paper-ingest-normalizer

# Paper Ingest Normalizer Convert raw literature inputs into standardized records safe for project memory, paper databases, and downstream synthesis pipelines. ## Input One of the following is required: - `pdf_path` — local path to PDF file - `url` — link to paper/article - `raw_text` — extracted or pasted text - `metadata_blob` — existing metadata dict Plus: - `project_id` — required for any writeback - `source_type` — one of: `pdf`, `doi`, `url`, `text`, `metadata` - `optional tags` — list of strings for categorization ## Output Schema Return a structured object: ``` title: string authors: string[] | null year: number | null source: string # journal, conference, preprint, etc. doi_or_url: string | null project_id: string paper_type: string # experimental, theoretical, review, etc. material_system: string | null # e.g. "钙钛矿太阳能电池", " graphene FET" device_type: string | null # e.g. "FTO/glass", "flexible substrate" key_variables: string[] | null # independent variables studied key_metrics: string[] | null # measured outcomes (PCE, mobility, etc.) core_findings: string # 2-3 sentence neutral summary claimed_mechanism: string | null limitations: string | null normalized_summary: string # 1-2 paragraph structured summary uncertain_fields: string[] | null # fields that could not be verified writeback_ready: boolean # true only if key identity fields present writeback_payload: object # the record to write into project memory ``` ## Rules 1. **Never write into project memory without project_id.** Ask if not provided. 2. **Separate direct observations from claimed interpretations.** Mark inference vs. direct extraction. 3. **Preserve uncertainty.** Use `null` for missing fields; list in `uncertain_fields`. 4. **Do not invent missing bibliographic fields.** Don't hallucinate authors, year, etc. 5. **Do not over-claim.** Keep `core_findings` and `normalized_summary` grounded in what the text actually says. 6. **Never conflate abstract with findings.** The abstract states intentions; findings are what the data supports. 7. If `writeback_ready = false`, list explicitly which fields are missing and why. ## PDF Extraction For PDFs, use the `summarize` skill or `pdfplumber`/`PyMuPDF` to extract text before processing. ## Workflow 1. **Identify source type** — determine which input field is populated 2. **Extract raw content** — PDF text, URL content, or use provided raw text 3. **Parse bibliographic fields** — title, authors, year, source, DOI 4. **Identify research content** — material system, device type, variables, metrics 5. **Distill findings** — separate what was measured from what was claimed 6. **Assemble writeback_payload** — structured record matching the schema above 7. **Assess completeness** — set `writeback_ready` based on presence of key identity fields ## Failure Handling If parsing is incomplete: - Return partial structured output with all successfully extracted fields - Populate `uncertain_fields` with the list of fields that could not be determined - Set `writeback_ready = false` when title, authors, or year are missing ## Cross-Reference For synthesis after normalization, see the `research` skill for paper synthesis workflows.