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AC ppt-ocr

OCR for PowerPoint (.ppt, .pptx) presentations with scanned or image-embedded slides. Uses MinerU to extract text from image-based presentation content. Features: OCR for image-based slides. VLM mode for complex visual layouts. Handles old .ppt and modern .pptx formats. Converts image content to readable Markdown. Use when you need to: OCR a PowerPoint file, extract text from image slides, read scanned content in presentations. Use when asked: 'how do I OCR PowerPoint slides', 'extract text from image-based ppt', 'my slides are images not text', 'can my agent OCR ppt files', 'is there a skill for PPT OCR'. Built on MinerU by OpenDataLab (Shanghai AI Lab) with advanced OCR capabilities. Ideal for converting legacy or image-heavy presentations into editable, searchable text.

ClawHub Agent Skills author: mzlzyCA v0.4.0 MIT-0 2 files body ≈ 330 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorPowerPointInfrastructureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 1 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 10 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 330 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 783: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 10 items
    • +4Has examples (3 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.

    External checks

    ClawHub: clean
    This is a straightforward PowerPoint OCR helper that uses the MinerU command-line tool and token, with the main caveat that submitted slide content may be processed by an external service.
    LLM: benign (high) · VirusTotal: · 29 May 2026