Borrowing it
Nothing to install: this file belongs to NomaDamas/AutoRAG-Research. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NomaDamas/AutoRAG-Research/main/.claude/agents/pipeline-test-writer.mdgit clone --depth 1 https://github.com/NomaDamas/AutoRAG-ResearchWrote 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/nomadamas/autorag-research/pipeline-test-writer)<a href="https://agentmods.dev/agents/nomadamas/autorag-research/pipeline-test-writer"><img src="https://agentmods.dev/badge/agents/nomadamas/autorag-research/pipeline-test-writer/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/nomadamas/autorag-research/pipeline-test-writer"><img src="https://agentmods.dev/badge/agents/nomadamas/autorag-research/pipeline-test-writer.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 | $0.00180 | $0.00274 |
| Opus 5 | $0.00090 | $0.00137 |
| Sonnet 5 | $0.00036 | $0.00055 |
| Haiku 4.5 | $0.00018 | $0.00027 |
Grade A, and why
pipeline-test-writer 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 11d 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.
What it actually says
Read and follow ai_instructions/pipeline_test_writer.md.
Task:
- Write the pipeline tests from
Pipeline_Design.md - Use the shared verifier/test utilities
- Do not implement the pipeline itself
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.
- 11d ago First seen · 38 lines · 180 tokens per session scan A 32ee1b366688
pipeline-test-writer is an agent published in the GitHub repository NomaDamas/AutoRAG-Research (148 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 180 tokens to every session and 274 once invoked, about $0.0009 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 2026-08-30.
Other agents, from other repositories
unfolding-po
PO (Product Owner) role in the Unfolding Specs process. Decomposes Features into smaller Features, creates Acceptance Tests, and identifies implicit business assumptions as Domain Model Decisions (DMDs).
evals
LLM evaluation — eval harness design, benchmark suites, automated regression, human eval orchestration.
FAI Deterministic Expert
Deterministic AI specialist — makes AI outputs reproducible, grounded, and auditable with temperature control, seed pinning, JSON schema output, RAG grounding, citation enforcement, and multi-layer hallucination defense.
rag-eval-runner
Use to run the RAG eval suite and produce a regression report. Best invoked after pipeline changes or before a release. Returns retrieval metrics, answer metrics, comparison to baseline, and a triage list of regressed queries. Runs in a separate context to keep the main session clean.
eval-specialist
LLM evaluation specialist. Designs frameworks to evaluate prompt quality, RAG retrieval performance, and overall app quality. Builds benchmarks, A/B tests, and regression tests.
FAI AI Search Portal Tuner
AI Search Portal tuner — hybrid weight optimization, scoring profile calibration, reranker config, suggester tuning, and answer generation quality.