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Document processing for OpenClaw — convert, extract, OCR, redact, sign, and watermark PDFs and Office documents using the Nutrient DWS API. Use when asked to convert documents (DOCX/XLSX/PPTX to PDF, PDF to images or Office formats), extract text or tables from PDFs, apply OCR to scanned documents, redact sensitive information or PII, add watermarks, or digitally sign documents. Triggers on "convert to PDF", "extract text", "OCR this", "redact PII", "watermark", "sign document", or any document processing request.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 455 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Document processing for OpenClaw — convert, extract, OCR, redact, sign, and watermark PDFs and Office documents using the Nutrient DWS API.

As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

GeneratorWordExcelPowerPointAI 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
D
48/100
Unfinished process
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 48/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (nutrient-openclaw) differs from the folder (nutrient-document-processing)
    • 70When it triggers. States when to use, but not when not to
    • 75Steps. 3 steps
    • 100Tools and files. No external tools needed
    • 100Execution cost. Instruction body is 455 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

    • +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
    • +5Description quotes 6 example trigger phrases
    • +3Description length 519: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 3 items
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.