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Proposal Ppt Production

Community
chenxupeng1990-eng
proposal-ppt-production

提案页面生产。内容、页面结构和视觉方向都已经确认后,才把定稿变成 16:9 页面,出评审 PNG 并组装 PPTX。通常由 qifei-proposal-v2 在页面生产阶段调用;单独使用的前提是用户已经给出确认的页面大纲和视觉方向。不做策略和内容决策,也不批准页面;从零开始做提案用 qifei-proposal-v2。

Overview

Publisherchenxupeng1990-eng
Repositoryqifei-proposal-skill
Skill nameproposal-ppt-production
Stars
74
Forks
4
Bundled files
9
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 chenxupeng1990-eng on GitHub. Read the source before you install it.

Installation

Install the Proposal Ppt Production 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.
    https://github.com/chenxupeng1990-eng/qifei-proposal-skill/tree/codex/qifei-proposal-base/skills/proposal-ppt-production
  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/chenxupeng1990-eng/qifei-proposal-skill.git /tmp/qifei-proposal-skill
mkdir -p .claude/skills
cp -r /tmp/qifei-proposal-skill/skills/proposal-ppt-production .claude/skills/proposal-ppt-production
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proposal Ppt Production 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 Proposal Ppt Production 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 Proposal Ppt Production 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.

Proposal PPT Production

Principle

Visualize the source information; do not turn all source information into visual spectacle. Preserve the argument and necessary detail while giving each page one clear focus.

REQUIRED SUB-SKILL: Use Presentations for PPTX work. Use imagegen for generated scenes, objects, textures, or visual carriers.

Always read references/production-sop.md, references/design-system-contract.md, references/page-contract-and-qa.md, and references/deterministic-production-utilities.md. The active project's confirmed DESIGN.md is the styling authority and overrides all generic examples. If the user provides a PPTX that must function as the actual template or editable source deck, also read references/template-exact-mode.md and use the Presentations template-following workflow.

When the proposal contains industry, competitor, people, product, channel, or operating data, also read references/data-analysis-and-comparison-methods.md before writing page contracts.

Role in a larger proposal workflow

This skill starts only after the content, page contracts, and design direction have been confirmed. It may produce review HTML and/or PNGs, but it cannot mark a page approved, alter evidence, or authorize final assembly. During review, use the lightest valid artifact: direct PNG plus Cowart annotation for image-native pages, or HTML plus PNG for pages with editable exact text, data or structured labels. Assemble PPTX/PDF only after the controlling workflow records explicit final approval for every required page. When the requested handoff requires editable copy, use the measured-HTML bridge after approval: keep approved visuals as the background layer and compile measured HTML text boxes into native PowerPoint text. This bridge is an export mode, never a replacement for HTML/PNG review.

Judgment and automation boundary

Proposal judgment stays with the agent and proposal owner: business question, conclusion, evidence chain, narrative role, communication object, carrier, visual hammer, product role, and audience decision. Deterministic utilities may validate fields, stage media, measure HTML, render, compare, package, and report errors. They may not autofill a missing decision, choose a convenient template, rewrite strategy, shrink copy to force a fit, or infer approval from a successful export.

Visual-direction preflight gate

Before producing content pages, verify that the project has an explicit approval record for a visual core sample. A palette, font list, client reference deck, or agent-authored DESIGN.md does not count as approval. If the sample approval is missing:

  1. stop content-page production;
  2. produce 2–3 genuinely different visual core samples using the same confirmed copy;
  3. record the selected primary direction and any named chapter variant;
  4. build and approve the required calibration page types;
  5. only then compile and freeze DESIGN.md.

Any DESIGN.md written before visual-sample approval must be marked draft or rejected and cannot be treated as styling authority.

Before calibration, choose exactly one deck-level visual authority:

  1. an actual user PPTX template to inherit exactly;
  2. an explicitly approved custom brand direction;
  3. a newly calibrated direction when neither exists.

Do not mix unrelated templates, default layout libraries, reference-deck styling and custom visual systems in one deck.

Workflow

  1. Lock source truth and choose the analysis loop. Inventory confirmed copy, evidence, numbers, sources, logos, product images, fonts, and prohibited changes. For analytical proposals, declare whether each major argument begins as a strategy hypothesis seeking evidence or as a data pattern seeking a strategy. In the first mode, search supporting and conflicting evidence and revise the hypothesis when needed. In the second mode, test competing explanations before converting a pattern into a strategy. Never invent proof, cherry-pick evidence, or silently alter conclusions.
  2. Define the communication job and map the narrative. Write: By the end, [audience] should [outcome] because [central takeaway]. Give every page one role and one conclusion. Build cumulative logic rather than independent posters. For analytical proposals, require every major finding to produce a strategy choice, downstream chapter task and verification metric. Reject an insight that does not change the later proposal, and reject a strategy that cannot be traced back to evidence.
  3. Pass the logic-to-visual gate. Before styling or prompting Image2, write the page's causal statement as because -> therefore -> audience decision. Reject any proposed carrier that merely illustrates the topic or mood without making that reasoning easier to see.
  4. Choose the carrier before styling. Use chart/table for data; image/storyboard for scenes and actions; path/loop/diagram for relationships; realistic UI or work artifact for execution; KV only for claims and transitions.
  5. Choose production mode. HTML owns exact copy, labels, data, sources, and typography. Image2 owns composition, material, people, scenes, and relationship carriers. Use hybrid pages when both are essential. If the page is image-native and contains no required editable text, exact data, source or dense labels, generate a complete 16:9 PNG directly, review and annotate it in Cowart, and skip HTML. When the visual hammer includes a real product, supply the official product image to Image2 as a strong reference and generate product, scene, lighting, reflections, contact shadows, scale and perspective as one composition. Do not generate an empty scene and paste the product into it later with HTML/CSS.
  6. Calibrate, then scale. Produce 2–3 representative pages, review at full and thumbnail size, lock tokens, then continue in small batches. Validate independent 1920×1080 PNGs before final assembly.
  7. Run semantic, visual, and technical QA in that order. A technically clean or beautiful page still fails when its carrier does not express the page conclusion. Fix only the affected layer and preserve approved pages.

Before visual production, store page contracts as JSON and run:

bash
node scripts/validate_page_contracts.mjs <page-contracts.json>

Do not write Image2 prompts or batch HTML for pages that fail this gate.

Hard rules

  • Never default the deck to KV, equal cards, 50/50 splits, or full-page dark blue.
  • A visually polished page fails when its primary carrier can be swapped for another on-topic image without changing the conclusion. This is the topic-illustration failure and requires carrier redesign, not cosmetic adjustment.
  • Every generated page must pass a removal test: hide the primary carrier and ask what understanding is lost. If no essential relationship, action, contrast, mechanism, proof, or decision is lost, the carrier is decorative and the page is rejected.
  • Information-led pages need visible structure or a restrained auxiliary visual. The auxiliary cannot outrank the information.
  • Except for pages dominated by large volumes of exact data, a content page may not be completed through text layout alone. It must contain a primary carrier that visibly performs part of the explanation: a diagram, progression, mechanism, scene, simulation, evidence artifact, workbench, product architecture, or semantic no-text image system. If removing the carrier leaves the page meaning unchanged, it is decoration and the page fails review.
  • For light information pages, prefer a semantic transparent PNG behind HTML; keep precise text in HTML.
  • Sparse, short and exact labels may be integrated into Image2 when they are inseparable from one visual hammer and can be checked word-for-word at full size. If any character, hierarchy or pointer relationship is wrong, regenerate the visual or move the labels back to HTML.
  • Large copy, exact data, sources, dense causal logic and unverifiable labels stay outside Image2. A successful sparse-label exception does not authorize generated text elsewhere in the deck.
  • Do not create HTML merely to satisfy a pipeline convention. A page may remain a direct PNG when all necessary meaning is already carried by the generated composition and all embedded sparse text has been verified. The moment exact editable copy, charts, tables, citations or dense logic are required, return to HTML or a hybrid route.
  • Scene-led pages must show what happens. Do not reduce live rooms, phone demos, or behavior to tables merely because they contain many details.
  • Integrate product through shared light, perspective, contact, and narrative function during primary-visual generation. If it still looks false, regenerate with stronger official-product references; do not repair the scene with a pasted product cutout.
  • Use real packaging artwork. Reject generated logo, label, claim, or pack hallucinations.
  • Semantic no-text icons are valid visual anchors when they improve recognition without replacing precise information.
  • Keep same-role font sizes in one band across pages. Do not solve overflow by making a single page much smaller.
  • Unless an approved template specifies otherwise, use at least 50pt for deck titles, 35pt for slide titles, 24pt for mid-level headings and 16pt for body copy. Shorten, restructure or split before shrinking. A title intended as one line may not wrap silently.
  • Visible copy must address the proposal audience. Production instructions, visual-design notes, page tasks, prompt language, timing scaffolds and agent reasoning belong in page contracts, notes or QA records—not on the slide.
  • Treat unintended overlap, clipping and overflow as hard failures. Inspect every page at full size; use the montage only for cross-page pacing and consistency.
  • Reject unexplained continuous whitespace above about 8% of the canvas and thumbnail-level weight imbalance.
  • Regenerate only the visual region when Image2 introduces text errors; exact copy and data stay outside generated images.
  • HTML may hold exact proposal copy and independent brand marks, but it must not be used to simulate product-scene integration.
  • Mechanical validation must fail closed on missing fields, duplicate IDs, unresolved placeholders, internal production language, stale versions, missing assets, or local-path leakage. It reports the page and violated field; it does not repair meaning by autofill.

Review loop

For every revision record: observed problem, violated rule, correction layer, affected pages, and regression checks. Stop for direction if the change would alter strategy, evidence, page count, or previously approved content.

For every calibration page, record five QA decisions before approval: the page conclusion, the communication object, the selected carrier, the visual hammer, and the full-size plus thumbnail checks. A visually polished page fails if its carrier does not make the conclusion easier to understand.

Completion gate

Use current renders—not assumptions—to prove 1920×1080 output, no clipping, readable text, balanced whitespace, consistent type and brand language, faithful product artwork, and deliberate montage rhythm. When the controlling workflow authorizes assembly, additionally prove PPTX parity with all approved PNGs. For editable-text handoff, also prove frozen-field fidelity, slide-note traceability, measured-box coordinate fidelity, and a full rendered comparison; a text-field PASS alone does not prove visual parity.

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 Proposal Ppt Production AI skill do?

提案页面生产。内容、页面结构和视觉方向都已经确认后,才把定稿变成 16:9 页面,出评审 PNG 并组装 PPTX。通常由 qifei-proposal-v2 在页面生产阶段调用;单独使用的前提是用户已经给出确认的页面大纲和视觉方向。不做策略和内容决策,也不批准页面;从零开始做提案用 qifei-proposal-v2。

Why use Proposal Ppt Production on TypingMind?

Because you install it once and use it with any model. Proposal Ppt Production 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 Proposal Ppt Production in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chenxupeng1990-eng/qifei-proposal-skill/tree/codex/qifei-proposal-base/skills/proposal-ppt-production. 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 Proposal Ppt Production?

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 Proposal Ppt Production?

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

Is the Proposal Ppt Production AI skill free?

It is published on GitHub by chenxupeng1990-eng. Check the repository for licensing terms. 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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