[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MB0AC88F8DR7MC17KWNDR6":3,"skill-same:01M6MB0AC88F8DR7MC17KWNDR6":87},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6MB0AC88F8DR7MC17KWNDR6","research-knowledge-graph",null,"Turn a bibliography or literature corpus into a knowledge graph of papers, authors, venues, topics and citation links, then analyze it (citation clusters, key papers, bridging work, research gaps) or export it to Neo4j, Gephi, NetworkX, Obsidian or Markdown-graph tools such as graphify. Use when mapping a research field, building a citation network, finding influential or bridging papers, or visualizing how a literature connects. Zero-dependency builder with optional OpenAlex enrichment.","hashgraph-online\u002Fawesome-codex-plugins","hashgraph-online","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160371962?v=4","01M6CNB6NRJVNNGN14TR01Q6D3","HOL (Hashgraph Online)","01M6JT1HSKKG9V68KQAQGBA7XP",1554,"MIT","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fresearch-knowledge-graph\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"research","research knowledge graph","knowledge","knowledge graph","graph","analyze","when","obsidian","papers","literature","bridging","builder","dependency","work","access","rag","markdown","how","api","analysis","network","2026-11-10 10:12:30",false,"---\nname: research-knowledge-graph\ndescription: Turn a bibliography or literature corpus into a knowledge graph of papers, authors, venues, topics and citation links, then analyze it (citation clusters, key papers, bridging work, research gaps) or export it to Neo4j, Gephi, NetworkX, Obsidian or Markdown-graph tools such as graphify. Use when mapping a research field, building a citation network, finding influential or bridging papers, or visualizing how a literature connects. Zero-dependency builder with optional OpenAlex enrichment.\nlicense: MIT\ncompatibility: Python 3.9+ standard library; network access to api.openalex.org for --openalex. networkx optional for analysis.\nmetadata:\n  version: \"1.0\"\n  category: knowledge-and-rag\n  maintainer: Kalaris Labs\n  author: Kalaris Labs (Sayan Chowdhury)\n  tags: knowledge graph, citation network, bibliometrics, OpenAlex, Neo4j, Gephi, NetworkX, Obsidian, graphify, science mapping\n---\n\n# Research Knowledge Graphs\n\nA literature review is a graph problem in disguise: which papers everyone\nbuilds on, which clusters talk past each other, which work bridges them, and\nwhere the gaps are. This skill builds that graph from a reference library and\nhands it to whichever graph tool the user prefers.\n\n## Build\n\n```bash\n# Offline: authors, venues and keywords from the .bib itself\npython scripts\u002Fbuild_graph.py references.bib --out kg\u002F\n\n# Enriched: disambiguated authors (OpenAlex IDs, ORCID), venues, topics, and\n# citation edges between papers in the library\npython scripts\u002Fbuild_graph.py references.bib --out kg\u002F --openalex --mailto you@university.edu\n```\n\nCSL-JSON (`.json`, exported by Zotero or `reference-manager-interop`) also works as input.\n\n| Output | Use it with |\n|---|---|\n| `graph.json` | NetworkX: `nx.node_link_graph(json.load(f), edges=\"links\")`, or any JS graph lib |\n| `graph.graphml` | Gephi, yEd, Cytoscape, igraph, `nx.read_graphml` |\n| `nodes.csv`, `edges.csv` | Neo4j (`neo4j-admin database import full --nodes=nodes.csv --relationships=edges.csv`) or Gephi spreadsheet import |\n| `vault\u002F` | Obsidian (graph view), and Markdown-graph tools such as graphify, which read `[[wikilinks]]` as edges |\n\n**Schema.** Node `type` ∈ {`paper`, `author`, `venue`, `topic`}. Edge `type` ∈\n{`authored_by`, `published_in`, `about`, `cites`}. Paper nodes carry `doi`, `year`,\n`openalex`, `cited_by_count` when known.\n\n## Expand beyond the library (snowballing)\n\nCitation edges only connect papers you already have. To map a field:\n\n1. Seed with 5-20 core papers.\n2. Backward: collect each seed's `referenced_works` from OpenAlex. Forward: query\n   `https:\u002F\u002Fapi.openalex.org\u002Fworks?filter=cites:W123...` for citing works.\n3. Keep candidates cited by ≥2 seeds (co-citation), export them to `.bib`, rebuild.\n4. Stop when a round adds few new highly connected papers. Report the rounds and thresholds (reproducibility).\n\n## Analyze (NetworkX)\n\n```python\nimport json, networkx as nx\ng = nx.node_link_graph(json.load(open(\"kg\u002Fgraph.json\")), edges=\"links\")\npapers = [n for n, d in g.nodes(data=True) if d[\"type\"] == \"paper\"]\ncit = g.edge_subgraph([(u, v) for u, v, d in g.edges(data=True) if d[\"type\"] == \"cites\"]).copy()\n\ninfluential = sorted(cit.in_degree(), key=lambda x: -x[1])[:10]            # most cited within corpus\nbridges = sorted(nx.betweenness_centrality(cit.to_undirected()).items(), key=lambda x: -x[1])[:10]\ncommunities = nx.community.louvain_communities(cit.to_undirected(), seed=0)  # research clusters\n```\n\nInterpretation guidelines:\n- **High in-degree** within the corpus = foundational for *this* literature (not globally important).\n- **High betweenness** = bridges clusters, often the best papers to read for synthesis sections.\n- **Communities** = candidate subsections of a review. Name each by its top topics and most cited paper.\n- **Sparse links between clusters that share topics** = a potential research gap worth stating (and verifying).\n- Co-authorship components show research groups. Beware name-based author merging without `--openalex`.\n\n## Visualize\n\n- Gephi: open `graph.graphml` → ForceAtlas2 → size by in-degree → color by modularity class.\n- Obsidian: open `kg\u002Fvault` as a vault and use Graph view, filtering by folder (papers\u002Fauthors\u002Ftopics).\n- Small graphs in documents: convert the citation subgraph to Mermaid (`flowchart LR`, one edge per `cites`).\n\n## Integrity rules\n\n- Never invent edges or papers. Every node and edge must come from the library or the OpenAlex record.\n- Verify surprising structure, such as an apparent gap, by reading the papers before claiming it.\n\n## Caveats to state in any write-up\n\nCoverage depends on OpenAlex\u002FCrossref metadata; citation edges are only as complete as\n`referenced_works`. Keyword\u002Ftopic nodes are coarse. Always report the data source,\nretrieval date and filtering thresholds.\n\n## Related skills\n\n`paper-corpus-rag` (full-text QA over the same papers), `literature-review`,\n`systematic-review-prisma`, `networkx`, `reference-manager-interop`, `citation-verification`.\n","# Research Knowledge Graphs\n\nA literature review is a graph problem in disguise: which papers everyone\nbuilds on, which clusters talk past each other, which work bridges them, and\nwhere the gaps are. This skill builds that graph from a reference library and\nhands it to whichever graph tool the user prefers.\n\n## Build\n\n```bash\n# Offline: authors, venues and keywords from the .bib itself\npython scripts\u002Fbuild_graph.py references.bib --out kg\u002F\n\n# Enriched: disambiguated authors (OpenAlex IDs, ORCID), venues, topics, and\n# citation edges between papers in the library\npython scripts\u002Fbuild_graph.py references.bib --out kg\u002F --openalex --mailto you@university.edu\n```\n\nCSL-JSON (`.json`, exported by Zotero or `reference-manager-interop`) also works as input.\n\n| Output | Use it with |\n|---|---|\n| `graph.json` | NetworkX: `nx.node_link_graph(json.load(f), edges=\"links\")`, or any JS graph lib |\n| `graph.graphml` | Gephi, yEd, Cytoscape, igraph, `nx.read_graphml` |\n| `nodes.csv`, `edges.csv` | Neo4j (`neo4j-admin database import full --nodes=nodes.csv --relationships=edges.csv`) or Gephi spreadsheet import |\n| `vault\u002F` | Obsidian (graph view), and Markdown-graph tools such as graphify, which read `[[wikilinks]]` as edges |\n\n**Schema.** Node `type` ∈ {`paper`, `author`, `venue`, `topic`}. Edge `type` ∈\n{`authored_by`, `published_in`, `about`, `cites`}. Paper nodes carry `doi`, `year`,\n`openalex`, `cited_by_count` when known.\n\n## Expand beyond the library (snowballing)\n\nCitation edges only connect papers you already have. To map a field:\n\n1. Seed with 5-20 core papers.\n2. Backward: collect each seed's `referenced_works` from OpenAlex. Forward: query\n   `https:\u002F\u002Fapi.openalex.org\u002Fworks?filter=cites:W123...` for citing works.\n3. Keep candidates cited by ≥2 seeds (co-citation), export them to `.bib`, rebuild.\n4. Stop when a round adds few new highly connected papers. Report the rounds and thresholds (reproducibility).\n\n## Analyze (NetworkX)\n\n```python\nimport json, networkx as nx\ng = nx.node_link_graph(json.load(open(\"kg\u002Fgraph.json\")), edges=\"links\")\npapers = [n for n, d in g.nodes(data=True) if d[\"type\"] == \"paper\"]\ncit = g.edge_subgraph([(u, v) for u, v, d in g.edges(data=True) if d[\"type\"] == \"cites\"]).copy()\n\ninfluential = sorted(cit.in_degree(), key=lambda x: -x[1])[:10]            # most cited within corpus\nbridges = sorted(nx.betweenness_centrality(cit.to_undirected()).items(), key=lambda x: -x[1])[:10]\ncommunities = nx.community.louvain_communities(cit.to_undirected(), seed=0)  # research clusters\n```\n\nInterpretation guidelines:\n- **High in-degree** within the corpus = foundational for *this* literature (not globally important).\n- **High betweenness** = bridges clusters, often the best papers to read for synthesis sections.\n- **Communities** = candidate subsections of a review. Name each by its top topics and most cited paper.\n- **Sparse links between clusters that share topics** = a potential research gap worth stating (and verifying).\n- Co-authorship components show research groups. Beware name-based author merging without `--openalex`.\n\n## Visualize\n\n- Gephi: open `graph.graphml` → ForceAtlas2 → size by in-degree → color by modularity class.\n- Obsidian: open `kg\u002Fvault` as a vault and use Graph view, filtering by folder (papers\u002Fauthors\u002Ftopics).\n- Small graphs in documents: convert the citation subgraph to Mermaid (`flowchart LR`, one edge per `cites`).\n\n## Integrity rules\n\n- Never invent edges or papers. Every node and edge must come from the library or the OpenAlex record.\n- Verify surprising structure, such as an apparent gap, by reading the papers before claiming it.\n\n## Caveats to state in any write-up\n\nCoverage depends on OpenAlex\u002FCrossref metadata; citation edges are only as complete as\n`referenced_works`. Keyword\u002Ftopic nodes are coarse. Always report the data source,\nretrieval date and filtering thresholds.\n\n## Related skills\n\n`paper-corpus-rag` (full-text QA over the same papers), `literature-review`,\n`systematic-review-prisma`, `networkx`, `reference-manager-interop`, `citation-verification`.\n",{"name":5,"license":15,"metadata":50,"description":7,"compatibility":56},{"tags":51,"author":52,"version":53,"category":54,"maintainer":55},"knowledge graph, citation network, bibliometrics, OpenAlex, Neo4j, Gephi, NetworkX, Obsidian, graphify, science mapping","Kalaris Labs (Sayan Chowdhury)","1.0","knowledge-and-rag","Kalaris Labs","Python 3.9+ standard library; network access to api.openalex.org for --openalex. networkx optional for analysis.","main","2b63f856ce19c2a797929cacabd9e0fc936812fa","plugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fresearch-knowledge-graph",468,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins","https:\u002F\u002Fhol.org\u002Fplugins\u002Fbest-codex-plugins","https:\u002F\u002Fgithub.com\u002Fhashgraph-online","https:\u002F\u002Fhol.org","Open-source standards and developer tools for interoperable AI agents: discovery, identity, communication, payments, privacy and security.","2026-11-11 02:22:54",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add hashgraph-online\u002Fawesome-codex-plugins --skill research-knowledge-graph",{"tool":73,"command":74},"bunx","bunx skills add hashgraph-online\u002Fawesome-codex-plugins --skill research-knowledge-graph",{"tool":76,"command":77},"pnpm","pnpm dlx skills add hashgraph-online\u002Fawesome-codex-plugins --skill research-knowledge-graph","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fresearch-knowledge-graph\u002FSKILL.md",true,[81],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"scripts\u002Fbuild_graph.py","SCRIPT","脚本",13100,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002FKalarisLabs\u002Fresearch-agent-skills\u002Fskills\u002Fresearch-knowledge-graph\u002Fscripts\u002Fbuild_graph.py",[88,115,140,171,192,205],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":45},"01M716A6ETNB9B1SQ3GXZC75VR","deja-search","deja-vu memory — search the user's past AI coding sessions with the deja CLI. Holds what they already settled and never wrote into the repo (naming rules, banned or already-tried tools, required checks before merge, preferred values, who owns what). Use before your first edit in a task, before you call a change done or ready to merge, before you recommend a tool, library, design or reviewer, before re-debugging an error that may already be solved, when they say 'didn't we fix this before', 'what did we decide about X' or 'I already have that', and before telling them something on this machine does not exist.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-search\u002FSKILL.md",[],[96,97,98,99,100,101,102,103,104,105,106,30,107,108,109,110,111,112,113,114],"search","deja search","error","ready","never","memory","re","decide","fix","design","debugging","you","done","reviewer","io","change","cli","merge","past",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":45},"01M716A3J2W5SENSE9VGMXB375","deja-reflect","End-of-session reflection and memory curation with deja-vu. Review what was decided, what commands broke and what repaired them, and record durable project rules or decisions with deja remember so the next session starts with them.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fvshulcz\u002Fdeja-vu\u002Fskills\u002Fdeja-reflect\u002FSKILL.md",[],[122,123,124,125,101,126,127,128,129,130,131,132,133,110,134,135,136,137,138,139],"reflect","deja reflect","brew","remember","project rules","project","session","end","reflection","Rules","decisions","review","with","record","github","next","https","node",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":155,"publishedAt":45},"01M715T3RR4BAYSR57HPPTJ0PF","revid","Make, edit, export and post AI videos with Revid (revid.ai) through the Revid MCP server. Use whenever the user wants a short-form or long-form video made (from an idea, a script, a URL, an article, a PDF, a tweet, a product page, their own clips, or a song), wants to find what is going viral on TikTok, YouTube, Instagram or X before making something, wants to edit or re-export an existing Revid project, generate a standalone AI image or clip, make a series episode, or publish and schedule a finished video to connected social accounts. Covers setup (hosted MCP with OAuth, or an API key), the 36 tools, credits, polling and the rules for publishing. Routes to the more specific revid-* skills when one fits.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Ftmakersas\u002Frevid-skills\u002Fskills\u002Frevid\u002FSKILL.md",[146,149,152],{"categoryNo":147,"categoryName":148,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":150,"categoryName":151,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":153,"categoryName":154,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[142,156,157,158,159,160,127,161,102,162,163,164,165,30,166,167,168,169,42,170],"Video","short-form","standalone","PDF","viral","tiktok","short","social","MCP","song","URL","image","mcp server","schedule","publish",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":177,"publishedAt":45},"01M71569ZM2X3H5RBFK8PVKMK2","review-study","Review a QuestionPunk survey before launch for wording, answer choices, branching, and missing settings. Use for check my survey or review this draft before publishing.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fquestionpunk\u002Fcodex-plugin\u002Fskills\u002Freview-study\u002FSKILL.md",[],[178,179,133,180,181,182,183,184,185,186,187,188,189,190,191],"study","review study","for","answer","missing","survey","publishing","use","check","draft","settings","launch","my","branching",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":198,"publishedAt":45},"01M71567GV0C35YQBY0DPGR86B","manage-study","Find QuestionPunk studies, check status, publish an explicitly requested draft, or retrieve its participant link. Use for find my survey, publish this study, or how many people responded.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fquestionpunk\u002Fcodex-plugin\u002Fskills\u002Fmanage-study\u002FSKILL.md",[],[199,178,200,180,183,185,201,186,187,170,41,202,203,204,190],"manage study","manage","people","find","link","status",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":92,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":211,"publishedAt":45},"01M71565A8TCA01AZQZRGRYMFZ","create-study","Create a saved QuestionPunk survey draft from a research brief or edit specified questions. Use for requests such as make a customer feedback survey or shorten my onboarding study.","https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fquestionpunk\u002Fcodex-plugin\u002Fskills\u002Fcreate-study\u002FSKILL.md",[],[212,178,213,214,215,216,180,217,183,218,185,219,220,187,24,221,222,223,224,190],"create study","create","brief","as","feedback","customer feedback","research brief","shorten","saved","requests","customer","questions","edit"]