Agent skill
bioinforw-data-plotting
Recommend and adapt scientific plotting templates from a local Bioinforw or NGplot catalog for research data visualization. Use when Codex needs to choose a chart type from `catalog_enriched.jsonl`, refresh `embedding_input.jsonl`, return top-k templates with Python/R code paths, map user columns to template-required columns, or turn a research plotting request into an executable plotting starting point.
Install this agent skill to your Project
npx add-skill https://github.com/cihebi2/codex_skills/tree/main/bioinforw-data-plotting
SKILL.md
Bioinforw Data Plotting
Overview
Prefer the local template catalog before inventing a chart from scratch. Start by matching the user's data and analysis goal to existing templates, then adapt the selected Python or R example code to the user's columns and file paths.
Workflow
- Discover the local catalog. Prefer
bioinforw_ngplot_dump/catalog_enriched.jsonl. If the user workspace does not contain one, fall back to the bundled asset catalog underassets/bioinforw_ngplot_dump/. - Refresh
embedding_input.jsonlwhen it is missing or stale. Usescripts/build_embedding_input.py. The script can auto-discover the dump root from the current workspace. - Convert the user request into a structured query when possible. Capture analysis goal, table count, variable roles, grouping, statistical annotation needs, must-have visual features, and families to avoid. Use
references/request-format.md. - Rank templates with
scripts/recommend_bioinforw_templates.py. Prefer JSON output for downstream use. - Return the best matches with:
- template id and name
- resolved
chart_familyand originalsource_chart_family - ranking reasons
python_path,r_path,svg_path,preview_path, anddata_paths
- If the user wants an actual figure, open the selected template's
python_example.pyorR_example.R, map the user's columns torequired_columns, and modify that code in the current repo instead of rewriting everything. - If no template fits, state that clearly and explain the nearest matching families instead of forcing an unrelated chart.
Query Construction
- Record the user's scientific question first: comparison, distribution, trend, correlation, composition, hierarchy, network, geography, or significance.
- Normalize variables into roles such as
group,category,measurement,time,source,target,geo, orp_value. - Prefer a structured JSON query when the user provides schema details. Fall back to raw text only when the request is underspecified.
- Penalize templates that conflict with hard constraints such as
不分组, missing statistics, wrong table count, or banned families.
Commands
Run from the user's workspace root when possible.
python C:\Users\ciheb\.codex\skills\bioinforw-data-plotting\scripts\build_embedding_input.py
python C:\Users\ciheb\.codex\skills\bioinforw-data-plotting\scripts\recommend_bioinforw_templates.py --query-file .\bioinforw_ngplot_dump\recommend_query_example.json --top-k 8 --format json
Pass an explicit path when the catalog is not under the current working directory.
Resources
scripts/build_embedding_input.py: Regenerateembedding_input.jsonlfromcatalog_enriched.jsonl.scripts/recommend_bioinforw_templates.py: Score templates against natural-language or structured requests and return reusable code paths.references/request-format.md: Structured query fields, examples, and response expectations.assets/bioinforw_ngplot_dump/: Bundled345-template snapshot withcatalog_enriched.jsonl,embedding_input.jsonl, local index page, preview images, rendered SVGs, example Python/R code, and example input data.
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