القائمة
Generate text embeddings via OpenRouter using Qwen3-Embedding-8B.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/QinghongLin/data2story-skill --skill openrouter-embeddingsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Exhaustively profile a dataset and list ALL possible analyses — distributions, correlations, rankings, trends, group comparisons, anomalies. Reads detective.json for context. Runs after the Detective/Scout, before the Editor. Outputs analyst.json with ana_xx IDs and chart-ready data_tables.
Audit a generated Data2Story blog for build correctness across ALL modalities by ACTUALLY RENDERING it in a real headless browser (when available) — catching blank/0-width charts, broken/oversized media, desktop+mobile overflow, and dead interactions that source inspection cannot see — then having vision agents review the screenshots, playtesting every interactive, and walking the flagship capability contract. Plus VIEW every image for a wrong/AI-faked subject. Fixes layout in place; sends content-correctness problems back to the owning role. Use at Stage 6 after the Programmer assembles index.html and before the Critic; re-runs each revision round. Triggers: a built index.html exists, or you need to confirm charts/media/interactions actually render.
Design the MANDATORY cinematic scroll experience (every blog ships one): a full-bleed, scroll-driven background — primarily from the Scout's verified real imagery — plus the global motion choreography, so the narrative unfolds like a film as the reader scrolls. Owns the BACKGROUND layer + page-level motion (not the Designer's per-section visuals, not the Interaction centerpiece — it stages them inside the scroll). Outputs cinematographer.json (cin_xx scenes). There is no off path: a topic with no sourceable real imagery falls back to a generative-atmosphere or data-driven-spine background, never to a bare column.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | openrouter-embeddings |
| description | Generate text embeddings via OpenRouter using Qwen3-Embedding-8B. |
Text → embedding vector via OpenRouter. Default model: qwen/qwen3-embedding-8b.
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.
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
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.
| 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 |
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": {...} }
qwen3-embedding-8b outputs high-dimensional dense vectors suitable for semantic similarity, clustering, RAG.qwen/qwen3-embedding-4b or other listed embedding models (GET /api/v1/embeddings/models).