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Bilibili

作者 AGImodel · GitHub ↗ · v1.0.0 · MIT-0
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在 OpenClaw 中安装
/install bilibili
功能描述
Design videos for cultural resonance on Bilibili. Analyze danmu psychology, meme triggers, collective reaction points, and community-native emotional beats t...
使用说明 (SKILL.md)

Bilibili

A good Bilibili video does not just get watched. It gets answered by the crowd.

Bilibili is a cultural resonance engine for danmu-native video design.

This skill is built for creators who want more than views. It is for videos that trigger:

  • collective commentary
  • synchronized reactions
  • community jokes
  • quote-worthy moments
  • repeatable meme energy
  • “I need to send this to a friend” resonance

Use this skill when you need to:

  • design stronger danmu moments
  • plant collective reaction triggers inside a video
  • identify where a viewer will want to吐槽, 共鸣, 站队, or刷梗
  • turn a script into a Bilibili-native engagement structure
  • build deeper community participation instead of passive viewing

This skill does NOT:

  • guarantee virality
  • replace editing, filming, or thumbnail design
  • optimize for every platform equally
  • act as a generic short-video growth hack tool

What This Skill Does

Bilibili helps:

  • identify likely danmu trigger points
  • design “槽点”, “梗点”, and “共鸣点”
  • structure videos for community response rather than passive consumption
  • improve comment, danmu, and rewatch potential
  • turn one-way content into two-way crowd participation

Best Use Cases

  • ACG / 二次元 / 游戏内容
  • commentary and reaction-driven videos
  • campus / youth culture content
  • emotionally resonant storytelling
  • meme-heavy editing plans
  • script review for Bilibili-native engagement
  • finding “名场面” insertion points

What to Provide

Useful input includes:

  • video topic
  • script or outline
  • target audience
  • intended tone
  • whether the goal is 搞笑, 共鸣, 吐槽, 热血, 反转, or discussion
  • where you think the current weak parts are
  • whether you want script help, beat design, or danmu trigger analysis

Standard Output Format

BILIBILI RESONANCE ASSESSMENT ━━━━━━━━━━━━━━━━━━━━━━━━━━ Video Goal: [What reaction this video should create] Audience Mode: [Who this is for] Resonance Type: [吐槽 / 梗 / 共情 / 站队 / 高能 / 名场面]

CORE TRIGGERS ━━━━━━━━━━━━━━━━━━━━━━━━━━

  • [Trigger point 1] — [why viewers will respond]
  • [Trigger point 2] — [why viewers will respond]
  • [Trigger point 3] — [why viewers will respond]

DANMU MOMENTS ━━━━━━━━━━━━━━━━━━━━━━━━━━ [Timestamp / segment idea] → [Likely danmu reaction] [Timestamp / segment idea] → [Likely danmu reaction] [Timestamp / segment idea] → [Likely danmu reaction]

RESONANCE RISKS ━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ [Too flat] ⚠️ [Too generic] ⚠️ [No shared emotional hook] ⚠️ [No meme or reaction anchor]

RECOMMENDED FIXES ━━━━━━━━━━━━━━━━━━━━━━━━━━

  1. [How to sharpen the槽点]
  2. [How to create a stronger梗点]
  3. [How to improve collective reaction probability]

NEXT STEP ━━━━━━━━━━━━━━━━━━━━━━━━━━

  • [What to rewrite / add / cut / exaggerate next]

Danmu Psychology Lens

When analyzing a Bilibili video, ask:

  • Where will viewers feel “I need to say something here”?
  • Where does the content invite collective emotion rather than solo viewing?
  • Is there a shared joke, contradiction, or emotional spike strong enough to trigger live response?
  • Does the video contain moments that are easy to quote, repeat, or mimic?
  • Is there a “群体观看感” or is it just a normal video placed on Bilibili?

Resonance Principles

  • community reaction beats passive clarity
  • collective emotion is stronger than isolated information
  • meme potential often starts from contrast, exaggeration, or recognizable pain
  • a weak槽点 produces silence
  • a strong共鸣点 makes viewers feel seen
  • a good名场面 is legible within seconds
  • danmu is not decoration; it is part of the content layer

Execution Protocol (for AI agents)

When user asks for Bilibili-oriented content help, follow this sequence:

Step 1: Parse content intent

Extract:

  • topic
  • audience
  • emotional goal
  • format
  • current script or outline
  • what kind of resonance is desired

Step 2: Identify reaction architecture

Classify desired engagement:

  • 吐槽
  • 共情
  • 反转
  • 热血
  • 站队
  • 名场面

Step 3: Find response gaps

Check whether the content lacks:

  • clear trigger points
  • emotional peaks
  • community-native references
  • quotable lines
  • contradiction or tension
  • reward for audience participation

Step 4: Design danmu moments

Suggest:

  • where viewers will likely comment
  • what style of reaction is likely
  • what line, beat, cut, or image should be sharpened

Step 5: Improve resonance

Return:

  • strongest reaction points
  • weak areas causing silence
  • edits that improve collective response
  • whether the content fits Bilibili specifically or feels cross-platform generic

Step 6: Guardrails

If content depends on trends, niche fandom knowledge, or platform-specific references not provided:

  • say so clearly
  • do not fake cultural certainty
  • ask for more audience context if needed

Activation Rules (for AI agents)

Use this skill when the user asks about:

  • Bilibili content strategy
  • danmu-friendly video design
  • meme triggers in videos
  • collective audience reaction
  • 共鸣 / 吐槽 / 梗点 design
  • how to make a video more B站-native

Do NOT use this skill when:

  • the user only wants generic SEO advice
  • the user is asking about pure editing software setup
  • the user wants a broad multi-platform strategy with no Bilibili-specific angle
  • the user needs ad-buying or paid traffic mechanics

If context is ambiguous

Ask: "Do you want a Bilibili-native resonance design, or just general video optimization?"


Boundaries

This skill supports Bilibili-native engagement design and resonance analysis.

It does not replace:

  • editing execution
  • community moderation
  • copyright review
  • platform policy interpretation
  • paid traffic strategy
安全使用建议
This skill appears internally coherent and low-risk: it is instruction-only, asks for no credentials, and stays on-topic for designing Bilibili danmu/resonance. Before installing, consider: 1) Content risks — the advice is explicitly about triggering audience reaction, which can be used manipulatively; review outputs for ethical concerns and platform policy compliance. 2) Privacy — avoid pasting private or sensitive information from real users into prompts. 3) Posting behavior — confirm that the agent or other tools will not post content to Bilibili or other platforms automatically; this skill only generates design suggestions. 4) Metadata note — skill.json lists a homepage while the top metadata said none; if provenance is important, ask the publisher for clarification. If you want extra assurance, request a sample run with non-sensitive example input to verify the behavior.
功能分析
Type: OpenClaw Skill Name: bilibili Version: 1.0.0 The skill bundle is a collection of AI instructions and metadata designed to help creators optimize video content for the Bilibili platform. It contains no executable code, requests no system permissions, and focuses entirely on creative analysis of 'danmu' (bullet chat) psychology and community engagement. The instructions in skill.md are well-defined and aligned with the stated purpose without any signs of prompt injection or malicious intent.
能力评估
Purpose & Capability
Name, description, examples, and declared capabilities all align with a creator-facing analysis/design helper for Bilibili danmu/resonance. There are no unrelated requirements (no cloud creds, no binaries) that would be out of scope for this purpose. Minor metadata inconsistency: top-level metadata listed 'Homepage: none' while skill.json includes a homepage URL (https://clawhub.ai); this is likely a small packaging oversight and not a substantive risk.
Instruction Scope
SKILL.md gives a focused, stepwise runtime protocol (parse intent, identify reaction architecture, design danmu moments, recommend edits). It does not instruct the agent to read arbitrary system files, access environment variables, or transmit data to unknown endpoints. It includes guardrails to avoid fabricating cultural certainty and asks for more context when ambiguous.
Install Mechanism
No install spec and no code files that would be written to disk; the skill is instruction-only. This is the lowest-risk installation model and consistent with its purpose.
Credentials
The skill declares no required environment variables, no primary credential, and no config paths. The runtime instructions do not reference hidden credentials or external services, so there is no disproportionate access being requested.
Persistence & Privilege
always is false (normal), and the skill does not request persistent or elevated presence. Model invocation is enabled (default) which is expected for an agent skill; this by itself is not a concern since there are no additional risky permissions or installed components.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install bilibili
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /bilibili 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Bilibili Skill 1.0.0 - First release for Bilibili-native video resonance design. - Provides analysis for danmu psychology, meme triggers, and community-specific engagement points. - Offers a structured output format to identify danmu moments, reaction triggers, and resonance risks. - Guides creators in designing videos that spark collective response, not just views. - Includes best-use cases, input guidelines, and step-by-step execution protocol for Bilibili-focused content strategy.
元数据
Slug bilibili
版本 1.0.0
许可证 MIT-0
累计安装 10
当前安装数 8
历史版本数 1
常见问题

Bilibili 是什么?

Design videos for cultural resonance on Bilibili. Analyze danmu psychology, meme triggers, collective reaction points, and community-native emotional beats t... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 557 次。

如何安装 Bilibili?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install bilibili」即可一键安装,无需额外配置。

Bilibili 是免费的吗?

是的,Bilibili 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Bilibili 支持哪些平台?

Bilibili 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Bilibili?

由 AGImodel(@agimodel)开发并维护,当前版本 v1.0.0。

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