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Ppt Image First

CommunityPopular
NyxTides
ppt-image-first

Build presentation plans for PPT / slides / decks through a conversation-first workflow, then propose multiple visual directions with preview images before writing deck specs. Use when the user asks to create a PPT, presentation, deck, 答辩稿, 路演 deck, 产品介绍 PPT, 汇报 PPT, or when the user has only a topic or rough materials and needs help clarifying structure, style, and page planning before generation.

Overview

PublisherNyxTides
Repositoryppt-image-first
Skill nameppt-image-first
Stars
1.2K
Forks
91
Bundled files
21
LicenseApache-2.0
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.

  • 21 bundled files

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

  • Open source

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

Installation

Install the Ppt Image First 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/NyxTides/ppt-image-first.git \
  .claude/skills/ppt-image-first
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ppt Image First 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 Ppt Image First 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 Ppt Image First 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.

PPT Image First

Use this skill to turn a vague PPT request into a generation-ready, image-first deck plan.

I/O Contract

  • Input: a PPT topic, rough goal, existing notes/materials, or complete report-like narrative.
  • Optional anchors: audience, page count or duration, school/company/lab/course/brand identity, use occasion, source files, and style constraints.
  • Output: a confirmed content basis, visual style previews, deck specs, generated page visuals, review surface, and final PPT after approval.
  • Confirmation gates: 需求确认, 风格确认, 生成前确认, and final review approval.
  • Default ratio: generate preview images and final page visuals in 16:9 unless the user explicitly requests another ratio.

Workflow Phases

Follow these phases in order and do not skip the confirmation gates:

  1. Intake: clarify lightly, identify anchors, output a short baseline judgment, then get 需求确认.
  2. Content basis: create or extract content_report.md unless the user already supplied complete report-like content; show a synthesized judgment, not the raw file by default.
  3. Style preview: align style boundaries, propose directions, generate 首页 / 目录页 / 正文页 previews for each direction, support refinement, then get 风格确认.
  4. Planning lock: run 风格反演确认, then write design_spec.md, slide_blueprint.md, and spec_lock.md in that order; summarize and get 生成前确认.
  5. Generation review: generate page visuals, optionally run candidate selection, open the review HTML, retouch as needed, and export the final PPT only after approval.

Read references/workflow.md for stage-by-stage execution details.

Progressive Loading

  • Intake and baseline: read references/conversation_framework.md only when you need the conversation-first intake pattern.
  • Style proposals: read references/style-system.md when producing proposal cards or interpreting V1-V8 internally.
  • Preview/review UI: read references/preview-flow.md before producing style previews, candidate-picker pages, or review pages.
  • Content basis: read templates/content_report_reference.md before writing content_report.md.
  • Planning files: read templates/design_spec_reference.md, templates/slide_blueprint_reference.md, and templates/spec_lock_reference.md immediately before writing those files.

Working Principles

  • Treat the user like a client and yourself like the proposing design agent.
  • Do not force the user to configure design parameters manually; translate their natural-language intent into design decisions.
  • Ask for real-world identity anchors when relevant, but treat them as grounding context rather than style questions.
  • Keep intake lightweight; do not turn content basis or style alignment into a long questionnaire.
  • Use style vectors internally, but present front-stage options as concise proposal cards plus image previews.
  • Use the content basis for previews and planning rather than generic placeholder structures.
  • Mark inferred claims carefully; do not present unsupported precise data, experiment results, citations, or institutional conclusions as user-provided facts.
  • Show summaries, proposal cards, previews, and review surfaces by default; show raw planning files only when useful or requested.

Hard Rules

  • Preview-first: final style confirmation should be based on generated 首页 / 目录页 / 正文页 previews, not text-only choices.
  • Shells are mandatory: use assets/preview_shell/index.html, assets/candidate_picker_shell/index.html, and assets/review_shell/index.html as the base UIs for their stages.
  • Open the local preview, candidate-picker, review, and final PPT files immediately when those stages produce them; if opening fails, state the blocker and provide the path.
  • Do not write slide_blueprint.md before style direction and 风格反演确认 are complete.
  • Keep generation image-first when the confirmed direction depends on generated page visuals; do not silently fall back to shape-by-shape PPT construction or hand-drawn code substitutes.
  • User-requested edits such as adding, replacing, correcting, or supplementing visible text are still image-generation/editing tasks by default. Do not use PIL, Pillow, canvas, SVG, HTML screenshots, PPT native text boxes, or deterministic overlays to patch final page visuals unless the user explicitly asks for a code/PPT-overlay workaround.
  • Treat generated page visuals as complete by default; post-generation overlays default to zero unless they are traceable to approved blueprint fields.
  • Keep slide identifiers, candidate codes, filenames, and generation batch labels outside the image-generation prompt body. They may appear in planning files, filenames, mapping tables, review UI, and chat instructions, but the prompt sent to the image model should contain only audience-facing content and visual direction.
  • Review payloads should carry lightweight coordinate markup, not base64 preview images. When a user pastes review JSON with markup, first render the marked review images locally with scripts/render_review_markup.py, then use the marked images plus separate text comments as the retouch/regeneration reference.
  • Before full generation, ask whether the user wants one final image per slide or multiple final candidates per slide.
  • Do not export the final PPT immediately after first-pass generation; export only after the reviewed pages are approved.

Output Hierarchy

Use these layers consistently:

  • content_report.md = upstream content basis when the user did not provide complete report-like material.
  • design_spec.md = global deck rationale and confirmed visual system.
  • slide_blueprint.md = page-by-page intent and content plan.
  • spec_lock.md = execution constraints and final generation guardrails.

content_report.md supports the 3 core planning files; it does not replace them. spec_lock.md is always the last core planning file.

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 Ppt Image First AI skill do?

Build presentation plans for PPT / slides / decks through a conversation-first workflow, then propose multiple visual directions with preview images before writing deck specs. Use when the user asks to create a PPT, presentation, deck, 答辩稿, 路演 deck, 产品介绍 PPT, 汇报 PPT, or when the user has only a topic or rough materials and needs help clarifying structure, style, and page planning before generation.

Why use Ppt Image First on TypingMind?

Because you install it once and use it with any model. Ppt Image First 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 Ppt Image First in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NyxTides/ppt-image-first/tree/master. 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 Ppt Image First?

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 Ppt Image First?

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

Is the Ppt Image First AI skill free?

Yes. It is published on GitHub by NyxTides under the Apache-2.0 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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