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Optional polish + scoring of a draft before publish, and engagement scoring after.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
Generate a Broadcast Kit content-batch contract from Markdown, a directory, a repo path, or a publish registry.
Join raw platform metrics JSONL back to registry/manifest metadata and compute experiment scoring priors.
Fetch and normalize platform metrics into the Broadcast Kit metrics snapshot shape.
Interview the user's content-publishing goal, map it to a capability tier, recommend the minimum setup, then run the matching recipe from CATALOG.md. Use this skill for any "publish / grow / analyze on Douyin / XHS / X" request.
Publish one Douyin manifest via the in-package Playwright publisher.
Publish a tweet or thread to X via NyxID proxy (preferred) or direct API (fallback).
| name | broadcast-optimize |
| description | Optional polish + scoring of a draft before publish, and engagement scoring after. |
Use this skill when you want to polish a draft before calling broadcast-publish-*, or rank post-publish metrics.
BROADCAST_KIT_LLM_PROVIDER ∈ {openai, anthropic, ollama} + matching API key. See docs/optimizers.md for the full env set.platform (x|xhs|douyin) and body.publish_threshold is -5 in the bundled rubrics; a composite score below threshold means do NOT publish without revising.content_brain.publish_decision == "hold" as a hard stop.engagement_score is pure math; no LLM, no env. HeavyRanker weights apply to X-style records; weighted composite scoring applies to any platform with engagement metrics.Structured diagnostic (dbskill-style):
broadcast-kit optimize content-brain --draft draft.yaml
Severity-weighted reviewer (10 dimensions per bundled rubric):
broadcast-kit optimize reviewer --draft draft.yaml --max-rounds 3
Generate N variants and pick the best:
broadcast-kit optimize variants --draft draft.yaml --n 3
Rank post-publish metrics:
broadcast-kit optimize engagement --metrics state/douyin/work/metrics/default/<date>.jsonl --scorer composite
broadcast-kit optimize engagement --metrics x_posts.jsonl --scorer heavy
See docs/optimizers.md for return shapes, Python-level usage, custom rubric format, and the source of every weight.