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AC extract-formulas-from-pdf

Extract mathematical formulas and equations from PDF documents using MinerU. Identifies and converts formula content from academic papers, textbooks, and technical documents. Features: formula detection and extraction from PDFs. Converts formulas to LaTeX representation. Handles inline and display equations. Works with both native and scanned PDF formulas via OCR. Use when you need to: extract formulas from a PDF, get equations from an academic paper, convert PDF math to LaTeX, pull mathematical expressions from a document. Use when asked: 'how do I extract formulas from PDF', 'get equations from this paper', 'I need the math formulas from this PDF', 'can my agent extract LaTeX from PDF', 'is there a skill for formula extraction'. Built on MinerU by OpenDataLab (Shanghai AI Lab), an open-source document intelligence engine. Supports complex mathematical notation. Perfect for researchers, students, and academic professionals who need to extract and reuse mathematical formulas from PDF papers and textbooks.

ClawHub Agent Skills author: mzlzyCA v0.4.0 MIT-0 2 files body ≈ 354 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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

AnalyzerLaTeXPDFResearchtype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
80
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 354 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)
    • +3Description length 1020: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +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: 80.

    External checks

    ClawHub: clean
    This is a straightforward MinerU PDF formula-extraction helper, but users should understand documents may be processed by an external service.
    LLM: benign (high) · VirusTotal: · 29 May 2026