Lov Bp Outline logo

Lov Bp Outline

Organization
lovstudio
lov-bp-outline

Turn existing project materials into a source-backed investor BP brief, evidence ledger, and 12–15 slide outline. Use before making slides, when the product positioning is unclear, or when an existing outline is too technical, generic, or unsupported. Trigger on "写 BP 大纲", "融资叙事", "梳理商业计划书", "先不要做 PPT", "BP outline", "investor narrative", or "pitch deck outline".

Overview

Publisherlovstudio
Repositoryskills
Skill namelov-bp-outline
Stars
67
Forks
17
Bundled files
9
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by lovstudio on GitHub. Read the source before you install it.

Installation

Install the Lov Bp Outline AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/lovstudio/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/bp-outline .claude/skills/lov-bp-outline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lov Bp Outline in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Lov Bp Outline on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Lov Bp Outline is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

BP 蓝图 · BP Blueprint

Build the investment story before choosing a visual style. This skill stops at an approved, source-backed outline and does not generate PPTX/PDF.

Input

Any combination of:

  • repository, product docs, PRD, website, prior applications, or existing BP;
  • analytics exports, payment/usage data, GitHub evidence, and customer notes;
  • product screenshots, founder profile, team history, brand assets;
  • financing stage, amount, equity/instrument, and milestones.

Output Contract

text
business-plan/
├── brief.md
├── evidence-ledger.md
├── outline.md
└── assets/

The output is ready for lov-bp-deck only when the evidence gate passes.

Workflow (MANDATORY)

Step 0: Resolve the skill and workspace

Resolve this SKILL.md directory as SKILL_DIR. If the user already has a business-plan/ workspace, continue in it. Otherwise initialize one:

bash
python3 "$SKILL_DIR/scripts/init_bp.py" \
  --name "Project Name" \
  --stage seed \
  --output ./business-plan

Read references/user-config.md for portable path and brand resolution.

Step 1: Inspect before asking

Search the user's supplied scope first. Prefer exact repo/files/URLs over broad discovery. Extract known audience, financing stage, ask, product, buyer, traction, business model, market, competition, growth, and team evidence.

Ask at most one compact round of questions for missing decisions that materially change the outline. If the user says “不要问”“按推荐方案”, assume seed investors, 12–15 slides, 8–10 minutes, and mark the financing ask as a visible gap if unknown.

Step 2: Build the evidence ledger

Record every material claim as:

  • fact;
  • inference;
  • assumption;
  • missing.

Include source, as-of date, slide destination, and next action. For changing claims, verify current authoritative sources. Never substitute a broad AI forecast for a buyer-linked market calculation. Read references/evidence-and-market.md.

Step 3: Define the investor-readable product

Before the vision, draft three one-line definitions:

  1. literal category;
  2. accurate comparator (“X for Y”);
  3. category-creation language with a plain-language explanation.

Choose the line a non-technical investor can repeat after ten seconds. Separate:

text
early wedge → adjacent users → long-term market

Treat technical modes as implementation unless the buyer actually pays for the mechanism. Read references/investor-story.md.

Step 4: Write the 12–15 page argument

Use this 12-page base:

  1. one sentence: who you are;
  2. concrete user problem;
  3. how the product solves it;
  4. product demo or core experience;
  5. why now;
  6. real validation;
  7. business model;
  8. market size and wedge;
  9. competition and differentiation;
  10. growth plan;
  11. why this team;
  12. financing ask, use, and next proof.

Expand only when a product demo, moat, B2B deployment model, or financial model needs a dedicated page. Read references/deck-architecture.md.

Each slide entry must contain:

  • conclusion headline;
  • investor takeaway;
  • evidence IDs and sources;
  • exact visual/chart proof;
  • speaker purpose;
  • unresolved gap or “none”.

Step 5: Run the evidence gate

The outline is ready only when:

  • all 12 investor questions are covered;
  • the product definition is investor-readable;
  • every core number traces to evidence or a labeled assumption;
  • traction stages and denominators are not mixed;
  • TAM/SAM/SOM has a buyer-price-reachability bridge;
  • the financing ask connects to 18–24 month validation milestones;
  • no placeholder is hidden as final copy.

Report the page map and at most five evidence gaps. Do not start slide generation unless the user asked for a complete BP or explicitly continues with bp-deck.

Recommended Next Step

text
$lov-bp-deck ./business-plan/outline.md

Runtime context (shared)

运行前读取本 Skill 包的 skill.yaml,由宿主提供 skill-runtime/v1 上下文。字段解析顺序为:当前请求、项目上下文、个人 Preferences、品牌 Profile、通用默认值。

  • 只使用 Manifest 声明的字段;Profile 保存公开品牌事实,Preferences 保存个人工作偏好。
  • required: true 字段缺失时,按 Manifest 的问题配置向用户提出一个聚焦问题;用户明确同意后再保存回答。
  • 报错提供可复制的 context_id、字段路径与来源,诊断内容避开秘密、完整私人路径和原始配置。

通用反馈闭环

用户在 Skill 驱动任务中提出修改意见时,继续当前产物前必须执行:

  1. 先判断意见是 task-specific(仅本次)还是 reusable(可跨任务复用)。
  2. task-specific 只修改当前任务,不改 Skill。
  3. reusable 先确定作用域:领域规则先更新对应 canonical Skill;适用于所有 Skill 的规则先更新共享规范。
  4. 完成规则更新、版本、lint 与分发核验后,再把修改应用到当前任务。
  5. reusable 修改会使此前的“确认”“继续”“发吧”失效;完成当前产物修改和回读后必须停下,等待用户下一步指示,不自动进入发布、提交或其他外部写入。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Lov Bp Outline AI skill do?

Turn existing project materials into a source-backed investor BP brief, evidence ledger, and 12–15 slide outline. Use before making slides, when the product positioning is unclear, or when an existing outline is too technical, generic, or unsupported. Trigger on "写 BP 大纲", "融资叙事", "梳理商业计划书", "先不要做 PPT", "BP outline", "investor narrative", or "pitch deck outline".

Why use Lov Bp Outline on TypingMind?

Because you install it once and use it with any model. Lov Bp Outline is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Lov Bp Outline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lovstudio/skills/tree/main/skills/bp-outline. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Lov Bp Outline?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Lov Bp Outline?

As many as you like. As long as a model supports skills, you can use Lov Bp Outline with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Lov Bp Outline AI skill free?

Yes. It is published on GitHub by lovstudio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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