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 skills add QinghongLin/data2story-skill --skill openrouter-embeddingsgit clone --depth 1 https://github.com/QinghongLin/data2story-skillWrote 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/qinghonglin/data2story-skill/openrouter-embeddings)<a href="https://agentmods.dev/skills/qinghonglin/data2story-skill/openrouter-embeddings"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/openrouter-embeddings/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/skills/qinghonglin/data2story-skill/openrouter-embeddings"><img src="https://agentmods.dev/badge/skills/qinghonglin/data2story-skill/openrouter-embeddings.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.00576 |
| Opus 5.5 | $0.00009 | $0.00230 |
| Sonnet 5.5 | $0.00004 | $0.00115 |
| Haiku 4.5 | $0.00002 | $0.00058 |
Grade A, and why
openrouter-embeddings 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.
What it actually says
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-8boutputs high-dimensional dense vectors suitable for semantic similarity, clustering, RAG.- For cheaper batches, consider
qwen/qwen3-embedding-4bor other listed embedding models (GET /api/v1/embeddings/models).
What ships with it
2 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.
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 · 71 lines · 22 tokens per session scan A 2db68fd3909d
openrouter-embeddings is a skill published in the GitHub repository QinghongLin/data2story-skill (161 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 576 once invoked, about $0.0001 per session on Opus 5.5. 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.
openrouter-embeddings compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| openrouter-embeddingsQinghongLin/data2story-skill | Skill | 161 | 4mo ago | MIT | A | 22 | Claude Code |
| aidp-knowledge-basesoracle-samples/oracle-aidp-samples | Skill | 58 | 2d ago | UPL-1.0 | A | 125 | Claude Code |
| aidp-toolsoracle-samples/oracle-aidp-samples | Skill | 58 | 2d ago | UPL-1.0 | A | 107 | Claude Code |
| rag-pipelineparam087/agent-ml-skills | Skill | 13 | 5mo ago | MIT | A | 40 | Claude Code, Codex |
| embeddingsruvnet/ruflo | Skill | 81k | yesterday | MIT | A | 62 | Claude Code |
| agent-platform-rag-engine-managementgoogle/skills | Skill | 22k | yesterday | Apache-2.0 | A | 85 | Claude Code, Codex |
| llm-app-patternsdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 54 | Claude Code, Codex |