[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-bytedance-systematic-literature-review-pl":3,"guides-for-bytedance-systematic-literature-review":241,"similar-k17a0hkkqxsvd50jg26542cxgn8662c8":242},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":21,"identity":186,"isFallback":191,"parentExtension":192,"providers":193,"relations":198,"repo":200,"workflow":238},1778053100136.247,"k17a0hkkqxsvd50jg26542cxgn8662c8",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":12,"sourceUrl":13,"tags":14},"Use this skill when the user wants a systematic literature review, survey, or synthesis across multiple academic papers on a topic. Also covers annotated bibliographies and cross-paper comparisons. Searches arXiv and outputs reports in APA, IEEE, or BibTeX format. Not for single-paper tasks — use academic-paper-review for reviewing one paper.",{},"Systematic Literature Review Skill","https://github.com/bytedance/deer-flow/tree/HEAD/skills/public/systematic-literature-review",[15,16,17,18,19,20],"research","literature-review","academic","arxiv","synthesis","reporting",{"_creationTime":22,"_id":23,"extensionId":5,"locale":24,"result":25,"trustSignals":175,"workflow":184},1778053169012.8342,"kn73pvgwkdt14w56jer5tgarf9866kes","en",{"checks":26,"evaluatedAt":165,"extensionSummary":166,"promptVersionExtension":167,"promptVersionScoring":168,"rationale":169,"score":170,"summary":171,"tags":172,"targetMarket":173,"tier":174},[27,32,35,38,42,45,49,53,57,60,64,69,72,76,79,82,85,88,91,94,97,101,105,110,114,117,120,123,127,130,133,136,139,142,146,149,152,155,158,162],{"category":28,"check":29,"severity":30,"summary":31},"Practical Utility","Problem relevance","pass","The description clearly states the extension's purpose: performing systematic literature reviews, surveys, and syntheses across academic papers, and explicitly mentions use cases like annotated bibliographies and cross-paper comparisons, directly addressing user needs.",{"category":28,"check":33,"severity":30,"summary":34},"Unique selling proposition","The skill offers significant value over a simple prompt by automating the complex multi-stage process of searching academic databases, extracting structured metadata in parallel via sub-agents, synthesizing findings, and formatting reports in specified academic styles, which is beyond basic LLM capabilities.",{"category":28,"check":36,"severity":30,"summary":37},"Production readiness","The extension outlines a complete, albeit complex, workflow from planning and searching to parallel metadata extraction, synthesis, and final report generation with various formatting options, suggesting it is production-ready for its stated use case.",{"category":39,"check":40,"severity":30,"summary":41},"Scope","Single responsibility principle","The skill focuses solely on systematic literature reviews and related tasks, clearly delineating its scope and explicitly stating what it does NOT do (single-paper reviews, general web research), aligning with the principle of single responsibility.",{"category":39,"check":43,"severity":30,"summary":44},"Description quality","The description accurately reflects the skill's functionality, clearly stating its purpose, supported formats (APA, IEEE, BibTeX), data sources (arXiv), and distinguishing it from similar skills like 'academic-paper-review'.",{"category":46,"check":47,"severity":30,"summary":48},"Invocation","Scoped tools","The skill utilizes a specific bundled script (`arxiv_search.py`) for its search needs and delegates metadata extraction to subagents via the `task` tool, avoiding general-purpose `shell_exec` or arbitrary command execution.",{"category":50,"check":51,"severity":30,"summary":52},"Documentation","Configuration & parameter reference","The SKILL.md thoroughly documents all parameters for the `arxiv_search.py` script (topic, max-results, category, sort-by, start-date, end-date) and explains their usage and defaults, with clear instructions on how to interact with the user.",{"category":39,"check":54,"severity":55,"summary":56},"Tool naming","not_applicable","The skill does not expose user-facing tools directly; its functionality is invoked through the skill itself, thus this check is not applicable.",{"category":39,"check":58,"severity":30,"summary":59},"Minimal I/O surface","The `arxiv_search.py` script takes well-defined parameters, and the expected output is a structured JSON array of paper metadata, adhering to minimal and documented I/O.",{"category":61,"check":62,"severity":30,"summary":63},"License","License usability","The repository includes a standard MIT License file, which is a permissive open-source license.",{"category":65,"check":66,"severity":67,"summary":68},"Maintenance","Commit recency","critical","There are no commits on the default branch as of today's date (2026-05-06), indicating the project is likely unmaintained and poses a significant risk due to potential lack of updates for dependencies or underlying APIs.",{"category":65,"check":70,"severity":30,"summary":71},"Dependency Management","The project is set up using standard Python and Node.js package managers (`pyproject.toml`, `Makefile`), and the `make install` command suggests dependency management is handled appropriately, though specific vulnerability checks are not explicitly detailed.",{"category":73,"check":74,"severity":55,"summary":75},"Security","Secret Management","The skill does not appear to use or handle any secrets directly; it relies on external tools and sub-agents, and there are no indications of secrets being echoed in output.",{"category":73,"check":77,"severity":30,"summary":78},"Injection","The script `arxiv_search.py` correctly handles input parameters via `argparse` and URL encoding, and the skill's design relies on structured data extraction rather than executing loaded external content.",{"category":73,"check":80,"severity":30,"summary":81},"Transitive Supply-Chain Grenades","The skill does not fetch remote code or data at runtime, and all scripts and dependencies are bundled within the repository, preventing supply-chain risks from external sources.",{"category":73,"check":83,"severity":30,"summary":84},"Sandbox Isolation","The skill's core logic involves API calls and data processing; it does not appear to modify files outside its designated workspace or bundle, and the use of Docker or local execution environments suggests isolation.",{"category":73,"check":86,"severity":30,"summary":87},"Sandbox escape primitives","There are no indications of detached process spawns or retry loops around denied tool calls within the provided script or SKILL.md instructions.",{"category":73,"check":89,"severity":30,"summary":90},"Data Exfiltration","The script only queries the public arXiv API and processes its results; there are no outbound calls to undocumented domains or attempts to exfiltrate confidential data.",{"category":73,"check":92,"severity":30,"summary":93},"Hidden Text Tricks","The bundled files (SKILL.md, Python scripts, templates) do not contain any hidden text, invisible characters, or other tricks designed to steer the model without curator visibility.",{"category":73,"check":95,"severity":30,"summary":96},"Opaque code execution","The `arxiv_search.py` script is plain Python code, and the overall skill structure relies on readable markdown and standard tool usage, with no signs of obfuscation, base64 payloads, or runtime code fetching.",{"category":98,"check":99,"severity":30,"summary":100},"Portability","Structural Assumption","The skill makes reasonable assumptions about its execution environment (Python 3.12+) and relies on bundled scripts and tools, with clear instructions for setup and execution, and no apparent reliance on specific user project structures.",{"category":102,"check":103,"severity":55,"summary":104},"Trust","Issues Attention","Issue data (opened/closed counts) is not available, therefore this check cannot be performed.",{"category":106,"check":107,"severity":108,"summary":109},"Versioning","Release Management","warning","There is no explicit version field in any manifest file (SKILL.md, package.json), no GitHub releases, and no CHANGELOG.md. The installation instructions do not reference a specific version, implying the `main` branch is used, which is a weak signal for version management.",{"category":111,"check":112,"severity":30,"summary":113},"Code Execution","Validation","The `arxiv_search.py` script uses `argparse` for command-line argument parsing and handles URL encoding for search queries, indicating a basic level of input validation.",{"category":73,"check":115,"severity":30,"summary":116},"Unguarded Destructive Operations","The skill is primarily read-only, querying an external API and processing data. There are no destructive operations like file deletion or system changes described or implemented.",{"category":111,"check":118,"severity":30,"summary":119},"Error Handling","The Python script includes basic error handling for HTTP requests (`raise_for_status`) and general exceptions in `main`, and the SKILL.md mentions handling script failures. The use of sub-agents also implies a mechanism for handling their failures.",{"category":111,"check":121,"severity":55,"summary":122},"Logging","The skill's primary function involves API calls and data processing; explicit local audit logging of actions is not a requirement for this read-only, external-API-driven task.",{"category":124,"check":125,"severity":30,"summary":126},"Compliance","GDPR","The skill only interacts with the public arXiv API and does not handle personal user data beyond what is necessary for performing the literature search, thus posing no GDPR compliance risk.",{"category":124,"check":128,"severity":30,"summary":129},"Target market","The skill operates on publicly available academic papers via arXiv and produces reports in standard academic formats, making it globally applicable without regional restrictions.",{"category":98,"check":131,"severity":30,"summary":132},"Runtime stability","The skill relies on standard Python libraries and tools, with a clear Python 3.12+ requirement mentioned, suggesting good portability across compatible environments.",{"category":46,"check":134,"severity":30,"summary":135},"Precise Purpose","The SKILL.md and displayed description clearly define the skill's purpose (systematic literature reviews), its target user (researchers), its data source (arXiv), and its non-goals (single-paper reviews, general web search).",{"category":46,"check":137,"severity":30,"summary":138},"Concise Frontmatter","The SKILL.md frontmatter is concise, containing a clear name and description that effectively summarizes the skill's core capability and purpose.",{"category":50,"check":140,"severity":30,"summary":141},"Concise Body","The SKILL.md is well-structured and under 500 lines, effectively delegating detailed procedures and templates to separate files, adhering to progressive disclosure principles.",{"category":143,"check":144,"severity":30,"summary":145},"Context","Progressive Disclosure","The SKILL.md effectively uses progressive disclosure by linking to separate template files for APA, IEEE, and BibTeX citation formats, keeping the main skill document concise.",{"category":143,"check":147,"severity":55,"summary":148},"Forked exploration","This skill performs a structured, multi-stage process but does not involve deep code review or extensive file exploration within a sandbox that would necessitate `context: fork`.",{"category":28,"check":150,"severity":30,"summary":151},"Usage examples","The SKILL.md provides three detailed, end-to-end examples covering typical SLR requests, demonstrating user prompts, expected outputs, and parameter usage, which are plausible and ready-to-use.",{"category":28,"check":153,"severity":30,"summary":154},"Edge cases","The SKILL.md documents several failure modes for the search and extraction phases (e.g., empty results, script failures, sub-agent failures) and suggests recovery steps like broadening the topic or enabling sub-agents.",{"category":111,"check":156,"severity":55,"summary":157},"Tool Fallback","The skill does not appear to rely on an external MCP server or custom tools that would require a fallback path; it uses bundled scripts and the `task` tool.",{"category":159,"check":160,"severity":30,"summary":161},"Safety","Halt on unexpected state","The SKILL.md instructs the agent to halt and report errors for script failures and empty search results, and implicitly handles sub-agent failures, ensuring workflow stability on unexpected states.",{"category":98,"check":163,"severity":30,"summary":164},"Cross-skill coupling","The skill is designed to be self-contained and does not implicitly rely on other skills; it clearly states its scope and directs users to `academic-paper-review` for single-paper tasks, avoiding cross-skill coupling.",1778053145077,"This skill automates literature surveys by searching arXiv, extracting metadata in parallel using sub-agents, and synthesizing themes. It supports APA, IEEE, and BibTeX formats, producing detailed reports with per-paper annotations and bibliographies. The workflow is designed for breadth-first synthesis across multiple papers.","2.0.0","3.4.0","The extension is well-documented, clearly scoped, and robustly handles its primary task of systematic literature reviews. The critical finding for 'Commit recency' due to the lack of recent commits significantly impacts maintenance confidence, preventing a perfect score. The absence of explicit versioning is a minor concern.",85,"A comprehensive skill for conducting systematic literature reviews on arXiv, synthesizing findings, and generating reports in multiple academic formats.",[15,16,17,18,19,20],"global","flagged",{"codeQuality":176,"collectedAt":177,"documentation":178,"maintenance":180,"security":181,"testCoverage":183},{},1778053131678,{"descriptionLength":179,"readmeSize":8},344,{},{"hasNpmPackage":182,"smitheryVerified":182},false,{"hasCi":182,"hasTests":182},{"updatedAt":185},1778053169012,{"githubOwner":187,"githubRepo":188,"locale":24,"slug":189,"type":190},"bytedance","deer-flow","systematic-literature-review","skill",true,null,{"extract":194,"llm":197},{"commitSha":195,"license":196},"1336872b15c25d45ebcb7c1cf72369c2bdd53187","MIT",{"promptVersionExtension":167,"promptVersionScoring":168,"score":170,"targetMarket":173,"tier":174},{"repoId":199},"kd789sm7egx1h0t1jag6zzhcq98656wv",{"_creationTime":201,"_id":199,"identity":202,"providers":204,"workflow":235},1777995558409.9045,{"githubOwner":187,"githubRepo":188,"sourceUrl":203},"https://github.com/bytedance/deer-flow",{"discover":205,"github":208},{"sources":206},[207],"skills-sh",{"closedIssues90d":209,"forks":210,"homepage":211,"license":196,"openIssues90d":212,"pushedAt":213,"readmeSize":214,"stars":215,"topics":216},389,8629,"https://deerflow.tech",356,1778052455000,38642,65247,[217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234],"agent","agentic","agentic-framework","agentic-workflow","ai","ai-agents","deep-research","langchain","langgraph","llm","multi-agent","nodejs","podcast","python","langmanus","typescript","harness","superagent",{"discoverAt":236,"extractAt":237,"githubAt":237,"updatedAt":237},1777995558409,1778053102364,{"anyEnrichmentAt":239,"extractAt":240,"githubAt":239,"llmAt":185,"updatedAt":185},1778053101076,1778053100136,[],[243,271,303,322,349,368],{"_creationTime":244,"_id":245,"community":246,"display":247,"identity":256,"providers":259,"relations":265,"workflow":267},1778053148350.4187,"k1780ay7627sgcf3rwrbek3g498660jd",{"reviewCount":8},{"description":248,"installMethods":249,"name":250,"sourceUrl":251,"tags":252},"Search and analyze academic literature. Find papers, understand research methodologies, and synthesize academic findings for research projects.",{},"Academic Search","https://github.com/claude-office-skills/skills/tree/HEAD/academic-search",[15,17,253,16,254,255],"papers","citations","claude-office-skills",{"githubOwner":255,"githubRepo":257,"locale":24,"slug":258,"type":190},"skills","academic-search",{"extract":260,"llm":262},{"commitSha":261,"license":196},"9c4c7d5cd2813a8936bf2c9fdb174ea883b85a11",{"promptVersionExtension":167,"promptVersionScoring":168,"score":263,"targetMarket":173,"tier":264},98,"verified",{"repoId":266},"kd7fw7xbj58qc2z8whrrjptbed8659db",{"anyEnrichmentAt":268,"extractAt":269,"githubAt":268,"llmAt":270,"updatedAt":270},1778053151766,1778053148350,1778053561145,{"_creationTime":272,"_id":273,"community":274,"display":275,"identity":284,"providers":288,"relations":296,"workflow":298},1778053968286.4954,"k179afn14fzy4sejmjf82fgqa9867ck6",{"reviewCount":8},{"description":276,"installMethods":277,"name":278,"sourceUrl":279,"tags":280},"Searches arXiv for preprints and academic papers, retrieves abstracts, and filters by topic. Use when the user asks to find research papers, search arXiv, look up preprints, find academic articles in physics, math, CS, biology, statistics, or related fields.",{},"arXiv Search","https://github.com/langchain-ai/deepagents/tree/HEAD/libs/cli/examples/skills/arxiv-search",[15,281,18,282,230,283],"data-analytics","academic-papers","cli",{"githubOwner":285,"githubRepo":286,"locale":24,"slug":287,"type":190},"langchain-ai","deepagents","arxiv-search",{"extract":289,"llm":291,"smithery":292},{"commitSha":290,"license":196},"b108c71d0c570e16c7050c1eac482e15dc35a5ed",{"promptVersionExtension":167,"promptVersionScoring":168,"score":263,"targetMarket":173,"tier":264},{"qualityScore":293,"totalActivations":294,"uniqueUsers":295,"useCount":8,"verified":182},0.7439563,9,8,{"repoId":297},"kd76dna2fvfbnjvzcpd2cwqnyd865xz7",{"anyEnrichmentAt":299,"extractAt":300,"githubAt":301,"llmAt":302,"smitheryAt":299,"updatedAt":302},1778053994907,1778053968286,1778053969344,1778054053159,{"_creationTime":304,"_id":305,"community":306,"display":307,"identity":315,"providers":317,"relations":320,"workflow":321},1778053100136.2388,"k17ba7hx1c2htdr84qc7vc86cd867abn",{"reviewCount":8},{"description":308,"installMethods":309,"name":310,"sourceUrl":311,"tags":312},"Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like \"review this paper\", \"analyze this research\", \"summarize this study\", or \"write a peer review\".",{},"Academic Paper Review Skill","https://github.com/bytedance/deer-flow/tree/HEAD/skills/public/academic-paper-review",[15,17,313,314,226],"paper-review","analysis",{"githubOwner":187,"githubRepo":188,"locale":24,"slug":316,"type":190},"academic-paper-review",{"extract":318,"llm":319},{"commitSha":195,"license":196},{"promptVersionExtension":167,"promptVersionScoring":168,"score":263,"targetMarket":173,"tier":264},{"repoId":199},{"anyEnrichmentAt":239,"extractAt":240,"githubAt":239,"llmAt":185,"updatedAt":185},{"_creationTime":323,"_id":324,"community":325,"display":326,"identity":334,"providers":338,"relations":343,"workflow":345},1778053622473.6565,"k175dk23bnmc82m0wgkc81c51s866xz1",{"reviewCount":8},{"description":327,"installMethods":328,"name":329,"sourceUrl":330,"tags":331},"Synthesize multiple media analyses into cross-source patterns and insights. Use when you need to cross-reference analyses, find patterns across sources, or perform meta-analysis of media content.",{},"Media Meta-Analysis","https://github.com/jwynia/agent-skills/tree/HEAD/skills/general/research/tools/media-meta-analysis",[15,314,332,19,333],"media","llm-guidance",{"githubOwner":335,"githubRepo":336,"locale":24,"slug":337,"type":190},"jwynia","agent-skills","media-meta-analysis",{"extract":339,"llm":341},{"commitSha":340,"license":196},"e02ec7e226a6e4f8419fd3b88a1d8e472d421b32",{"promptVersionExtension":167,"promptVersionScoring":168,"score":342,"targetMarket":173,"tier":264},95,{"repoId":344},"kd7efn3mprpa8rd8vm5hw5ebzx864fph",{"anyEnrichmentAt":346,"extractAt":347,"githubAt":346,"llmAt":348,"updatedAt":348},1778053625386,1778053622473,1778054012696,{"_creationTime":350,"_id":351,"community":352,"display":353,"identity":362,"providers":363,"relations":366,"workflow":367},1778053148350.4348,"k173w12kz9jfzqwqp9ewbv9mf9866w3v",{"reviewCount":8},{"description":354,"installMethods":355,"name":356,"sourceUrl":357,"tags":358},"Conduct comprehensive research on any topic. Synthesize information from multiple angles, provide structured analysis, and generate detailed research reports.",{},"Deep Research","https://github.com/claude-office-skills/skills/tree/HEAD/deep-research",[15,314,19,359,360,361],"report","investigation","documentation",{"githubOwner":255,"githubRepo":257,"locale":24,"slug":223,"type":190},{"extract":364,"llm":365},{"commitSha":261,"license":196},{"promptVersionExtension":167,"promptVersionScoring":168,"score":342,"targetMarket":173,"tier":264},{"repoId":266},{"anyEnrichmentAt":268,"extractAt":269,"githubAt":268,"llmAt":270,"updatedAt":270},{"_creationTime":369,"_id":370,"community":371,"display":372,"identity":381,"providers":385,"relations":389,"workflow":391},1778053327521.5806,"k17438c14kb8zmd41gvs9dwc91866q2h",{"reviewCount":8},{"description":373,"installMethods":374,"name":375,"sourceUrl":376,"tags":377},"Academic research assistant for literature reviews, paper analysis, and scholarly writing. Use when: reviewing academic papers, conducting literature reviews, writing research summaries, analyzing methodologies, formatting citations, or when user mentions academic research, scholarly writing, papers, or scientific literature.",{},"Academic Researcher","https://github.com/shubhamsaboo/awesome-llm-apps/tree/HEAD/awesome_agent_skills/academic-researcher",[17,15,378,16,379,380],"writing","paper-analysis","citation",{"githubOwner":382,"githubRepo":383,"locale":24,"slug":384,"type":190},"shubhamsaboo","awesome-llm-apps","academic-researcher",{"extract":386,"llm":388},{"commitSha":387,"license":196},"a35897449fe8b0fab12e8f0fd9f2e2a40e872ab7",{"promptVersionExtension":167,"promptVersionScoring":168,"score":342,"targetMarket":173,"tier":264},{"repoId":390},"kd73kvct1kme7748mpsbddhhmx865wd3",{"anyEnrichmentAt":392,"extractAt":393,"githubAt":392,"llmAt":394,"updatedAt":394},1778053329769,1778053327521,1778053376632]