Update to Beeblio
Snapshot Nov 7, 2026 · 06:11 UTC · version 0.2.0
Collection source: downloaded plugin package.
Instructions updated for beeblio-bibliometrics
Full description — Now says: MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps.
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Full description
Oct 29, 2026 · 00:06 UTC
ChatGPT's sandbox. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps of a research field.
Nov 7, 2026 · 06:11 UTC
MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps.
Skill instructions
Oct 29, 2026 · 00:06 UTC
ChatGPT's sandbox. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps of a research field. Use `search_literature` for focused discovery and `get_paper` for individual met...
Nov 7, 2026 · 06:11 UTC
MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps. Use `search_literature` for focused discovery and `get_paper` for individual metadat...
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--- before +++ after @@ -1,6 +1,6 @@ --- name: beeblio-bibliometrics -description: Build a reproducible bibliometric or science-mapping study for a Beeblio project using ChatGPT's sandbox. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps of a research field. +description: Build a reproducible bibliometric or science-mapping study for a Beeblio project using MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps. --- # Bibliometric study in Beeblio @@ -9,14 +9,14 @@ ## Collect and screen -Use `search_literature` for focused discovery and `get_paper` for individual metadata. For a larger corpus, collect from a documented scholarly API or dataset in ChatGPT's sandbox only when that environment has the needed access; obey the provider's terms and rate limits. Beeblio's in-app `fetch_openalex_works` is not an MCP tool. If comprehensive collection is unavailable, analyze the accessible sample and label it as such. +Use `search_literature` for focused discovery and `get_paper` for individual metadata. For a larger OpenAlex corpus, use `fetch_openalex_works` with queries or work IDs, year filters, page limits, and a new destination under `3-Analysis/` or `2-Data/derived/`. Choose `full` records when reference and affiliation detail matters; `labels` is a smaller representation. Record each returned page and count, and check `partial` and `failedRequests` before treating the corpus as complete. For other sources, collect through a documented provider only when access permits, respecting its terms and rate limits. Deduplicate records by stable work identifiers. Screen titles and abstracts against the design, record inclusion and exclusion counts, and keep the selected corpus as a coding sheet. Avoid selecting solely the most cited works: older papers have had more time to accumulate citations. If limiting corpus size, use a defensible year-aware or otherwise documented strategy and report the resulting bias. Do not interpret missing affiliation, references, or keywords as zeros. ## Compute and interpret -Use saved scripts in ChatGPT's sandbox to calculate publication and citation trends, sources, authors, countries, keywords, and themes when the metadata supports them. For co-citation, bibliographic coupling, or collaboration networks, state the edge definition, counting method, thresholds, and missing-reference coverage. Distinguish diagnostic plots from findings worth discussing. Check plotted counts against the coding sheet and label each figure with its sample size and filters. +Use saved scripts and `submit_analysis_job` to calculate publication and citation trends, sources, authors, countries, keywords, and themes when the metadata supports them. Pass collected JSON pages as `/workspace/` input paths and poll `get_analysis_job` to completion. For co-citation, bibliographic coupling, or collaboration networks, state the edge definition, counting method, thresholds, and missing-reference coverage. Distinguish diagnostic plots from findings worth discussing. Check plotted counts against the coding sheet and label each figure with its sample size and filters. Use `beeblio-data-visualization` for chart design. Write a synthesis that explains patterns, competing interpretations, retrieval bias, database coverage, and research gaps. Avoid claiming that citation counts measure study quality or that a keyword cluster proves a theoretical relationship. -Save the study design, source manifest, coding sheet, scripts, chart data, figures, and a Markdown report back to Beeblio. Use `write_text_file` for small text artifacts and `upload_artifact` for larger or binary outputs, with new paths under `3-Analysis/` and `4-Reports/`; place derived datasets under `2-Data/derived/` through `upload_artifact`. Verify required files with `stat_file` before reporting completion. If an artifact cannot be transferred, name that limitation explicitly. +Save the study design, source manifest, coding sheet, scripts, chart data, figures, and a Markdown report in Beeblio. Use `write_text_file` for small text artifacts; write generated files from the analysis job under `/workspace/3-Analysis/`, `/workspace/4-Reports/`, or `/workspace/2-Data/derived/`. Use `upload_artifact` for files created outside Beeblio. Verify required files with `stat_file` before reporting completion.
Full technical diff · 2 changed fields
changed /description
"Build a reproducible bibliometric or science-mapping study for a Beeblio project using ChatGPT's sandbox. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps of a research field."
"Build a reproducible bibliometric or science-mapping study for a Beeblio project using MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps."
changed /skill_md_contents
"---\nname: beeblio-bibliometrics\ndescription: Build a reproducible bibliometric or science-mapping study for a Beeblio project using ChatGPT's sandbox. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps of a research field.\n---\n\n# Bibliometric study in Beeblio\n\nBibliometric maps depend on a defined corpus. Start a new run under `3-Analysis/bibliometric/<run-name>/` and record a study design: question, field-specific synonyms, databases, queries, retrieval date, years, document types, language rules, screening criteria, and planned metrics. Do not reuse a fixed topic vocabulary across fields. Preserve the raw metadata and collection script or query log so another researcher can reconstruct the corpus.\n\n## Collect and screen\n\nUse `search_literature` for focused discovery and `get_paper` for individual metadata. For a larger corpus, collect from a documented scholarly API or dataset in ChatGPT's sandbox only when that environment has the needed access; obey the provider's terms and rate limits. Beeblio's in-app `fetch_openalex_works` is not an MCP tool. If comprehensive collection is unavailable, analyze the accessible sample and label it as such.\n\nDeduplicate records by stable work identifiers. Screen titles and abstracts against the design, record inclusion and exclusion counts, and keep the selected corpus as a coding sheet. Avoid selecting solely the most cited works: older papers have had more time to accumulate citations. If limiting corpus size, use a defensible year-aware or otherwise documented strategy and report the resulting bias. Do not interpret missing affiliation, references, or keywords as zeros.\n\n## Compute and interpret\n\nUse saved scripts in ChatGPT's sandbox to calculate publication and citation trends, sources, authors, countries, keywords, and themes when the metadata supports them. For co-citation, bibliographic coupling, or collaboration networks, state the edge definition, counting method, thresholds, and missing-reference coverage. Distinguish diagnostic plots from findings worth discussing. Check plotted counts against the coding sheet and label each figure with its sample size and filters.\n\nUse `beeblio-data-visualization` for chart design. Write a synthesis that explains patterns, competing interpretations, retrieval bias, database coverage, and research gaps. Avoid claiming that citation counts measure study quality or that a keyword cluster proves a theoretical relationship.\n\nSave the study design, source manifest, coding sheet, scripts, chart data, figures, and a Markdown report back to Beeblio. Use `write_text_file` for small text artifacts and `upload_artifact` for larger or binary outputs, with new paths under `3-Analysis/` and `4-Reports/`; place derived datasets under `2-Data/derived/` through `upload_artifact`. Verify required files with `stat_file` before reporting completion. If an artifact cannot be transferred, name that limitation explicitly.\n"
"---\nname: beeblio-bibliometrics\ndescription: Build a reproducible bibliometric or science-mapping study for a Beeblio project using MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps.\n---\n\n# Bibliometric study in Beeblio\n\nBibliometric maps depend on a defined corpus. Start a new run under `3-Analysis/bibliometric/<run-name>/` and record a study design: question, field-specific synonyms, databases, queries, retrieval date, years, document types, language rules, screening criteria, and planned metrics. Do not reuse a fixed topic vocabulary across fields. Preserve the raw metadata and collection script or query log so another researcher can reconstruct the corpus.\n\n## Collect and screen\n\nUse `search_literature` for focused discovery and `get_paper` for individual metadata. For a larger OpenAlex corpus, use `fetch_openalex_works` with queries or work IDs, year filters, page limits, and a new destination under `3-Analysis/` or `2-Data/derived/`. Choose `full` records when reference and affiliation detail matters; `labels` is a smaller representation. Record each returned page and count, and check `partial` and `failedRequests` before treating the corpus as complete. For other sources, collect through a documented provider only when access permits, respecting its terms and rate limits.\n\nDeduplicate records by stable work identifiers. Screen titles and abstracts against the design, record inclusion and exclusion counts, and keep the selected corpus as a coding sheet. Avoid selecting solely the most cited works: older papers have had more time to accumulate citations. If limiting corpus size, use a defensible year-aware or otherwise documented strategy and report the resulting bias. Do not interpret missing affiliation, references, or keywords as zeros.\n\n## Compute and interpret\n\nUse saved scripts and `submit_analysis_job` to calculate publication and citation trends, sources, authors, countries, keywords, and themes when the metadata supports them. Pass collected JSON pages as `/workspace/` input paths and poll `get_analysis_job` to completion. For co-citation, bibliographic coupling, or collaboration networks, state the edge definition, counting method, thresholds, and missing-reference coverage. Distinguish diagnostic plots from findings worth discussing. Check plotted counts against the coding sheet and label each figure with its sample size and filters.\n\nUse `beeblio-data-visualization` for chart design. Write a synthesis that explains patterns, competing interpretations, retrieval bias, database coverage, and research gaps. Avoid claiming that citation counts measure study quality or that a keyword cluster proves a theoretical relationship.\n\nSave the study design, source manifest, coding sheet, scripts, chart data, figures, and a Markdown report in Beeblio. Use `write_text_file` for small text artifacts; write generated files from the analysis job under `/workspace/3-Analysis/`, `/workspace/4-Reports/`, or `/workspace/2-Data/derived/`. Use `upload_artifact` for files created outside Beeblio. Verify required files with `stat_file` before reporting completion.\n"
Full snapshot data
{
"description": "Build a reproducible bibliometric or science-mapping study for a Beeblio project using MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps.",
"included_files": [],
"name": "beeblio-bibliometrics",
"skill_md_contents": "---\nname: beeblio-bibliometrics\ndescription: Build a reproducible bibliometric or science-mapping study for a Beeblio project using MCP collection and analysis tools. Use for publication trends, citation and keyword patterns, co-citation, collaboration networks, or quantitative maps.\n---\n\n# Bibliometric study in Beeblio\n\nBibliometric maps depend on a defined corpus. Start a new run under `3-Analysis/bibliometric/<run-name>/` and record a study design: question, field-specific synonyms, databases, queries, retrieval date, years, document types, language rules, screening criteria, and planned metrics. Do not reuse a fixed topic vocabulary across fields. Preserve the raw metadata and collection script or query log so another researcher can reconstruct the corpus.\n\n## Collect and screen\n\nUse `search_literature` for focused discovery and `get_paper` for individual metadata. For a larger OpenAlex corpus, use `fetch_openalex_works` with queries or work IDs, year filters, page limits, and a new destination under `3-Analysis/` or `2-Data/derived/`. Choose `full` records when reference and affiliation detail matters; `labels` is a smaller representation. Record each returned page and count, and check `partial` and `failedRequests` before treating the corpus as complete. For other sources, collect through a documented provider only when access permits, respecting its terms and rate limits.\n\nDeduplicate records by stable work identifiers. Screen titles and abstracts against the design, record inclusion and exclusion counts, and keep the selected corpus as a coding sheet. Avoid selecting solely the most cited works: older papers have had more time to accumulate citations. If limiting corpus size, use a defensible year-aware or otherwise documented strategy and report the resulting bias. Do not interpret missing affiliation, references, or keywords as zeros.\n\n## Compute and interpret\n\nUse saved scripts and `submit_analysis_job` to calculate publication and citation trends, sources, authors, countries, keywords, and themes when the metadata supports them. Pass collected JSON pages as `/workspace/` input paths and poll `get_analysis_job` to completion. For co-citation, bibliographic coupling, or collaboration networks, state the edge definition, counting method, thresholds, and missing-reference coverage. Distinguish diagnostic plots from findings worth discussing. Check plotted counts against the coding sheet and label each figure with its sample size and filters.\n\nUse `beeblio-data-visualization` for chart design. Write a synthesis that explains patterns, competing interpretations, retrieval bias, database coverage, and research gaps. Avoid claiming that citation counts measure study quality or that a keyword cluster proves a theoretical relationship.\n\nSave the study design, source manifest, coding sheet, scripts, chart data, figures, and a Markdown report in Beeblio. Use `write_text_file` for small text artifacts; write generated files from the analysis job under `/workspace/3-Analysis/`, `/workspace/4-Reports/`, or `/workspace/2-Data/derived/`. Use `upload_artifact` for files created outside Beeblio. Verify required files with `stat_file` before reporting completion.\n"
}SHA-256 of public snapshot: 3ea320b93b78a04e30ef66d5c556100cad41825fedb4c799ed313c9644473c23