# Send Academic Paper Summarizer to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "paper-summarize-academic", "name": "Academic Paper Summarizer", "source": "tencent", "type": "skill", "category": "AI 智能", "sourceUrl": "https://clawhub.ai/nomorecoding/paper-summarize-academic", "canonicalUrl": "https://clawhub.ai/nomorecoding/paper-summarize-academic", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/paper-summarize-academic", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=paper-summarize-academic", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "README.md", "SKILL.md", "USAGE_EXAMPLE.md", "_meta.json", "templates/sop_templates.ts" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/paper-summarize-academic" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/paper-summarize-academic", "downloadUrl": "https://openagent3.xyz/downloads/paper-summarize-academic", "agentUrl": "https://openagent3.xyz/skills/paper-summarize-academic/agent", "manifestUrl": "https://openagent3.xyz/skills/paper-summarize-academic/agent.json", "briefUrl": "https://openagent3.xyz/skills/paper-summarize-academic/agent.md" } } ``` ## Documentation ### Paper Summarize Skill This skill provides academic-grade paper summarization with dynamic Standard Operating Procedure (SOP) selection based on paper topic classification. ### Capabilities Dynamic SOP Selection: Automatically selects appropriate analysis template based on paper type (method, dataset, multimodal, etc.) Rigorous Analysis: Follows top-tier conference review criteria (NeurIPS/ICML/ICLR/ACL) Structured Output: Generates comprehensive summaries with methodology critique, experimental assessment, strengths/weaknesses Local File Storage: Saves summaries to organized directory structure with proper naming Prompt Tracking: Maintains record of actual prompts used for reproducibility Dataset Focus: Explicit attention to training/evaluation datasets used in experiments ### Supported Paper Types method: Algorithm/architecture papers dataset: Dataset/benchmark papers multimodal: Cross-modal learning papers tech_report: System/model release papers application: Applied AI papers survey: Survey/review papers rl_alignment: RL/Alignment/Safety papers speech_audio: Speech/audio processing papers benchmark: Evaluation/benchmark papers analysis: Empirical analysis papers ### Input Requirements Paper title, authors, abstract Topic classification (one of supported types) Research context (keywords, subtopics) ### Output Format Local file: {paper_title}.md in research/{domain}/ai_summaries/ Content structure: Paper information (title, authors, venue, links) Core contribution summary Methodology critique (2000+ words) Experimental assessment (1000+ words, with dataset focus) Strengths and weaknesses Critical questions for authors Impact assessment ### Quality Standards Methodology Critique: 2000+ characters, deep technical analysis including pipeline, novelty, mathematical principles, assumptions, prior art comparison, computational cost, and failure modes Experimental Assessment: 1000+ characters, rigorous evaluation with explicit focus on datasets used for training and testing, protocol rigor, baseline fairness, ablation completeness, and statistical significance Overall Analysis: 3000+ characters, critical perspective Technical Precision: Correct terminology, specific method names, exact metrics ### Workflow Integration This skill integrates with the broader research workflow: Paper Discovery: Works with arXiv search results Quality Filtering: Processes papers that pass relevance screening Batch Processing: Can be called repeatedly for multiple papers Report Generation: Outputs feed into final research report ### Configuration SOP templates are defined in: src/lib/agents/topic-sops.ts (primary location) summarization_prompt.ts (backup/reference) Both files contain identical SOP definitions with shared output format requirements. ### Examples # Summarize a method paper paper_summarize --title "SongEcho: Cover Song Generation" --topic "method" --abstract "..." --authors "..." # Summarize a dataset paper paper_summarize --title "MusicSem: Language-Audio Dataset" --topic "dataset" --abstract "..." --authors "..." ### Files Created research/{domain}/ai_summaries/{paper_title}.md research/{domain}/prompts/{paper_title}_prompt.txt Directory structure automatically created if missing ## Trust - Source: tencent - Verification: Indexed source record - Publisher: nomorecoding - Version: 1.0.1 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-04-23T16:43:11.935Z - Expires at: 2026-04-30T16:43:11.935Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/paper-summarize-academic) - [Send to Agent page](https://openagent3.xyz/skills/paper-summarize-academic/agent) - [JSON manifest](https://openagent3.xyz/skills/paper-summarize-academic/agent.json) - [Markdown brief](https://openagent3.xyz/skills/paper-summarize-academic/agent.md) - [Download page](https://openagent3.xyz/downloads/paper-summarize-academic)