AD opendataloader-pdf
OpenDataLoader PDF — AI-ready PDF parser. Parse PDFs into Markdown/JSON/HTML for RAG pipelines, extract tables with bounding boxes, OCR scanned PDFs, and enrich charts/formulas with AI descriptions. Use when: (1) parsing PDFs for knowledge bases or RAG systems; (2) extracting structured data from medical reports, academic papers, invoices; (3) building AI knowledge bases from PDF documents; (4) converting PDF documents to Markdown/JSON for further processing; (5) any PDF-to-LLM data extraction task.
As a process D 46/100 · Unfinished process — weak spots: when it triggers, inputs and preconditions, failures and branches
How to improve
- 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: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: OpenDataLoader PDF — AI-ready PDF parser. Parse PDFs into Markdown… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 46/100
- 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
- 40Consistency. Frontmatter name (opendataloader-pdf) differs from the folder (opendataloader-pdf-zmy)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 75Steps. 3 steps
- 100Execution cost. Instruction body is 800 tokens
- 100Running it twice. No mutating operations
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)
- +1No license
- +2Single-language instructions
- +3Description length 504: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 3 items
- +3Output format is stated explicitly
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.