ppt-analysis

ppt-analysis is a skill for Claude Code, Codex from OpenSenseNova/SenseNova-Skills. It costs 51 tokens per session (2,691 once invoked), scanned A, original, MIT.

A parser for PowerPoint presentations in .pptx or older .ppt format that extracts their contents and flags slides needing image-based inspection.

In plain words
What is it for?
Use it to extract text, tables, chart titles, data labels, and captions from slides, and to render or identify slides that contain mainly images.
Why use it?
It helps you inspect an entire presentation, including content that ordinary text extraction may miss, such as image-only slides and embedded visuals.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to extract text, tables, chart titles, data labels, and captions from slides, and to render or identify slides that contain mainly images.

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Install with agentmods
npx agentmods add skills/opensensenova/sensenova-skills/ppt-analysis
About the project

SenseNova-Skills is a collection of modular skills that extend SenseNova models with office-assistant capabilities such as image generation, presentation creation, spreadsheet analysis, and research. The skills are designed for use in agent runtimes and can be combined into productivity workflows; the catalogue entries are individual skills and agents from this collection.

OpenSenseNova/SenseNova-Skills · 5,515 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add OpenSenseNova/SenseNova-Skills --skill ppt-analysis
Clone the repo
git clone --depth 1 https://github.com/OpenSenseNova/SenseNova-Skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for ppt-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/ppt-analysis/github.svg)](https://agentmods.dev/skills/opensensenova/sensenova-skills/ppt-analysis)
Your own site
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/ppt-analysis"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/ppt-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for ppt-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/ppt-analysis"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/ppt-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,691 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.02691
Opus 5 $0.00026 $0.01345
Sonnet 5 $0.00010 $0.00538
Haiku 4.5 $0.00005 $0.00269

Measured 11d ago against content hash d93099069252, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

ppt-analysis scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run(
skills/sn-da-non-spreadsheet-analysis/capability/ppt-analysis/SKILL.md · 330 lines

How it starts

The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PPT Analysis — .pptx / .ppt

Environment

from pptx import Presentation
from pptx.util import Inches
import os, subprocess, json

# python-pptx is available
# For .ppt (old binary format): convert via libreoffice
def load_pptx(path):
    if path.lower().endswith('.ppt'):
        import subprocess
        out_dir = os.path.dirname(path)
        subprocess.run(
            ['libreoffice', '--headless', '--convert-to', 'pptx', '--outdir', out_dir, path],
            check=True, capture_output=True
        )
        path = path.rsplit('.', 1)[0] + '.pptx'
    return Presentation(path), path

Core Method 1: Full Text Extraction (ALL slides)

def extract_all_slides_text(pptx_path):
    """
    Extract text from every slide: text frames, tables, chart titles.
    For slides with no extractable text, flag them for image captioning.
    """
    prs, _ = load_pptx(pptx_path)
    slides_data = []

    for slide_num, slide in enumerate(prs.slides, start=1):
        slide_texts = []
        has_text = False

        for shape in slide.shapes:
            # Text frame (most common)
            if shape.has_text_frame:
                for para in shape.text_frame.paragraphs:
                    text = para.text.strip()
                    if text:
                        slide_texts.append(text)
                        has_text = True

            # Table
            if shape.has_table:
                tbl = shape.table
                for row in tbl.rows:
                    row_text = '\t'.join(cell.text.strip() for cell in row.cells)
                    if row_text.strip():
                        slide_texts.append(row_text)
                        has_text = True

            # Chart title
            if shape.shape_type == 3:  # MSO_SHAPE_TYPE.CHART
                try:
                    if shape.chart.has_title:
                        title = shape.chart.chart_title.text_frame.text
                        slide_texts.append(f"[Chart: {title}]")
                        has_text = True
                except Exception:
                    pass

        slides_data.append({
            'slide': slide_num,
            'text': '\n'.join(slide_texts),
            'has_text': has_text,
            'needs_caption': not has_text  # flag image-only slides
        })

    print(f"Total slides: {len(slides_data)}")
    image_only = sum(1 for s in slides_data if s['needs_caption'])
    print(f"Slides with text: {len(slides_data) - image_only}, image-only: {image_only}")
    return slides_data

Read the full file on GitHub · 330 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 330 lines · 51 tokens per session scan A d93099069252

Subscribe to this mod's changes

ppt-analysis is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,515 stars, last pushed today), licensed MIT. It adds 51 tokens to every session and 2,691 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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