How do I install the Text2multihopqa Generator skill?

Run npx skillmds@latest add opendcai/text2multihopqa-generator-2 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Text2multihopqa Generator safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Text2multihopqa Generator?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Text2multihopqa Generator free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Text2multihopqa Generator?

opendcai (@opendcai) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Text2multihopqa Generator by opendcai

Name: Text2multihopqa Generator
Author: opendcai

Text2multihopqa Generator

Reference documentation for the Text2MultiHopQAGenerator operator. [Purpose] Generates multi-hop QA pairs from one text column and writes two output columns: one for `qa_pairs` and one for metadata. [When to use] Use it when you want reasoning-style QA pairs derived from longer text chunks. If only simple single-hop QA is needed, use `Text2QAGenerator` instead.

opendcai Updated Sep 21, 2026

File contents

Text2MultiHopQAGenerator Operator Reference

Text2MultiHopQAGenerator reads one text column, generates up to num_q multi-hop QA pairs per input row, stores the per-row QA list in output_key, stores metadata in output_meta_key, then filters out rows whose generated QA list is empty.

See examples/good.md for a valid pipeline pattern and examples/bad.md for common failure cases.

1. Import

from dataflow.operators.core_text import Text2MultiHopQAGenerator

2. Constructor

Text2MultiHopQAGenerator(
    llm_serving=llm,
    seed=0,
    lang="en",
    prompt_template=None,
    num_q=5,
)

Parameter	Required	Default	Description
`llm_serving`	Yes	None	LLM backend passed through to `ExampleConstructor`, which later calls `generate_from_input(...)`.
`seed`	No	`0`	Used to initialize `random.Random(seed)`.
`lang`	No	`"en"`	Controls prompt construction and sentence splitting logic.
`prompt_template`	No	`None`	If omitted, uses `Text2MultiHopQAGeneratorPrompt(lang=self.lang)`.
`num_q`	No	`5`	Maximum number of QA pairs kept per row after generation.

3. run() Signature

op.run(
    storage=self.storage.step(),
    input_key="cleaned_chunk",
    output_key="QA_pairs",
    output_meta_key="QA_metadata",
)

Parameter	Required	Default	Description
`storage`	Yes	None	Used as `storage.read("dataframe")` and `storage.write(dataframe)`.
`input_key`	No	`"cleaned_chunk"`	Source text column. This column must already exist.
`output_key`	No	`"QA_pairs"`	Output column containing a list of QA dicts for each remaining row. This column must not already exist.
`output_meta_key`	No	`"QA_metadata"`	Output column containing metadata dicts for each remaining row.

Return Value

The method returns [output_key].

4. Actual Runtime Logic

The source code behavior is:

Read the DataFrame from storage.
Validate that input_key exists.
Validate that output_key does not already exist.
Read all texts from dataframe[input_key].tolist().
Generate one example record per input row via process_batch(...).
Truncate each row's qa_pairs list to at most num_q.
Write QA lists to output_key and metadata dicts to output_meta_key.
Drop rows whose output_key is not a non-empty list.
Write the filtered DataFrame back to storage.
Return [output_key].

Important consequences:

The operator does not expand one row into multiple rows.
The final row count is less than or equal to the input row count.
Rows with empty generated QA lists are removed entirely.

5. Text Filtering Rules

Inside ExampleConstructor, a text can fail before QA generation if:

it is not a string,
its length is less than 100,
its length is greater than 200000,
it fails the basic sentence-count or special-character quality checks.

When that happens, the row gets an empty qa_pairs list first, and is then filtered out by run().

6. Important Constraints

input_key must exist, otherwise run() raises ValueError.
output_key must not already exist, otherwise run() raises ValueError.

---name: text2multihopqa-generator-2description: Reference documentation for the Text2MultiHopQAGenerator operator. [Purpose] Generates multi-hop QA pairs from one text column and writes two output columns: one for `qa_pairs` and one for metadata. [When to use] Use it when you want reasoning-style QA pairs derived from longer text chunks. If only simple single-hop QA is needed, use `Text2QAGenerator` instead.---# Text2MultiHopQAGenerator Operator Reference`Text2MultiHopQAGenerator` reads one text column, generates up to `num_q` multi-hop QA pairs per input row, stores the per-row QA list in `output_key`, stores metadata in `output_meta_key`, then filters out rows whose generated QA list is empty.See `examples/good.md` for a valid pipeline pattern and `examples/bad.md` for common failure cases.---## 1. Import```pythonfrom dataflow.operators.core_text import Text2MultiHopQAGenerator```---## 2. Constructor```pythonText2MultiHopQAGenerator( llm_serving=llm, seed=0, lang="en", prompt_template=None, num_q=5,)```| Parameter | Required | Default | Description ||-----------|----------|---------|-------------|| `llm_serving` | Yes | None | LLM backend passed through to `ExampleConstructor`, which later calls `generate_from_input(...)`. || `seed` | No | `0` | Used to initialize `random.Random(seed)`. || `lang` | No | `"en"` | Controls prompt construction and sentence splitting logic. || `prompt_template` | No | `None` | If omitted, uses `Text2MultiHopQAGeneratorPrompt(lang=self.lang)`. || `num_q` | No | `5` | Maximum number of QA pairs kept per row after generation. |---## 3. run() Signature```pythonop.run( storage=self.storage.step(), input_key="cleaned_chunk", output_key="QA_pairs", output_meta_key="QA_metadata",)```| Parameter | Required | Default | Description ||-----------|----------|---------|-------------|| `storage` | Yes | None | Used as `storage.read("dataframe")` and `storage.write(dataframe)`. || `input_key` | No | `"cleaned_chunk"` | Source text column. This column must already exist. || `output_key` | No | `"QA_pairs"` | Output column containing a list of QA dicts for each remaining row. This column must not already exist. || `output_meta_key` | No | `"QA_metadata"` | Output column containing metadata dicts for each remaining row. |### Return ValueThe method returns `[output_key]`.---## 4. Actual Runtime LogicThe source code behavior is:1. Read the DataFrame from storage.2. Validate that `input_key` exists.3. Validate that `output_key` does not already exist.4. Read all texts from `dataframe[input_key].tolist()`.5. Generate one example record per input row via `process_batch(...)`.6. Truncate each row's `qa_pairs` list to at most `num_q`.7. Write QA lists to `output_key` and metadata dicts to `output_meta_key`.8. Drop rows whose `output_key` is not a non-empty list.9. Write the filtered DataFrame back to storage.10. Return `[output_key]`.Important consequences:- The operator does not expand one row into multiple rows.- The final row count is less than or equal to the input row count.- Rows with empty generated QA lists are removed entirely.---## 5. Text Filtering RulesInside `ExampleConstructor`, a text can fail before QA generation if:- it is not a string,- its length is less than `100`,- its length is greater than `200000`,- it fails the basic sentence-count or special-character quality checks.When that happens, the row gets an empty `qa_pairs` list first, and is then filtered out by `run()`.---## 6. Important Constraints1. `input_key` must exist, otherwise `run()` raises `ValueError`.2. `output_key` must not already exist, otherwise `run()` raises `ValueError`.

Frequently asked questions