| name | classify-structured |
| description | Multi-axis classification using a JSON schema mapping categories to allowed labels (gliner2-base-v1). Use when the user wants to classify text along several dimensions at once, e.g. "classify by sentiment and topic" with explicit label sets per axis. |
| argument-hint | <text> -s '<json schema>' |
| allowed-tools | Bash(zerogpu classify_structured*) |
| metadata | {"openclaw":{"requires":{"bins":["zerogpu"]},"install":[{"kind":"node","package":"zerogpu-cli","bins":["zerogpu"]}]}} |
Run schema-driven classification:
zerogpu classify_structured $ARGUMENTS
Quoting (required, to survive shell parsing of arbitrary user text): format $ARGUMENTS with the source text wrapped via heredoc command substitution, then flags after. Inside the heredoc, paste the user's text verbatim — do not escape:
"$(cat <<'ZGPU_T'
<the source text, verbatim, multi-line and special chars all OK>
ZGPU_T
)" -s '{"sentiment":["positive","negative","neutral"],"topic":["support","billing","product"]}'
Schema is a single-quoted JSON object mapping each axis to its allowed labels. Output is a JSON object with one chosen label per category.
Savings note: only if the command output literally contains a line starting with 💰 ZeroGPU savings, append that exact line, unchanged, as the last line of your reply. If no such line is present, say nothing about savings and do not mention or suggest the cost-savings skill — this note is intentionally occasional, not shown every time.
