What is integrate-pipeline?

A command for adding a document-parsing pipeline to ParseBench, a benchmark for comparing document-processing systems.

It helps determine the provider type, authentication, input and output formats, and the files needed for an end-to-end integration.

It organizes the research and implementation work needed to make a new pipeline run from input to measured output.

How run-llama/ParseBench configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to run-llama/ParseBench. 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/run-llama/ParseBench/main/.claude/commands/integrate-pipeline.md

Clone the repo

git clone --depth 1 https://github.com/run-llama/ParseBench

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/run-llama/parsebench/integrate-pipeline/github.svg)](https://agentmods.dev/commands/run-llama/parsebench/integrate-pipeline)

Your own site

<a href="https://agentmods.dev/commands/run-llama/parsebench/integrate-pipeline"><img src="https://agentmods.dev/badge/commands/run-llama/parsebench/integrate-pipeline/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/run-llama/parsebench/integrate-pipeline"><img src="https://agentmods.dev/badge/commands/run-llama/parsebench/integrate-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 1,745 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.

Model	Per session	Once invoked
Fable 5.1	$0.00000	$0.01745
Opus 5.5	$0.00000	$0.00698
Sonnet 5.5	$0.00000	$0.00349
Haiku 4.5	$0.00000	$0.00175

Measured 2mo ago against content hash a432856d997e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

integrate-pipeline 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.

.claude/commands/integrate-pipeline.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Integrate a new document parsing pipeline into ParseBench: $ARGUMENTS

You are integrating a new pipeline into the ParseBench benchmark. The user will provide a pipeline name and any relevant context (API docs, SDK links, product website, etc.). Your job is to create all the files needed so that uv run parse-bench run <pipeline_name> works end-to-end.

Step 1: Understand the provider

Before writing any code, research the provider:

If the user gave a URL, fetch and read it to understand the API/SDK.
Determine:
- Product type: Is this a PARSE provider (PDF -> markdown) or LAYOUT_DETECTION provider (PDF -> bounding boxes)?
- Integration style: Cloud API (needs API key), self-hosted model (needs endpoint URL), or local library (no external deps)?
- SDK/API pattern: Does it have a Python SDK? REST API? What's the auth method?
- Input format: Does it accept PDF files directly, or does it need images (page screenshots)?
- Output format: What does the raw response look like? Markdown? HTML? JSON with pages?

Step 2: Find the closest existing provider to use as a template

Look at the existing providers and pick the best template:

Cloud API with Python SDK (e.g., OpenAI, Anthropic, Google): Copy from src/parse_bench/inference/providers/parse/openai.py or anthropic_haiku.py
Cloud API with REST calls: Copy from src/parse_bench/inference/providers/parse/reducto.py or chunkr.py
Self-hosted vLLM endpoint: Copy from src/parse_bench/inference/providers/parse/gemma4.py or qwen3_5.py
Local library (no API): Copy from src/parse_bench/inference/providers/parse/pymupdf.py or tesseract.py
Layout detection: Copy from src/parse_bench/inference/providers/layoutdet/docling.py

Read the template file to understand the exact pattern.

Step 3: Create the provider file

Create src/parse_bench/inference/providers/parse/<provider_name>.py (or layoutdet/ for layout detection).

Read the full file on GitHub · 175 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.

2mo ago First seen · 175 lines · 0 tokens per session scan A a432856d997e

Subscribe to this mod's changes

integrate-pipeline is a command published in the GitHub repository run-llama/ParseBench (618 stars, last pushed yesterday), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,745 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 2026-08-30.

Documents Evaluation LLM LlamaIndex Testing benchmark document-ai document-parsing

output

Generate output artifacts from active wiki content — summaries, reports, study guides, slide outlines, timelines, glossaries, comparisons. Outputs are filed back into the wiki.

nvk/llm-wiki · 33 tokens

expect

Diff-aware AI browser testing — reads the git diff, maps changes to affected pages via the route map, generates a targeted test plan, and executes it via agent-browser (Rust daemon + CDP, ARIA-tree-first) with pass/fail reporting. Use when testing UI changes, verifying PRs before merge, or running regression checks on…

yonatangross/orchestkit · 73 tokens

ParseBench: Command for Claude Code

Borrowing it

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

Step 1: Understand the provider

Step 2: Find the closest existing provider to use as a template

Step 3: Create the provider file

What this file has done since we first saw it

Other commands, from other repositories

output

expect

brief-compliance-check

fire-verify-uat

proofrag

fire-test