assignee-normalization
The assignee-normalization skill standardizes patent assignee names across different patent offices and maps corporate relationships including parent companies, subsidiaries, and acquired entities. Use this when processing patent data that contains inconsistent assignee naming conventions or when you need to identify corporate group structures and affiliations across multiple patent databases.
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine /tmp/assignee-normalization && cp -r /tmp/assignee-normalization/skills/assignee-normalization ~/.claude/skills/assignee-normalizationSKILL.md
# Assignee Normalization Standardizes patent assignee names across different patent offices and identifies corporate group affiliations (parent companies, subsidiaries, acquired entities). <!-- BEGIN available-tables (generated) --> ## Available SOPs Optional, no fixed order; the final leaf is always a sop. | SOP | When to use | | --- | --- | | spawn-agent | Spawn a customized CC subagent with full MCP tool access. Used by SOPs that declare execution: subagent. | <!-- END available-tables (generated) -->
Experiment-specific - summarize the DARE executor's research design into a clean research_result report, forced to write back into the spec file produced by formated-specs.
Experiment-specific - replaces writing-specs, emits DARE's 4-layer call plan as a clean research_graph schema. Last step forces load formated-result.
loss-1 judge - read a sample's full dialogue and decide whether the user simulator semantically enacted its Policy Card. check-blind.
loss-2 judge - pairwise quality comparison across the n rungs within one topic; decide monotonicity and endpoint separation. check-blind, D1-D5 only.
Strategy: Inference to the best explanation in the face of anomalies
Remove components one by one, observe system changes to reveal hidden
Map system architecture to ablatable units for ablation studies
Design ablation studies to isolate component contributions in ML systems