القائمة
Decode BO suggestions back to nearest real reactions via DRFP similarity.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Orchestrate an end-to-end chemistry or materials optimization study from a plain-English research question to BO execution and a paper draft.
BO execution layer — initializes a run from a resolved experiment spec, records observations, and continues through suggest/observe/report.
Initialize a BO run from a dataset or explicit search-space JSON.
Generate a final BO report and summarize optimization status.
Run an external evaluator loop for a BO run using a pre-provisioned backend id.
Design and stabilize an expensive or fragile chemistry evaluator before BO setup.
| name | bo-decode-drfp |
| description | Decode BO suggestions back to nearest real reactions via DRFP similarity. |
Use this skill when the BO engine has suggested fingerprint vectors and the user wants to find the closest real reactions from the original catalog.
uv run python -m bo_workflow.converters.reaction_drfp decode \
--catalog <CATALOG_CSV> --query '<JSON>'
Optional flags: --k <N> (number of nearest neighbors, default 3).
rank, similarity, and all non-fingerprint columns from the catalog (including the original reaction SMILES).--query accepts a JSON dict of fingerprint bit values (inline or path to a .json file), matching the format produced by suggest.--catalog can be the catalog.csv from encode, or any external CSV with matching fp_* columns (e.g. a large database of commercially available reactions). Useful for human-in-the-loop workflows where BO suggests fingerprints outside the training set.