- ---
name: docx-pdf-knowledge-parser description: parse local docx and pdf files into report-first knowledge artifacts. use when chatgpt needs to extract text from uploaded or locally available attachments, generate ingest-report.md, kb-items.jsonl, failed-items.jsonl, and memory.candidate.md without directly writing memory.md.
# Docx PDF Knowledge Parser
Use this skill to turn local or uploaded .docx and .pdf files into structured, reviewable knowledge outputs.
## What this skill does - Accept local or already-available .docx and .pdf files. - Classify files into parseable, manual-review, or failed. - Parse .docx and .pdf in v1.0. - Produce report-first outputs instead of writing MEMORY.md directly. - Preserve failures and uncertainty instead of guessing content.
## Supported v1.0 scope ### Inputs - Local .docx file path - Local .pdf file path - A batch of local .docx and .pdf files in one directory
### Parsing - .docx - .pdf
### Outputs - ingest-report.md - kb-items.jsonl - failed-items.jsonl - MEMORY.candidate.md
## Required behavior 1. Only process files that are already available locally or have already been provided to the runtime. 2. Do not claim file content was learned unless text was actually extracted. 3. Default to report-first. Do not write MEMORY.md in v1.0. 4. Record every failed file with a concrete reason. 5. Prefer plain-text summaries over complex cards when reporting progress.
## File routing rules ### Parseable Treat these as parseable in v1.0: - .docx - .pdf
### Manual-review Route here when the file is out of scope or low-confidence in v1.0: - .pptx - images - scans with no extractable text - archives - unusual file types
### Failed Route here when the file cannot be opened, parsed, or extracted successfully.
## Standard workflow 1. Resolve input type. - Single file path -> process one file - Directory path -> enumerate supported files 2. Create a batch record. - Generate batch_id - Record started_at 3. Build a manifest. - File name - File path - File type - Route decision 4. Attempt extraction. - .docx -> use parsers/parse_docx.py - .pdf -> use parsers/parse_pdf.py 5. Produce structured outputs. - success -> append to kb-items.jsonl - failure -> append to failed-items.jsonl 6. Summarize the batch. - Write ingest-report.md - Write MEMORY.candidate.md 7. Finish the batch. - Record finished_at - Never auto-write MEMORY.md
## Output contracts ### kb-items.jsonl Write one JSON object per successfully extracted knowledge item with at least: - batch_id - source_file - source_path - file_type - topic - content_type - summary - extracted_at - confidence
### failed-items.jsonl Write one JSON object per failed file with at least: - batch_id - source_file - source_path - file_type - failure_reason - error_detail - suggested_action - failed_at
### MEMORY.candidate.md Include: - batch header (batch_id, started_at, finished_at, source_directory or source_file) - grouped knowledge summaries - source references - confidence notes - items needing review
### ingest-report.md Include: 1. Batch summary 2. Input scope 3. File counts and routing counts 4. Successful extraction summary 5. Failures and risks 6. Recommended next actions
## Safety rules - Never invent text that was not extracted. - If parsing fails, say so plainly and log it. - Treat filenames as hints only, never as proof of document contents. - Keep sensitive data out of MEMORY.candidate.md unless the workflow explicitly allows it.
## Included files - run.py: minimal batch runner for local testing - parsers/parse_docx.py: docx text extraction helper - parsers/parse_pdf.py: pdf text extraction helper - references/output_examples.md: sample output shapes and field guidance - README.md: setup and usage notes