Derive content from a single Note via LLM-guided extraction, compression, or transformation. Output is grounded entirely in the input — no new information is introduced.
LLM-guided extraction or transformation from a single Note. Output is grounded entirely in the input — no new information introduced.
target: Single Note (variable, ID, or name) — NOT Collectionsinstruction: What to extract or how to transform (required)target_tokens: Optional output length in tokensout: Variable name for resulting NotePrefer Python for small, predictable structure (YAML frontmatter, key:value
blocks). Use extract when layout is unknown or you need semantic judgment.
Output length is unreliable. Word-count instructions ("output a 1-3 word phrase") are frequently ignored — expect verbose output. When output will be used as a filename, directory name, or identifier, always truncate and sanitize in Python:
tokens = re.split(r'[\s_]+', raw)[:3] # split on spaces AND underscores
name = re.sub(r'[^a-z0-9_]', '', '_'.join(tokens).lower())
Do NOT use when:
extract-references (GROBID, structured, deterministic)synthesizegenerate-notefilter-structured or filter-semanticproject or pluckPrefer over synthesize when there is only ONE source document.
extract(target=$collection) — Use map(extract) for Collections.extract(target=$paper, instruction="extract citations") — Use extract-references.{"type":"extract","target":"$paper","instruction":"Extract the key architectural innovation as one sentence.","out":"$innovation"}
{"type":"extract","target":"$abstract","instruction":"Compress to 2-3 sentences retaining methodology and results.","out":"$compressed"}
{"type":"map","target":"$papers","operation":"extract","instruction":"State the main contribution in one sentence.","out":"$contributions"}