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Agent4ppt Skill

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xy040427-collab
agent4ppt-skill

Create Agent4PPT full-slide-image and editable-mode PowerPoint decks and paired comparisons. For every editable page, generate a complete slide first, erase selected text with an image model, then restore native text through PowerPoint MCP and visually match the original. Includes the portable runtime, stage records and review tools.

Overview

Publisherxy040427-collab
Repositoryagent4ppt-skill
Skill nameagent4ppt-skill
Stars
3
Forks
0
Bundled files
62
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.

  • 62 bundled files

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

  • Open source

    Published by xy040427-collab on GitHub. Read the source before you install it.

Installation

Install the Agent4ppt Skill 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/xy040427-collab/agent4ppt-skill/tree/main/skills/agent4ppt-skill
  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/xy040427-collab/agent4ppt-skill.git /tmp/agent4ppt-skill
mkdir -p .claude/skills
cp -r /tmp/agent4ppt-skill/skills/agent4ppt-skill .claude/skills/agent4ppt-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent4ppt Skill 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 Agent4ppt Skill 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 Agent4ppt Skill 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.

Agent4PPT

Deliver a presentation that the user can open, inspect and present. Preserve coherent visual design and meaningful generated artwork when adding editability.

For every new editable page, use full_slide_first. Read editable composition and visual replication before generating images or writing a production script. The sequence is complete slide generation → accepted original → image-model removal of selected text → native text restoration through PowerPoint MCP → rendered visual comparison and adjustment. Initial layout estimates are not the visual target.

For a two-mode comparison, retain those same accepted generated originals as the full-slide version. Do not flatten the editable deck and label it a full-slide-image baseline. Do not switch to reserved, reuse unrelated images or submit an unrendered background as QA evidence to work around a tool failure. Resume existing legacy projects as needed; creating a new reserved project requires an explicit user request for that workflow. If a required tool fails, diagnose/retry within the task scope or report the blocked stage. The runtime cannot prove that a host's visual judgment is truthful.

Choose the production mode

For built-in image outputs, read the artifact handoff before generation. A display data URL does not mean no local file exists. Resolve the output of the specific tool call, use import-image to validate/copy it into the task directory, then inspect and register it with the existing stage command. Importing does not complete or approve a page.

  • Full-slide imagery: the image model produces each entire page; the bundled CLI maintains jobs and exports rasterized pages with text notes. Use the existing workflow below when this format fits the request.
  • Editable composition: read editable composition. New projects default to editable_workflow: full_slide_first: generate a finished slide WITH text and pictures, measure selected native overlays from that design, register it with record-design, use its returned erase request to remove ONLY selected content, register the edited background with record-background, then compose and review native overlays. Keep the complete artwork and fixed text. Explicit editable_workflow: reserved retains the earlier blank-region route. Native charts, tables and connected diagrams still require the documented host extension; the bundled exporter supports text and pictures.

Both modes share planning, style guides, source handling, page leases, revisions, notes and delivery checks. The prompt and exported objects depend on the project mode. Editable does not mean every visible mark must become an object: explicitly allowed fixed text and visual symbols can stay in the image. Do not split a complete scene into many assets merely to make selected text editable. Never flatten a composed page and call the flattened result editable.

New full-slide-first projects enforce review_policy: structured-v1. Initial overlay coordinates are provisional; do not constrain the complete design with blank-region guides. Measure positions and typography from the accepted design and preflight native text before record-design --overlays-file. Compare the design, erased background and composed render. Check erasure residue, artwork preservation and design alignment in addition to ordinary composition QA. Diagnostic reservation guides remain available for the explicit reserved route. With authorized delegation, assign the complete page loop to page workers; the runtime queue does not launch host agents itself.

Start from the intended result

New text-only full-slide-first projects also enable visual_comparison: raster-v1. After each PowerPoint checkpoint render, run compare-render as described in visual replication. Inspect the overview on every pass and every object panel initially and finally; intermediate passes focus on modified objects and remaining findings. Use the named native object and actual run formatting in its report to correct position, visible size, color and emphasis through MCP. Recompose, rerender and compare again after corrections. Completion recomputes these measurements and rejects stale reports or unaddressed findings. A diagnostic with no flags is not visual approval; it does not recognize words or identify the target font. Do not manufacture exceptions to approve visible mismatches.

New full-slide-first projects containing only native text overlays enforce native_acceptance: target-v1: completion requires an adopted compose --native-draft checkpoint, a PowerPoint preview exported at the target image dimensions, and observations for every text object. Initial drafts cannot pass this gate. Existing databases and mixed image/text extensions keep their previous compatibility rules. These checks establish artifact completeness, not proof of MCP use or visual quality.

Before batch production, finish one representative content page with multiple separately positioned text blocks, including emphasis and body copy. A cover alone does not validate restoration. Inspect target and render yourself at equal dimensions; fix visible differences before accepting the sample or expanding production. Never prefill pass reports for pages you have not inspected. Use font_size in points, not size.

After the sample is accepted, use page subagents where the host supports them. New briefs default to parallelism: 10, with a maximum of 10. Keep the worker pool as full as useful: target min(10, project parallelism, ready pages, actual available child-agent capacity), accounting for workers already running rather than spawning a duplicate pool. Refill each freed slot immediately instead of waiting for an entire batch. The limit is a ceiling, not a reason to create ten workers for fewer ready pages. Use dispatch-plan PROJECT --host-slots N to inspect the runtime's read-only dispatch recommendation; pass actual currently available child slots. This command neither creates agents nor increases the host's own limit. If the host offers fewer slots, use those and report the actual concurrency honestly. See production.

Assign each editable worker the complete page loop and its own files and session. The coordinator owns the outline, accepted sample, shared notes, final deck and acceptance. Do not consume page-worker slots with redundant QA-only agents. When PowerPoint uses a shared endpoint/process, serialize all its mutations, saves and renders through one coordinator-owned queue; separate session IDs alone do not guarantee safe concurrency. Image generation, erasure and independent visual inspection can continue in parallel. Never close shared PowerPoint sessions or quit the shared process while another page is active.

For each correction pass, inspect the page first, prepare all required object changes, then execute ordinary MCP calls together in one orchestration, checking each result; save, adopt and render once after the pass. This is host-side batching, not a new native MCP batch endpoint. Preserve every-object initial and final comparison review and all runtime gates. The coordinator inspects every final page overview and the detailed regions needed to resolve findings, small-text concerns and exceptions, reusing the worker's genuine per-object evidence instead of repeating every successful intermediate pass. Stop polishing imperceptible glyph/antialias differences after visual acceptance; unresolved visible defects or required comparison findings still block completion.

For full-slide-first editable production, read visual replication before composition. Use the accepted design as the visual target, adjust existing native text through PowerPoint MCP, verify East Asian fonts, and register adjusted checkpoints with compose --native-draft. Render and compare before completion. This is the normal production loop, not optional post-delivery repair; final export retains adopted text formatting.

Read the supplied material and determine audience, purpose, language and approximate length. Verify changing factual claims against authoritative sources. Extract required figures and screenshots before planning layouts. Keep original assets and their sources with the project.

Offer a concise outline and a representative sample when the user wants collaboration. If they ask for the finished result directly, choose a reasonable style and proceed; do not repeatedly request permission for already authorized work. Record important choices in the brief. Do not require the user to configure an API when a built-in image tool is available.

Use a coherent palette and typography across pages while varying composition by content. Prefer large evidence figures and meaningful photographs over decorative filler. Put detailed explanation in notes.md.

Read the guidance needed for the current phase

The references are operational resources, not optional filler. Read the applicable ones before making the decisions they govern; do not load all twelve style guides for every deck.

TaskRead
Decide phases, approvals, progress and recoveryWorkflow
Write outline, map sources and choose a representative samplePlanning
Choose built-in tool or APIBackend choice
Set up Python, diagnose API access or configure credentialsConfiguration
Use figures, screenshots, photos or identity assetsAssets
Prepare full-context requests, dispatch workers or recover leasesProduction
Invoke API generation/editing, batching or transparency processingBackends
Write talk tracks, inspect results and exportNotes and delivery
Extract a reusable visual systemStyle library

Use brief schema for data fields and commands for executable syntax. When authorized delegation is available, use the page worker handoff. The coordinator owns final result acceptance.

Full visual systems

Run styles to discover built-in and personal recipes. A known style_name in the brief freezes the entire recipe at initialization; subsequent claims include it. Read the chosen guide, not just its name. Each guide links its machine-readable recipe containing canvas, palette, typography, page variants, composition options, image treatment, constraints and review criteria.

Visual systemDetailed guide
清爽专业Professional
创意杂志Editorial
墨水阅读Ink
数据分析Dashboard
学术汇报Research
复古插画Retro
白板讨论Whiteboard
技术手绘Technical sketch
温暖手作Handmade
咨询简报Consulting
公共事务Public affairs
教学演示Teaching

Run a full-slide image project

The following is the unchanged mode: full_slide route (also the default for older briefs). For mode: editable, follow the shared queue with the additional compose → render/review → complete steps in editable composition.

The portable entry point is python <skill>/scripts/agent4ppt.py. Run it with Python 3.10+ and Pillow. doctor reports the current runtime; setup creates a separate runtime if needed. See commands.md for commands and brief.md for the brief schema.

  1. Write a brief containing the actual page titles, exact visible copy, layout direction, notes, context, and image references. init PROJECT --brief brief.json validates it and creates a SQLite-backed project.
  2. claim PROJECT --worker NAME --page N produces a self-contained request and a lease token. Read that request. The token is required when returning a result; do not manufacture one or edit the database.
  3. With backend: builtin, inspect every local reference image using the available image viewing tool, then call the built-in image generation/editing tool with the request's prompt and explicit reference paths. Generate one complete page per call. Python cannot invoke or detect a host's built-in image tool. If it is unavailable, report this and discuss the API route rather than silently switching providers.
  4. Inspect the generated page at readable size: title and numbers, clipping, overlaps, layout, asset identity, and figure labels. Repair with the same image backend. Do not substitute locally drawn final pages or paste text over model-generated pages to conceal a failed generation. key is an optional asset preprocessing tool, not a final-page renderer.
  5. complete PROJECT --page N --token TOKEN --image PATH --backend builtin --qa "..." --method-file method.json records the chosen original output and actual tool/mode. Use --sample for the first style reference; subsequent claims include the image and method contract. Sample acceptance means the user accepted it or explicitly delegated the visual decision. Record only exposed model/size/quality settings, never guessed host internals. Older projects may omit method evidence, but new production should retain it.
  6. Continue the other pages. Independent workers may claim separate pages when the host supports delegation and it is authorized. Otherwise work sequentially. Every worker must receive the claimed request, inspect its references, and use its fixed backend. renew extends a lease; fail records a blocker; revise invalidates stale work and starts a new revision.
  7. Complete notes.md, inspect every page, and run export PROJECT. Export rejects unfinished pages or changed result files. Reopen the PPTX with an independent reader; check page count, notes, and visual output. Report any reader limitations honestly.

For CLI/API image execution, read backends.md. API credentials, rate limits and model access are independent of the built-in tool.

Deliver and retain useful evidence

Provide the PPTX and an easily viewable preview when possible. Briefly state page count, which content is editable, material limitations, and what was actually tested. Keep the working materials appropriate to the chosen mode outside the reusable skill. Do not confuse a mocked HTTP test with a real provider call or a reused image with a newly generated page.

When a reusable custom style emerged, offer to save its visual recipe using styles --save; include no private slide content in the saved style. User styles live outside the installed skill and survive updates.

Bundled files

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

and 2 more files.

Frequently asked questions

What does the Agent4ppt Skill AI skill do?

Create Agent4PPT full-slide-image and editable-mode PowerPoint decks and paired comparisons. For every editable page, generate a complete slide first, erase selected text with an image model, then restore native text through PowerPoint MCP and visually match the original. Includes the portable runtime, stage records and review tools.

Why use Agent4ppt Skill on TypingMind?

Because you install it once and use it with any model. Agent4ppt Skill 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 Agent4ppt Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xy040427-collab/agent4ppt-skill/tree/main/skills/agent4ppt-skill. 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 Agent4ppt Skill?

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 Agent4ppt Skill?

As many as you like. As long as a model supports skills, you can use Agent4ppt Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent4ppt Skill AI skill free?

It is published on GitHub by xy040427-collab. 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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