nano-banana-pro-prompts-recommend-skill
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Given an arXiv ID or URL, fetch the paper, spawn sub-agents to read its key citations, and write an executive summary under.
Suivez les instructions du dépôt pour votre agent. Consultez les fichiers et dépendances avant l’installation.
SkillHub CLI
skillhub install damoonsh/paper-digestExaminez la source et la compatibilité, puis suivez les étapes d’installation.
Donnez le lien à votre agent et demandez-lui d’examiner instructions et dépendances avant d’expliquer les étapes.
arxiv_id: bare ID like 2305.11206 or full https://arxiv.org/abs/2305.11206 (Normalise: strip the URL prefix, extract bare ID.)Try HTML first: web.fetch https://arxiv.org/html/<arxiv_id>
paper_text.If the paper_text is retrieved then write the summary to ~/.openclaw/workspace/papers/<arxiv-id>.md.
Note: DO NOT DO THIS STEP INSIDE sub-agents
Within the main agent and from paper_text, identify at most 5 citations the paper most directly builds on. Prioritize:
For each citation, extract either the arXiv ID or the title. Then resolve to an arXiv URL:
https://arxiv.org/html/<id>
https://arxiv.org/search/?query=<title>&searchtype=all and take the first match.Note: Ensure that the sub-agent related task is precise and concise so the sub-agent does not have to re-read the previously read SKILLs and files.
For each resolved citation:
~/.openclaw/workspace/papers/<arxiv-id>.md, if it does then skip and consider the sub-agent concluded.~/.openclaw/workspace/papers/<arxiv-id>.md.Ces informations proviennent de l’analyse indexée et peuvent être anciennes. Vérifiez les fichiers actuels.
# Paper Digest ## Input - `arxiv_id`: bare ID like `2305.11206` or full `https://arxiv.org/abs/2305.11206` (Normalise: strip the URL prefix, extract bare ID.) ## Step 1 — Fetch the main paper Try HTML first: web.fetch https://arxiv.org/html/<arxiv_id> - If HTTP 200 → use this as `paper_text`. - If Falis: add v1 at the end, and try again: web.fetch https://arxiv.org/html/<arxiv_id>v1 - else if all fails skip and ABORT! If the `paper_text` is retrieved then write the summary to `~/.openclaw/workspace/papers/<arxiv-id>.md`. ## Step 2 — Extract citations Note: DO NOT DO THIS STEP INSIDE sub-agents Within the `main agent` and from `paper_text`, identify **at most 5 citations** the paper most directly builds on. Prioritize: - Papers that are **explicitly extended** or improved upon - Papers used as the **primary baseline** for comparison - Papers that **provide the core architecture** this work adopts - Papers **referred to repeatedly** (not just mentioned once) or that provide essential context For each citation, extract either the `arXiv ID` or the **title**. Then resolve to an arXiv URL: - If an arXiv ID is in the reference → `https://arxiv.org/html/<id>` - Otherwise search `https://arxiv.org/search/?query=<title>&searchtype=all` and take the first match. ## Step 3 — Spawn sub-agents for citations ***Note***: Ensure that the sub-agent related task is precise and concise so the sub-agent does not have to re-read the previously read SKILLs and files. For each resolved citation: - Check if the file for citation exists: `~/.openclaw/workspace/papers/<arxiv-id>.md`, if it does then skip and consider the sub-agent concluded. - If the previous step fails then spawn a sub-agent with this EXACT instruction in VERBATIM: - Fetch https://arxiv.org/html/<citation_id> (or add v1 at the end, and try again: web.fetch https://arxiv.org/html/<arxiv_id>v1). If unavailable, SKIP. If retrieved then Write the summary to `~/.openclaw/workspace/papers/<arxiv-id>.md`. ## Step...
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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