Skill v1.0.0

name: extract-signals description: Turn free text into structured signals — topics, keywords, intent, and other contextual attributes — in a single call (zlm-v1-signal-extract, 80M). Use for content enrichment, contextual intelligence, ad targeting, agent routing, recommendation, and analytics pipelines, where a general-purpose LLM would be overkill. For IAB taxonomy categories alongside these signals, use classify-iab-enriched. argument-hint: "<text>" allowed-tools: Bash(zerogpu chat_completions *) metadata: version: "1.0.0"

Run signal extraction. $ARGUMENTS is the raw source text — pass it verbatim, no escaping or quoting required (the heredoc below handles every shell metacharacter, newline, quote, and paren safely):

zerogpu chat_completions -m zlm-v1-signal-extract <<'ZGPU_END_OF_INPUT'
$ARGUMENTS
ZGPU_END_OF_INPUT

Output is a structured JSON object of the signals the model found — topics, keywords, intent, and other contextual attributes.

At 80M parameters and \$0.04 / \$0.10 per 1M input/output tokens this is built for high-volume enrichment. Its context window is 400 tokens, so send the passage you want enriched rather than a whole document. If you need IAB audience and content categories too, /zerogpu-router:classify-iab-enriched returns those alongside topics, keywords, and intent.

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 /zerogpu-router:cost-savings — this note is intentionally occasional, not shown every time.