Openrouter Embeddings Agent Skill

by QinghongLin

Generate text embeddings via OpenRouter using Qwen3-Embedding-8B.

1k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

149

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/QinghongLin/data2story-skill --skill openrouter-embeddings

The instruction itself

7 sections, as written by the author

openrouter-embeddings

Text → embedding vector via OpenRouter. Default model: qwen/qwen3-embedding-8b.

Usage

Resolve TOOL_DIR = the directory containing this SKILL.md. Commands below use TOOL_DIR as a symbolic placeholder; replace it with the resolved, quoted path before running Bash.

Single text

export OPENROUTER_API_KEY=sk-or-v1-...

python3 TOOL_DIR/scripts/embed.py \
  --text "The quick brown fox jumps over the lazy dog" \
  --output vec.json

Batch from JSONL

Input records.jsonl (one JSON per line):

{"id": "row_0", "text": "Every place name in the United States."}
{"id": "row_1", "text": "Nearby stars and potential exoplanets."}

Run:

python3 TOOL_DIR/scripts/embed.py \
  --jsonl records.jsonl \
  --output records_with_embeddings.jsonl \
  --batch-size 32

Output is the same JSONL with an added embedding field per line.

Flags

| Flag | Default | Description |

|---|---|---|

| --text | — | Embed one string (mutually exclusive with --jsonl) |

| --jsonl | — | Embed many; each line must have a text field |

| --output | required | Output path |

| --model | qwen/qwen3-embedding-8b | Any embedding model on OpenRouter |

| --batch-size | 32 | Records per API call (jsonl mode) |

| --dimensions | — | Optional: truncate to N dims if supported |

Endpoint

POST /api/v1/embeddings — OpenAI-compatible schema.

Request:

{ "model": "qwen/qwen3-embedding-8b", "input": ["text1", "text2", ...] }

Response:

{ "data": [ { "embedding": [0.01, -0.02, ...], "index": 0 }, ... ], "model": "...", "usage": {...} }

Notes

qwen3-embedding-8b outputs high-dimensional dense vectors suitable for semantic similarity, clustering, RAG.
For cheaper batches, consider qwen/qwen3-embedding-4b or other listed embedding models (GET /api/v1/embeddings/models).

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How to use it

Copy the folder

Take qinghonglin/openrouter-embeddings from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.