Literature Trends
Literature Trends Skill
Trace how explanatory hypotheses evolve over time within a tagged literature corpus.
Purpose
The literature-trends skill implements an abductive argumentation approach to literature analysis: given a set of papers tagged with a keyword, identify the dominant explanatory hypotheses in successive time windows and trace their genealogy — which hypotheses replaced which, which were refined, and which were abandoned.
This is not a citation analysis tool. It is a tool for understanding how the explanatory landscape shifts in a field.
Prerequisites
- TypeDB running (
make db-start) uvinstalled- Papers ingested and tagged via the
scientific-literatureskill (the trend analysis operates onscilit-paperentities already in TypeDB)
Commands
uv run python .claude/skills/literature-trends/literature_trends.py <command> [args]
| Command | What it does |
|---|---|
create-thread |
Create a new trend thread for a keyword or topic |
record-hypothesis |
Record the dominant explanatory hypothesis for a time window |
record-genealogy |
Link two hypotheses (replacement, refinement, abandonment) |
show-thread |
Display the full hypothesis genealogy for a thread |
Typical Workflow
You: I have papers on CRISPR off-target effects tagged in my corpus.
Create a trend thread for this topic.
You: For the 2017-2019 window, the dominant explanation was
non-specific nuclease activity at mismatched sites.
Record this as hypothesis H1.
You: For 2020-2022, the field shifted to RNA-guided scanning models.
Record this as H2 and note it refined H1.
You: Show me the full genealogy for the CRISPR off-target thread.
Schema
lt-thread— a named trend analysis thread (domain-thing)lt-hypothesis— an explanatory claim associated with a time windowlt-genealogy— relation linking hypotheses (replacement / refinement / abandonment)
Relationship to Other Skills
Literature Trends depends on scientific-literature for the underlying paper corpus. Papers must be ingested and tagged before trend analysis can begin. The literature-trends skill queries scilit-paper entities and their keyword attributes to identify the relevant corpus for a thread.