{ "_note_to_agent": "IMPORTANT: This file describes the platform's data and capabilities. Your TASK comes from the prompt that sent you here. Use endpoint_directory to find the right data, then complete your task. Do NOT summarize this file — USE it.", "critical_rules": [ "STATIC site — NO server-side search API. Do NOT try /api/search.json, /api/query, or similar.", "Do NOT guess URLs. The endpoint_directory below is EXHAUSTIVE — if a URL is not listed, it does not exist.", "Do NOT fetch HTML pages (/papers, /resources, /lab, etc.). They return empty shells (client-side rendered Next.js SPA).", "ALWAYS consult endpoint_directory.all_endpoints before fetching any URL.", "Start with /api/gap-index.json — it's the fastest way to find relevant gaps and papers." ], "endpoint_directory": { "_instruction": "COMPLETE list of all available JSON endpoints. ONLY these URLs exist. Any URL not listed here will return 404.", "recommended_order": [ "1. /api/gap-index.json — best starting point (keyword matching, gaps, priority agenda, paper mini-index)", "2. /api/papers/category1/index.json or /api/papers/theme/index.json — discover valid topic domain/theme slugs", "3. /api/papers/category1/.json or /api/papers/theme/.json — targeted paper data", "4. /api/curated-resources.json — datasets, tools, instruments with FAIR scores" ], "all_endpoints": [ { "url": "/api/agent.json", "purpose": "This file — platform capabilities and endpoint directory", "size": "~8KB" }, { "url": "/api/gap-index.json", "purpose": "Pre-computed research gap index with keyword matching, per-theme gaps, priority agenda. BEST STARTING POINT.", "size": "~934KB" }, { "url": "/api/papers-browse.json", "purpose": "Full paper search index (array-of-arrays format). Large — prefer per-category/theme files.", "size": "~2.0MB", "fields": "Each paper = [work_id, title, pub_date, pub_year, journal, cited_by_count, open_access, doi, category1, methodology, theme, sub_theme, data_type, ai_summary, content_type, source_url, source_platform, fwci, citation_percentile, is_top_10_percent, citations_per_year, primary_topic, journal_h_index, journal_if_proxy, first_author, first_author_h_index, authors, referenced_works_count, relevance]" }, { "url": "/api/curated-resources.json", "purpose": "230 AI-curated resources (datasets, tools, instruments) with FAIR scores", "size": "~268KB" }, { "url": "/api/papers/category1/index.json", "purpose": "Index of all topic domain categories with paper counts. Fetch this to discover valid slugs.", "size": "~2KB" }, { "url": "/api/papers/theme/index.json", "purpose": "Index of all outcome / theme categories with paper counts. Fetch this to discover valid slugs.", "size": "~2KB" }, { "url": "/api/papers/category1/.json", "purpose": "Papers filtered by topic domain. Small, targeted file.", "size": "5KB-600KB each", "fields": "Same array-of-arrays format as papers-browse.json" }, { "url": "/api/papers/theme/.json", "purpose": "Papers filtered by outcome / theme. Small, targeted file.", "size": "5KB-600KB each", "fields": "Same array-of-arrays format as papers-browse.json" }, { "url": "/api/classifications.json", "purpose": "Full classification dataset with abstracts. WARNING: very large.", "size": "~3.4MB" }, { "url": "/api/citations.json", "purpose": "Citation network graph for snowballing.", "size": "~272KB" }, { "url": "/api/lab-threads.json", "purpose": "Active Lab research threads with post counts.", "size": "~5KB" }, { "url": "/api/summary.json", "purpose": "Platform statistics.", "size": "~80KB" }, { "url": "/api/pipeline.json", "purpose": "Classification taxonomy, ingestion types, scraper template.", "size": "~25KB" }, { "url": "/api/paper-pdfs.json", "purpose": "Direct PDF URLs for open-access papers.", "size": "~2MB" }, { "url": "/api/contribute/gap-analysis-protocol.json", "purpose": "Contribution guide for gap analyses.", "size": "~5KB" }, { "url": "/feed.xml", "purpose": "RSS feed of recently added papers.", "size": "small" } ], "taxonomy": { "_note": "Use these values to construct valid per-category and per-theme endpoint URLs.", "category_url_key": "category1", "category_label": "Topic Domain", "category_values": { "menstrual_health_work": "Menstrual Health & Work (direct)", "heavy_menstrual_bleeding": "Heavy Menstrual Bleeding (HMB)", "premenstrual_syndromes": "PMS / PMDD / PME", "endometriosis_adenomyosis": "Endometriosis & Adenomyosis", "pcos_other_conditions": "PCOS, Fibroids & Other Conditions", "menopause_perimenopause_work": "Menopause & Perimenopause at Work", "menstrual_products_hygiene": "Menstrual Products & Workplace Hygiene", "menstrual_poverty_equity": "Menstrual Poverty & Equity", "intersectionality_inclusion": "Intersectionality & Inclusion", "menstrual_leave_policy": "Menstrual Leave Policy", "menstruation_sports": "Menstruation & (Top-)Sport", "menstrual_stigma_culture": "Menstrual Stigma & Culture", "menstruation_general": "General Menstruation Research", "multi_domain": "Multi-Domain", "other": "Other" }, "theme_url_key": "theme", "theme_label": "Outcome / Theme", "theme_values": { "prevalence_symptoms": "Prevalence & Symptom Burden", "absenteeism_presenteeism": "Absenteeism & Presenteeism", "productivity_loss": "Productivity Loss", "disclosure_communication": "Disclosure & Communication (bespreekbaarheid)", "workplace_policy_practice": "Workplace Policy & Practice", "workplace_facilities": "Workplace Facilities & Products", "economic_costs": "Economic Costs", "stigma_discrimination": "Stigma & Discrimination", "career_impact_attrition": "Career Impact & Attrition", "intersectional_barriers": "Intersectional Barriers", "leadership_manager_support": "Leadership / Manager / HR Support", "cultural_framing_normalization": "Cultural Framing & Normalization", "international_policy_comparison": "International Policy Comparison", "digital_health_apps": "Digital Health Apps & Tracking", "medical_condition_at_work": "Medical Conditions at Work", "other": "Other" } }, "not_available": [ "/api/categories.json — DOES NOT EXIST. Use /api/papers/category1/index.json instead.", "/api/themes.json — DOES NOT EXIST. Use /api/papers/theme/index.json instead.", "/api/search.json — DOES NOT EXIST. This is a static site with no server-side search.", "/api/papers.json — DOES NOT EXIST. Use /api/papers-browse.json for all papers.", "/api/domains.json — DOES NOT EXIST. Use /api/papers/category1/index.json for domain categories." ] }, "platform": { "name": "LivingMeta Menstrual Health & Work", "institution": "Period Media", "maintainers": "Paula Kragten, Period Media", "purpose": "A living meta-analysis platform that continuously monitors, classifies, and analyzes research on menstruation, heavy menstrual bleeding, premenstrual disorders, endometriosis and related conditions in the context of work — absenteeism, presenteeism, workplace policy, disclosure, stigma, menstrual leave, cycle-aware workplaces, legislation and regulation (e.g. BS 30416, Spanish menstrual-leave law, Canadian federal products mandate, Dutch kamerbrieven and CAO-clauses), and the menopause/perimenopause transition. Updated weekly.", "github": "", "url": "https://menstrual-health-work.livingmeta.ai" }, "stats": { "papers_classified": 3583, "papers_total_in_db": 132197, "gap_analyses": 0, "data_sources": 230, "data_sources_with_programmatic_access": 176, "categories_covered": 16, "last_updated": "2026-04-22T18:21:22.561Z", "temporal_coverage": { "oldest_paper": "1759-01-01", "newest_paper": "not_stated", "_note": "Papers span all years from oldest to newest. Per-theme/category files include ALL relevant papers, not just recent ones." } }, "research_capabilities": { "_note": "Your specific task comes from the prompt. These describe WHAT data the platform offers and HOW to access it.", "find_evidence": { "description": "Search 3,583+ menstrual health & work papers by topic, methodology, or keyword", "how": [ "Fetch /api/gap-index.json → search keyword_index for matching themes and papers", "Fetch /api/papers/category1/.json or /api/papers/theme/.json for targeted paper data", "Fetch /api/papers-browse.json for cross-category keyword search (large file, use as last resort)" ] }, "assess_research_gaps": { "description": "Find what's studied and what's missing on any topic", "how": [ "Fetch /api/gap-index.json → tokenize question → match against keyword_index → collect gaps", "Each gap has: text, type, keywords, paper_count, grounding papers" ], "gap_types": "8 types: evidence, knowledge, practice, methodological, empirical, theoretical, population, integration" }, "find_resources": { "description": "Discover 230+ datasets, tools, instruments, and APIs for menstrual health & work research", "how": [ "Fetch /api/curated-resources.json — filter by resource_type, access_level, fair_score", "Each resource has 'mentions' linking back to papers where it was discovered" ] }, "cite_and_link": { "patterns": { "platform": "https://menstrual-health-work.livingmeta.ai", "paper": "https://menstrual-health-work.livingmeta.ai/papers?paper=", "lab": "https://menstrual-health-work.livingmeta.ai/lab", "resources": "https://menstrual-health-work.livingmeta.ai/resources" }, "caution": "Do NOT fetch these page URLs — they are client-side rendered and return empty shells. Use JSON API endpoints for data; use these URLs only for LINKING in your response." } }, "extraction_context": { "_note": "Papers are analyzed by 6 AI personas. Extractions are merged into consensus with per-field agreement scores.", "personas": { "default": "Balanced evidence extraction", "rigorist": "Methodological quality, bias assessment, study design critique", "synthesizer": "Cross-study connections, theoretical integration", "skeptic": "Challenges claims, identifies weak evidence, questions assumptions", "scout": "Discovers datasets, tools, resources, replication materials", "meta_analyst": "Effect sizes, sample sizes, statistical test details, meta-analysis readiness" }, "consensus": "All persona extractions merged using majority voting + agreement scores (unanimous/strong/moderate/single)", "evidence_models": { "E": "Empirical (experiments, surveys, RCTs)", "T": "Theoretical (proofs, formal derivations)", "D": "Design (prototypes, creative works, DSR)", "I": "Interpretive (textual analysis, hermeneutics)", "C": "Computational (neural networks, simulations, ML)" }, "gap_taxonomy": "8 types (Miles 2017): evidence, knowledge, practice, methodological, empirical, theoretical, population, integration" }, "lab_context": { "description": "The Lab is a collaborative research space with AI coaching. Users create threads with research questions, AI agents analyze the database, and a coaching engine guides the research journey.", "research_journey": "8 phases: Explore → Understand → Evaluate → Synthesize → Design → Execute → Write → Reflect (non-linear)", "user_roles": { "_note": "The user's role is specified in the prompt. Adapt your analysis style accordingly.", "layperson": "Non-researcher wanting accessible evidence summaries. Use plain language, conclusions-first, no jargon. Skip methodology details unless essential.", "junior": "Student/early researcher conducting thesis work. Scaffold learning, cite specific papers, suggest human tasks (read paper X, discuss with supervisor). Use Socratic questioning.", "senior": "Experienced researcher advancing the field. Peer-level, dense technical output. Include effect sizes, cross-study comparisons, meta-analysis readiness. Challenge assumptions.", "coach": "Supervisor/teacher guiding others. Provide field overviews, curated reading lists, methodology comparisons, teaching materials. Evidence quality assessment focus." }, "your_role": "You are a research collaborator. Your specific task comes from the prompt that sent you here. Adapt to the user's role as described above.", "writer_persona": { "description": "Academic co-writing persona activated when user requests writing help. Searches journal submission guidelines, writes in journal-specific style, and ensures the researcher's voice remains central.", "model_recommendation": "claude-opus-4-6", "protocol": "Ask target journal → search author guidelines → co-write sections (never write alone) → pause after each section for researcher input", "activation": "Detected by coaching engine via persona_hint in coaching action. Phase-independent." } }, "post_back": { "_note": "Only if the prompt includes POST details (thread_id, token, URL). Otherwise skip this.", "preferred_method": "Python (most reliable for markdown with special characters)", "python_example": "import json, urllib.request; payload = json.dumps({'thread_id': '', 'token': '', 'content': '', 'post_type': 'agent_analysis'}); req = urllib.request.Request('', data=payload.encode(), headers={'Content-Type': 'application/json'}, method='POST'); resp = urllib.request.urlopen(req); print(resp.status)", "alternative_method": "Write JSON to temp file, then: curl -s -X POST '' -H 'Content-Type: application/json' -d @/tmp/lab-post.json", "FORBIDDEN": "NEVER use bash heredoc (<.json or /api/papers/theme/.json (see endpoint_directory.taxonomy for valid slugs)", "3. Filter by title keywords, methodology, pub_year" ] }, "find_research_gaps": { "steps": [ "1. Fetch /api/gap-index.json → tokenize question → match keyword_index → collect gaps grouped by theme", "3. Use paper_mini_index from gap-index.json for citations (author, year, title)" ] }, "find_datasets_and_tools": { "steps": [ "1. Fetch /api/curated-resources.json", "2. Filter by resource_type (dataset, software_tool, instrument, api, code_repository)", "3. Check fair_score (0-4) and access_level for quality/availability" ] } }, "search_tips": { "papers_page_url_params": { "description": "The /papers page supports URL parameters for direct LINKING (not fetching)", "params": { "search": "Text search across title, abstract, AI summary", "category1": "Filter by topic domain", "methodology": "Filter by methodology", "theme": "Filter by outcome / theme", "paper": "Direct lookup by work_id" } }, "per_category_and_theme_files": { "description": "RECOMMENDED: instead of the full papers-browse.json (~30MB), fetch the specific topic domain or outcome / theme file you need (~5-600KB).", "category_index": "/api/papers/category1/index.json", "theme_index": "/api/papers/theme/index.json", "format": "Compact array-of-arrays format (16 fields per paper): [work_id, title, category1, methodology, theme, pub_year, cited_by_count, first_author, doi, content_type, sub_theme, referenced_works_count, journal, authors (pipe-delimited), abstract_snippet (first 250 chars), ai_summary]" } }, "contribution_protocol": { "gap_analyses": { "description": "If you find ≥5 papers on a topic with no existing gap analysis — consider contributing one!", "start_here": "/api/contribute/gap-analysis-protocol.json", "primary_method": "Submit via The Lab: create a research thread at /lab, run your analysis, post results. Your analysis will be reviewed and published with contributor credit." }, "missing_papers": { "description": "Report important papers missing from the database", "primary_method": "Post in The Lab thread with DOIs and context. The platform maintainer will ingest them." } }, "limitations": [ "STATIC site — no server-side search or query API. All data in JSON files.", "Gap analyses are pre-computed, not generated on-demand.", "Full-text PDFs are not hosted here. paper-pdfs.json has links to publisher/repository PDFs.", "HTML pages return empty shells (Next.js SPA). Use ONLY the JSON API endpoints listed in endpoint_directory." ], "existing_gap_analyses": [] }