Bilibili Video Deep Analysis Expert

Problem

When reviewing Bilibili videos, creators and operators often get stuck on surface metrics such as views and likes, without understanding why a video earns saves, comments, or shares. This skill turns that ambiguity into a structured analysis: it isolates the video’s basic metadata, observable performance, danmaku sentiment, and reusable content patterns, so decisions can be compared, challenged, and applied rather than guessed.

How It Works

The skill approaches a Bilibili video as a content system instead of a single narrative clip:

• Extract video data: collect title, tags, and interaction signals to create a baseline.
• Analyze danmaku sentiment: identify recurring themes, strong reactions, complaints, and viewer intent.
• Derive operational insights: infer why the video may perform well, which structure is worth borrowing, and which title formulas can be reused.
• Generate actionable recommendations: convert findings into topic angles, opening hooks, pacing choices, and content formats.

For the full workflow, it expects the agent to read references/core_workflow.md first, then follow its trigger rules, terminology constraints, data-source limits, step sequence, self-check list, and cautions. Field collection is usually iterative, with results recorded in JSON for downstream comparison.

Boundaries

It fits Bilibili content operations, competitor teardowns, topic planning, and content research. The output depends on visible data and a sample of danmaku, so it cannot replace A/B testing. Danmaku reflects fragmented viewer feedback rather than a consensus, and recommendations should still be filtered by account positioning, compliance, and platform rules.