Lov Bp Polish logo

Lov Bp Polish

Organization
lovstudio
lov-bp-polish

Review and improve an existing BP outline, PPTX, PDF, or rendered slide set across investment logic, evidence, copy, charts, and visual quality. Produces a scored report, page-level revisions, and targeted regeneration instructions while keeping facts separate from assumptions. Trigger on "润色 BP", "审稿商业计划书", "PPT 不专业", "逐页检查", "改图表", "BP review", "polish pitch deck", or "audit investor deck".

Overview

Publisherlovstudio
Repositoryskills
Skill namelov-bp-polish
Stars
67
Forks
17
Bundled files
6
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.

  • 6 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 Polish 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-polish .claude/skills/lov-bp-polish
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lov Bp Polish 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 Polish 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 Polish 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 精修 · BP Polish

Improve an existing BP without silently changing its business facts. This skill can be used before deck production, after rendering, or in a correction loop.

Modes

ModeInputFocus
contentOutline / MarkdownProduct definition, story, copy, page rhythm
evidenceOutline + ledger/sourcesClaims, metrics, TAM/SAM/SOM, assumptions
visualPPTX/PDF/slide imagesHierarchy, charts, layout, branding, QR codes
fullAny complete BP workspaceAll dimensions + correction loop

Infer the smallest useful mode from the request. Do not require a PDF when the user only wants the outline reviewed.

Output Contract

text
business-plan/
├── reports/bp-review.md
├── outline.md                 # patched only when authorized by the request
├── deck-manifest.md           # updated for visual fixes
└── revised slide/deck assets  # only affected artifacts

Workflow (MANDATORY)

Step 0: Resolve source and mode

Resolve this SKILL.md directory as SKILL_DIR. Inspect the supplied source and any existing BP workspace. Reuse audience, stage, style, and evidence definitions.

If multiple review modes are plausible, prefill from the user's wording. Ask one compact question only when the choice materially changes the work. “不专业” defaults to full; “文字太技术” defaults to content; “图表不好看” defaults to visual.

Read references/review-rubric.md, references/charts-and-visuals.md, and references/user-config.md as relevant.

Step 1: Run deterministic outline audit

When an outline exists:

bash
python3 "$SKILL_DIR/scripts/audit_bp.py" \
  --input ./business-plan/outline.md \
  --output ./business-plan/reports/bp-review.md

Use --strict before final delivery. The script checks structure and evidence hygiene; it does not replace investor judgment or visual inspection.

Step 2: Review as four adversaries

  1. Non-technical investor — can the product be repeated after ten seconds?
  2. Category expert — which product/competition claims are naive or imprecise?
  3. Skeptical partner — which core claims lack proof or overstate traction?
  4. Design director — where does layout reduce trust or distort meaning?

For every issue record slide, severity, why it matters, and exact revision.

Step 3: Protect facts during polishing

  • Never make numbers “look better”.
  • Never turn an assumption into a fact.
  • Never invent a customer quote or testimonial.
  • Never widen TAM without a buyer and price bridge.
  • Never change a product boundary solely to improve the story.

If a revision needs new evidence, mark an evidence blocker and return it to lov-bp-outline.

Step 4: Inspect rendered slides

For visual/full mode, create or inspect a contact sheet and every page at normal presentation size. Check:

  • one conclusion per page;
  • body text at least 20 pt;
  • optical alignment and whitespace;
  • chart axes, units, legends, dates, and sources;
  • genuine, legible product screenshots;
  • unstretched images and logos;
  • no clipped text or broken CJK;
  • QR codes decoded from final rendered pages;
  • clean cover and one-action final page;
  • matching PPTX/PDF page counts and normalized filenames.

Step 5: Apply targeted fixes

Classify each fix:

  • copy fix — patch outline and regenerate affected slides;
  • evidence blocker — update ledger/source before rewriting;
  • visual defect — change layout/style or regenerate affected slides only.

Do not regenerate the whole deck for a one-page issue. Repeat audit → fix → audit for at most three cycles.

Step 6: Deliver the report

Lead with verdict and score, then blockers, page-level findings, evidence gaps, visual QA, and delivery status. Use assets/templates/bp-review.md.

Target: 85+ with no blocker. A high numeric score never overrides fabricated data, missing financing ask, broken source, unreadable slide, or invalid QR code.

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 Polish AI skill do?

Review and improve an existing BP outline, PPTX, PDF, or rendered slide set across investment logic, evidence, copy, charts, and visual quality. Produces a scored report, page-level revisions, and targeted regeneration instructions while keeping facts separate from assumptions. Trigger on "润色 BP", "审稿商业计划书", "PPT 不专业", "逐页检查", "改图表", "BP review", "polish pitch deck", or "audit investor deck".

Why use Lov Bp Polish on TypingMind?

Because you install it once and use it with any model. Lov Bp Polish 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 Polish in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lovstudio/skills/tree/main/skills/bp-polish. 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 Polish?

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 Polish?

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

Is the Lov Bp Polish 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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