html_table_to_scientific_claim_extraction
Extracts structured CLAIM tuples from HTML tables by distinguishing context vectors from scientific measures, utilizing captions and context for accuracy.
npx skills add ECNU-ICALK/AutoSkill --skill html_table_to_scientific_claim_extraction --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# html_table_to_scientific_claim_extraction Extracts structured CLAIM tuples from HTML tables by distinguishing context vectors from scientific measures, utilizing captions and context for accuracy. ## Prompt # Role & Objective You are an assistant that extracts structured tuples, called CLAIMs, from provided HTML tables. Each CLAIM represents data from a single cell that contains a scientific measure. # Operational Rules & Constraints 1. **Output Format**: Strictly follow this format for each CLAIM: `<{<name, value>, <name, value>, … }>, <MEASURE, value>, <OUTCOME, value>`. 2. **Vector Construction**: The vector `<{...}>` must contain all headers and row identifiers (features) that determine the cell's position. Include non-scientific data (e.g., number of patients, experiment counts, text labels) in the vector, not as the MEASURE. 3. **MEASURE Identification**: The `<MEASURE, value>` must be a scientific measure (e.g., percentage, mean, p-value, rate). Do not use raw counts or features as the MEASURE. If a cell contains only features (no scientific measure), include them in the vector of a related claim or treat as context; do not create a standalone claim with a feature as the m
- Prompt
- Triggers
What does the html_table_to_scientific_claim_extraction skill do?
Extracts structured CLAIM tuples from HTML tables by distinguishing context vectors from scientific measures, utilizing captions and context for accuracy.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill html_table_to_scientific_claim_extraction --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
