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 skills/opendcai/dataflow-loopai/generating-dataflow-pipelinenpx skills add OpenDCAI/Dataflow-LoopAI --skill generating-dataflow-pipelinegit clone --depth 1 https://github.com/OpenDCAI/Dataflow-LoopAIWrote 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/skills/opendcai/dataflow-loopai/generating-dataflow-pipeline)<a href="https://agentmods.dev/skills/opendcai/dataflow-loopai/generating-dataflow-pipeline"><img src="https://agentmods.dev/badge/skills/opendcai/dataflow-loopai/generating-dataflow-pipeline.svg" alt="Measured on agentmods" 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 | $0.00020 | $0.05720 |
| Opus 5 | $0.00010 | $0.02860 |
| Sonnet 5 | $0.00004 | $0.01144 |
| Haiku 4.5 | $0.00002 | $0.00572 |
Grade A, and why
generating-dataflow-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 3d 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 — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataFlow Pipeline Code Generator
Goal
This skill is used when users provide:
- Target: What the pipeline should achieve
- Sample Data File: Path to a JSONL file containing 1-5 representative data samples
The skill must:
- Read and analyze the JSONL file at the provided path
- Infer data structure, field types, and content characteristics
- Determine task type based on file content (document processing, text transformation, multi-field composition)
- Select appropriate operators from preferred primitives
- Validate field dependencies
- Output intermediate operator decision summary
- Generate standard DataFlow pipeline code with
first_entry_file_nameset to the user-provided file path
User Input Format
Users provide:
Target: [Clear task description]
Sample file: [Path to JSONL file, e.g., ./data/input.jsonl]
Important: The sample file is a JSONL file (one JSON object per line), not a JSON array.
Preferred Operator Strategy
Six Core Primitives (high-coverage operators for most data science tasks):
PromptedGenerator- Single-field LLM generationFormatStrPromptedGenerator- Multi-field template generationText2MultiHopQAGenerator- Multi-hop QA pair constructionPromptedFilter- LLM-based quality filteringGeneralFilter- Rule-based filtering- KBC trio (always used together in order):
FileOrURLToMarkdownConverterFlash→KBCChunkGenerator→KBCTextCleaner
These are preferred primitives, not fixed workflows. They can be used repeatedly and combined flexibly.
Operator Selection Priority Rule (MANDATORY)
When a specialized operator exists for the task, it MUST be used over generic operators. Do NOT use PromptedGenerator to replicate functionality that a dedicated operator already provides.
Decision table (check in order, use the first match):
| Task / Scenario | Required Operator | Do NOT use |
|---|---|---|
| Generate QA pairs from text | Text2MultiHopQAGenerator |
PromptedGenerator with QA prompt |
| Convert file path / URL to text | KBC trio (FileOrURLToMarkdownConverterFlash → KBCChunkGenerator → KBCTextCleaner) |
PromptedGenerator to summarize files |
| Score / evaluate using multiple fields | FormatStrPromptedGenerator + GeneralFilter |
PromptedFilter (single input_key only) |
| Filter by deterministic rule on existing fields | GeneralFilter |
PromptedFilter |
| Generate new content from a single field | PromptedGenerator |
— |
| Generate new content from multiple fields | FormatStrPromptedGenerator |
Multiple PromptedGenerator steps |
What ships with it
19 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/agentic_rag_text_pipeline.md 1.3 KB
- examples/basic_generate_and_filter.md 4.5 KB
- examples/chemistry_smiles_text_pipeline.md 1.1 KB
- examples/code_text_pipelines.md 1.3 KB
- examples/function_call_text_pipeline.md 1.0 KB
- examples/kbc_pdf_to_qa.md 9.5 KB
- examples/multi_stage_pipeline.md 3.3 KB
- examples/multifield_scoring.md 2.9 KB
- examples/reasoning_cpu_clean_pipeline.md 1.6 KB
- examples/reasoning_diy_pipeline.md 2.6 KB
- examples/reasoning_general_pipeline.md 3.9 KB
- examples/reasoning_math_fusion_pipeline.md 3.3 KB
- examples/reasoning_math_pipeline.md 6.6 KB
- examples/reasoning_pretrain_pipeline.md 2.8 KB
- examples/text_benchmark_eval_pipelines.md 1.1 KB
- examples/text_synthesis_and_quality_pipelines.md 1.3 KB
- examples/text2qa_pipeline.md 1013 B
- examples/text2sql_text_pipelines.md 1.3 KB
- templates/pipeline_template.py 1.8 KB runs code
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.
- 3d ago First seen · 431 lines · 20 tokens per session scan A 5f084f0329b1
generating-dataflow-pipeline is a skill published in the GitHub repository OpenDCAI/Dataflow-LoopAI (22 stars, last pushed 3d ago), licensed Apache-2.0. It adds 20 tokens to every session and 5,720 once invoked, about $0.0001 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.
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