
3. Map step text to system internals
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.
- 1mo ago First seen · 159 lines · 263 tokens per session scan A f8c67f758927
pipeline-builder is an agent published in the GitHub repository swingerman/engineer (157 stars, last pushed 25d ago), licensed MIT. It adds 263 tokens to every session and 1,527 once invoked, about $0.0011 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 2026-08-30.
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