{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-awesome-ai-market-maps","slug":"awesome-ai-market-maps","name":"Awesome AI Market Maps","type":"repo","url":"https://github.com/joylarkin/Awesome-AI-Market-Maps","page_url":"https://unfragile.ai/awesome-ai-market-maps","categories":["automation"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-awesome-ai-market-maps__cap_0","uri":"capability://memory.knowledge.temporal.thematic.market.map.aggregation.and.indexing","name":"temporal-thematic market map aggregation and indexing","description":"Aggregates 400+ AI market maps from 50+ sources (Tier 1 VCs, specialized investors, analysts) into a unified README.md single-source-of-truth using a two-dimensional taxonomy (temporal quarters/months × thematic AI domains). Implements hierarchical markdown structure with level-2 headers for quarters and level-3 headers for months, enabling deterministic parsing by downstream automation pipelines. The architecture enforces unidirectional data flow where README.md is the canonical source, preventing synchronization conflicts across derivative outputs (RSS, CSV, external platforms).","intents":["Track AI market landscape evolution across quarters and domains without manual aggregation","Discover which AI subcategories (agents, RAG, code generation, etc.) are attracting investor attention","Monitor market map publication patterns from specific VCs and analysts over time","Build a historical record of AI market perception shifts from Q1 2024 through Q1 2026"],"best_for":["AI investors and VCs evaluating market positioning and competitive landscapes","AI founders researching market saturation and emerging subcategories","Researchers analyzing AI industry trends and VC thesis evolution","Product managers tracking competitor market maps and positioning"],"limitations":["Manual curation process creates latency between map publication and inclusion (typically 1-7 days)","No automated deduplication of maps published by multiple sources — requires manual review","Taxonomy is fixed at collection time; retroactive category changes require README.md edits","No semantic analysis of map content — only metadata (title, source, date, URL) is indexed"],"requires":["GitHub account to access repository","Markdown parser to read README.md structure","Internet access to follow external links to original market maps"],"input_types":["Market map URLs from VCs, analysts, and practitioners","Metadata: publication date, source organization, domain category","Human curation decisions on inclusion/exclusion"],"output_types":["Hierarchical markdown document (README.md)","Structured CSV export with columns: date, source, title, category, URL","RSS feed entries with publication metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_1","uri":"capability://automation.workflow.automated.rss.feed.generation.from.markdown.source","name":"automated rss feed generation from markdown source","description":"Transforms README.md markdown structure into valid RSS/XML feed via GitHub Actions workflow (re-build-rss.yml) that runs on push events. The generate_rss.py script parses markdown hierarchically starting from the '## ▦ MARKET MAPS ▦' delimiter, extracts market map entries with metadata (title, source, date, URL), sanitizes text for XML compatibility, and generates timestamped RSS entries. Implements real-time syndication with near-zero latency between README.md updates and feed availability, enabling subscribers to receive new market maps via RSS readers without polling the repository.","intents":["Subscribe to new market map additions via RSS reader without visiting GitHub","Integrate market map updates into personal knowledge management systems (Obsidian, Notion, etc.)","Trigger downstream workflows when new maps are published (e.g., Zapier, IFTTT automations)","Monitor specific VC or analyst market maps in real-time across multiple sources"],"best_for":["Researchers and investors who use RSS readers as primary information intake","Automation enthusiasts building custom workflows on top of market map data","Teams integrating market intelligence into internal dashboards or Slack bots","Content aggregators republishing curated market maps to broader audiences"],"limitations":["RSS generation is fully automated but only triggers on GitHub pushes — manual README.md edits required","Text sanitization for XML may strip formatting (bold, italics) from original markdown","Feed entries include only metadata (title, source, date, URL) — no full market map content or summaries","No incremental feed updates — entire feed regenerated on each push, potentially causing duplicate entries in some RSS readers"],"requires":["GitHub Actions enabled on repository","Python 3.7+ runtime for generate_rss.py script","RSS reader or aggregator supporting standard RSS 2.0 format","Network access to feeds/AIMarketMaps.xml endpoint"],"input_types":["Markdown-formatted market map entries in README.md","Git commit metadata (timestamp, author)","Markdown link syntax: [Title](URL)"],"output_types":["Valid RSS 2.0 XML feed (feeds/AIMarketMaps.xml)","RSS item entries with title, link, description, pubDate, guid"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_10","uri":"capability://tool.use.integration.external.platform.integration.and.cross.posting","name":"external platform integration and cross-posting","description":"Integrates with external platforms (Twitter, LinkedIn, Slack) to republish market map updates beyond the GitHub repository. Market map additions can be automatically or manually cross-posted to these platforms, extending reach to audiences who don't follow the GitHub repository directly. Integration points include Twitter API for tweet posting, LinkedIn API for article sharing, and Slack webhooks for channel notifications. This capability enables the market map collection to function as a content distribution hub, with GitHub as the source of truth and external platforms as distribution channels. Cross-posting can be triggered manually by the maintainer or automated via GitHub Actions workflows.","intents":["Reach broader audiences on Twitter, LinkedIn, and Slack who don't follow GitHub","Distribute market map updates to team Slack channels for internal awareness","Build social proof and engagement around market map discoveries","Integrate market intelligence into existing team communication workflows"],"best_for":["Maintainers wanting to amplify reach of market map collection","Teams using Slack as primary communication hub","Researchers and analysts active on Twitter and LinkedIn","Organizations building internal market intelligence workflows"],"limitations":["External platform integrations depend on third-party APIs and rate limits","Cross-posting creates additional latency and potential for sync failures","Platform-specific formatting requirements may truncate or distort metadata","No automatic reverse-sync — updates on external platforms don't update GitHub","Requires API credentials and OAuth setup for each platform","Twitter/LinkedIn posts may be deleted or edited independently, creating divergence"],"requires":["API credentials for Twitter, LinkedIn, and/or Slack","OAuth setup and token management","GitHub Actions workflow configuration for automation (optional)","Understanding of platform-specific API requirements"],"input_types":["Market map metadata from README.md","Platform-specific formatting templates","Trigger events (manual post or GitHub Actions workflow)"],"output_types":["Twitter posts with market map title, source, and link","LinkedIn articles or posts with market map summary","Slack messages in designated channels"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_11","uri":"capability://search.retrieval.market.map.discovery.and.research.support","name":"market map discovery and research support","description":"Enables researchers and analysts to discover relevant market maps for specific AI domains, time periods, or source organizations through browsing, filtering, and searching capabilities. Users can navigate the hierarchical README.md structure to find maps by quarter/month or domain, use CSV export to filter programmatically, or subscribe to RSS feed for specific categories. The repository also serves as a research artifact itself, enabling meta-analysis of market map creation patterns (e.g., 'which domains have the most maps?', 'how has VC focus shifted over time?'). This capability transforms the collection from a passive list into an active research tool for understanding AI market evolution.","intents":["Find all market maps related to a specific AI domain (agents, RAG, code generation, etc.)","Research how investor focus on specific domains has evolved over quarters","Discover market maps from specific VCs or analysts","Analyze patterns in market map creation (volume trends, domain focus shifts)","Use the collection as a research artifact to study AI market perception"],"best_for":["Researchers studying AI market trends and investor thesis evolution","Founders researching competitive landscapes in specific AI domains","Investors analyzing peer market maps and positioning","Analysts building market intelligence reports","Academics studying VC investment patterns and market dynamics"],"limitations":["Discovery is limited to metadata (title, source, date, category) — no full-text search of map content","No semantic analysis — cannot find maps by topic unless explicitly categorized","Filtering requires manual browsing or CSV export — no built-in search interface","No recommendation engine — users must manually explore to find relevant maps","Meta-analysis requires external tools (Pandas, Excel, etc.) — no built-in analytics"],"requires":["Access to README.md for browsing","CSV parser or spreadsheet tool for filtering","Data analysis tools (Pandas, Excel, SQL) for meta-analysis","Understanding of AI domain landscape to interpret results"],"input_types":["User queries (domain, time period, source organization)","Filtering criteria (date range, category, source)"],"output_types":["Filtered list of relevant market maps","Meta-analysis results (trend charts, statistics)","Research insights about market evolution"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_2","uri":"capability://data.processing.analysis.structured.csv.export.with.manual.refresh.cadence","name":"structured csv export with manual-refresh cadence","description":"Exports aggregated market map metadata into a structured CSV dataset (ai_market_maps.csv) with columns for date, source organization, market map title, AI domain category, and direct URL link. The export is manually maintained with documented lag (typically bimonthly refresh cycle), allowing downstream tools (Pandas, Excel, Tableau, SQL databases) to ingest market map data for analysis, filtering, and visualization. Provides a machine-readable alternative to markdown for users who need tabular data structures, enabling programmatic access without parsing markdown syntax.","intents":["Import market map metadata into data analysis tools (Pandas, R, SQL) for statistical analysis","Filter and sort market maps by date range, source organization, or AI domain category","Build custom dashboards or visualizations of market map publication trends","Export market map data to spreadsheet tools for manual analysis or team sharing"],"best_for":["Data analysts and researchers performing quantitative analysis of market map trends","Product managers building internal dashboards of competitive market maps","Teams using spreadsheet tools (Excel, Google Sheets) for collaborative analysis","Developers building custom tools that require structured market map metadata"],"limitations":["Manual refresh process creates 1-14 day lag between README.md updates and CSV availability","No automated validation — stale or malformed entries may persist until next manual refresh","CSV schema is fixed; adding new metadata fields requires manual column additions and backfilling","No incremental updates — entire CSV regenerated, making it unsuitable for real-time streaming applications"],"requires":["CSV parser or spreadsheet application (Excel, Google Sheets, Pandas, etc.)","Python 3.7+ for programmatic CSV processing","Network access to ai_market_maps.csv file in repository"],"input_types":["Market map metadata from README.md (title, source, date, category, URL)","Manual curation decisions on field values and formatting"],"output_types":["CSV file with columns: date, source, title, category, url","Structured rows compatible with spreadsheet and data analysis tools"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_3","uri":"capability://automation.workflow.multi.format.content.distribution.with.tiered.freshness.strategy","name":"multi-format content distribution with tiered freshness strategy","description":"Distributes aggregated market map data across three output formats (Markdown README, RSS feed, CSV export) with intentionally different update cadences: README.md updates on manual edits (immediate), RSS regenerates on every push (near-real-time), and CSV refreshes bimonthly (batch). This tiered freshness strategy allows different consumer personas to choose their preferred trade-off between recency and stability. The architecture maintains unidirectional data flow from README.md as single source of truth, preventing synchronization conflicts while enabling each format to optimize for its use case (human browsing, feed subscription, data analysis).","intents":["Consume market map updates via preferred channel (web browsing, RSS reader, data analysis tool)","Integrate market intelligence into heterogeneous systems with different update frequency requirements","Access market maps through external platform integrations (Twitter, LinkedIn, Slack bots)","Choose freshness vs. stability trade-off based on use case (real-time monitoring vs. batch analysis)"],"best_for":["Organizations with diverse teams using different tools (VCs using RSS, analysts using CSV, founders browsing web)","Automation enthusiasts building multi-channel market intelligence pipelines","Teams integrating market maps into existing data workflows with varying update requirements","Content aggregators republishing to multiple platforms with different sync frequencies"],"limitations":["Tiered freshness creates inconsistency windows where different formats show different data (e.g., RSS has new map, CSV doesn't)","No automatic format validation — malformed entries in README.md propagate to all outputs","External platform integrations (Twitter, LinkedIn) depend on third-party services and may have additional latency","Users must manually select appropriate format for their use case; no intelligent routing"],"requires":["GitHub repository access for README.md and RSS","CSV parser or spreadsheet tool for CSV format","RSS reader for feed subscription","External platform accounts (Twitter, LinkedIn, Slack) for integrated access"],"input_types":["Market map metadata edited into README.md","Git commit events triggering RSS regeneration","Manual CSV refresh triggers"],"output_types":["Markdown document (README.md) for web browsing","RSS 2.0 feed (feeds/AIMarketMaps.xml) for syndication","CSV dataset (ai_market_maps.csv) for data analysis","External platform posts (Twitter, LinkedIn, Slack messages)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_4","uri":"capability://memory.knowledge.ai.domain.taxonomy.and.hierarchical.categorization","name":"ai domain taxonomy and hierarchical categorization","description":"Implements a fixed taxonomy of AI domain categories (agents, RAG, code generation, image generation, etc.) used to classify and organize market maps within the README.md structure. Market maps are grouped by both temporal dimension (quarters/months) and thematic dimension (AI domain), enabling discovery along either axis. The taxonomy is curated by the repository maintainer and applied consistently across all 400+ market maps, allowing users to filter by domain (e.g., 'show me all agent-related market maps') or track how investor attention shifts within specific domains over time.","intents":["Discover all market maps related to a specific AI domain (agents, RAG, code generation, etc.)","Track how investor interest in specific AI domains evolves across quarters","Identify which domains have the most market map coverage (proxy for investor attention)","Filter market maps by domain when researching competitive landscapes in specific areas"],"best_for":["AI founders researching market saturation and investor thesis in their domain","Investors tracking which AI subcategories are attracting peer attention","Researchers analyzing AI market trends and investor focus shifts","Product managers evaluating competitive positioning within specific AI domains"],"limitations":["Taxonomy is fixed at collection time; adding new domains requires manual README.md edits","Single-category assignment per map — no multi-domain tagging for maps spanning multiple areas","No hierarchical taxonomy (e.g., 'agents' as parent of 'autonomous agents', 'agentic workflows') — flat structure only","Taxonomy reflects curator's judgment; no community voting or consensus mechanism for category definitions","No semantic analysis of map content — categorization based on title and curator knowledge, not actual map analysis"],"requires":["Knowledge of AI domain landscape to understand category definitions","Access to README.md to view category structure","Markdown parser to programmatically extract category information"],"input_types":["Market map title and source metadata","Curator's domain knowledge and judgment","Manual category assignment during curation"],"output_types":["Categorized market map entries in README.md","Category-filtered views in CSV export","Category-based RSS feed subscriptions (if supported by reader)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_5","uri":"capability://data.processing.analysis.temporal.market.map.organization.with.quarterly.and.monthly.granularity","name":"temporal market map organization with quarterly and monthly granularity","description":"Organizes market maps along a temporal dimension using hierarchical markdown headers: level-2 headers for quarters (e.g., '## AI Market Maps - Q1 2026') and level-3 headers for months (e.g., '### January 2026'). This structure enables users to browse market maps by publication date, track how market maps evolve within specific time periods, and identify temporal trends (e.g., 'which domains had the most maps in Q4 2025?'). The temporal hierarchy is deterministically parseable by automation scripts, allowing RSS generation and CSV export to preserve publication dates and enable time-based filtering.","intents":["Browse market maps published in a specific quarter or month","Track how market map publication volume changes over time","Identify temporal trends in investor focus (e.g., surge in agent maps in Q4 2025)","Filter market maps by date range for time-bounded research"],"best_for":["Researchers analyzing temporal trends in AI market perception","Investors tracking how their peers' market maps evolve quarter-over-quarter","Founders researching market timing and when specific domains gained investor attention","Analysts building time-series visualizations of market map publication patterns"],"limitations":["Granularity is fixed at monthly level — no daily or weekly organization","Retroactive date changes require manual README.md edits and may break parsing if not careful","No timezone normalization — publication dates reflect source timezone without standardization","Temporal organization assumes maps are published in chronological order; out-of-order additions require manual reordering"],"requires":["Markdown parser supporting hierarchical header extraction","Understanding of quarter/month boundaries for filtering","Access to README.md to view temporal structure"],"input_types":["Market map publication date (from source or curator estimate)","Temporal hierarchy decisions (which quarter/month to assign)"],"output_types":["Hierarchically organized markdown sections","Date-tagged CSV entries","Timestamped RSS feed entries with pubDate field"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_6","uri":"capability://memory.knowledge.human.in.the.loop.curation.with.quality.filtering","name":"human-in-the-loop curation with quality filtering","description":"Applies manual curation to maintain Awesome List standards, where the repository maintainer (Joy Larkin) reviews candidate market maps for inclusion based on subjective 'taste' criteria. The curation process filters out low-quality, duplicative, or off-topic maps before adding them to README.md, ensuring the collection remains focused and high-signal. Curation decisions are made asynchronously via GitHub issues, pull requests, and direct contributions, with the maintainer as final arbiter of inclusion. This human-in-the-loop approach trades scalability for quality, keeping the collection curated rather than exhaustive.","intents":["Ensure market map collection maintains high quality and relevance standards","Filter out duplicative maps from multiple sources covering the same market","Maintain focus on substantive, well-researched maps rather than low-effort content","Preserve the 'awesome' brand by applying consistent editorial standards"],"best_for":["Users who value curated, high-signal collections over exhaustive databases","Teams building on top of the repository who need quality guarantees","Researchers who want to study only the most influential market maps","Awesome List enthusiasts who expect editorial standards"],"limitations":["Curation creates latency — maps may take 1-7 days to be reviewed and added","Curator bias — inclusion decisions reflect maintainer's subjective taste, not objective criteria","Scalability bottleneck — single maintainer cannot review all submissions at high volume","No transparent rubric — curation criteria are implicit rather than explicitly documented","No appeals process — rejected maps have no formal recourse"],"requires":["GitHub account to submit market maps for consideration","Willingness to follow contribution guidelines","Patience for review process (typically 1-7 days)"],"input_types":["Candidate market map URLs and metadata from contributors","GitHub issues and pull requests proposing additions","Curator's domain knowledge and judgment"],"output_types":["Approved market maps added to README.md","Rejected submissions (with optional feedback)","Curated collection maintaining quality standards"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_7","uri":"capability://tool.use.integration.contribution.workflow.and.community.submission.pipeline","name":"contribution workflow and community submission pipeline","description":"Enables community contributions via GitHub pull requests and issues, allowing users to propose new market maps for inclusion in the collection. Contributors submit market map URLs with metadata (title, source, date, category) following documented guidelines, which are then reviewed by the maintainer for quality and relevance. The workflow is asynchronous and GitHub-native, requiring no external tools or registration beyond a GitHub account. Accepted contributions are merged into README.md, while rejected submissions receive optional feedback. This distributed contribution model allows the repository to scale beyond the maintainer's personal discovery while maintaining quality control.","intents":["Submit newly discovered market maps for inclusion in the collection","Propose corrections or updates to existing market map entries","Suggest new AI domain categories or organizational improvements","Participate in community curation of the market map landscape"],"best_for":["Community members who discover market maps and want to share them","Researchers and analysts who want to contribute their findings","VC firms and analysts who want to ensure their maps are included","Open-source enthusiasts who value community-driven curation"],"limitations":["Requires GitHub account and familiarity with pull request workflow","No guaranteed acceptance — contributions subject to maintainer's curation judgment","No SLA for review time — contributions may wait 1-14 days for feedback","Contribution guidelines are informal; no structured template or validation","No reputation system or contributor recognition beyond GitHub commit history"],"requires":["GitHub account","Git knowledge (or GitHub web UI for simple edits)","Understanding of contribution guidelines","Markdown editing skills"],"input_types":["Market map URL","Metadata: title, source organization, publication date, AI domain category","Optional: description or rationale for inclusion"],"output_types":["GitHub pull request with proposed changes","Merged README.md entry (if accepted)","Feedback or rejection reason (if declined)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_8","uri":"capability://data.processing.analysis.market.map.metadata.extraction.and.normalization","name":"market map metadata extraction and normalization","description":"Extracts and normalizes metadata from market map entries in README.md, including title, source organization, publication date, AI domain category, and direct URL link. The extraction process is deterministic, parsing markdown link syntax and hierarchical headers to identify metadata fields. Normalization includes date standardization (converting various date formats to YYYY-MM-DD), source name deduplication (e.g., 'a16z' vs 'Andreessen Horowitz'), and URL validation. This normalized metadata feeds downstream systems (RSS generation, CSV export, external platform integrations), enabling consistent data representation across all output formats.","intents":["Extract structured metadata from unstructured markdown for programmatic processing","Normalize inconsistent date formats and source names for analysis","Validate URLs and detect broken links in market map collection","Enable cross-format consistency (same metadata in README, RSS, CSV)"],"best_for":["Developers building tools that consume market map metadata","Data analysts who need clean, normalized data for analysis","Automation engineers building pipelines that depend on consistent metadata","Quality assurance teams validating data integrity"],"limitations":["Extraction depends on consistent markdown formatting — malformed entries may fail to parse","Normalization rules are hardcoded in generate_rss.py — changes require code modifications","No validation of metadata correctness (e.g., whether publication date is accurate)","Date extraction may fail if dates are written in non-standard formats","No deduplication of maps published by multiple sources — requires manual review"],"requires":["Python 3.7+ for running generate_rss.py extraction script","Markdown parser library (built into generate_rss.py)","Consistent markdown formatting in README.md"],"input_types":["Markdown-formatted market map entries","Hierarchical header structure (quarters/months)","Markdown link syntax: [Title](URL)"],"output_types":["Normalized metadata tuples: (date, source, title, category, url)","Structured data suitable for CSV, RSS, or database ingestion"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-awesome-ai-market-maps__cap_9","uri":"capability://data.processing.analysis.git.history.integration.for.publication.date.inference","name":"git history integration for publication date inference","description":"Integrates Git commit history to infer or validate publication dates for market maps when explicit dates are unavailable or uncertain. The system can query Git metadata (commit timestamps, author information) to determine when a market map entry was added to the repository, providing a fallback date source. This capability enables temporal analysis even when source market maps lack explicit publication dates, and allows the system to track when maps were discovered/added relative to their actual publication. Git history integration is used by the RSS generation pipeline to populate pubDate fields and by temporal analysis tools to understand discovery lag.","intents":["Infer publication dates for market maps when source dates are unavailable","Track discovery lag between actual publication and repository inclusion","Validate curator-provided dates against Git commit history","Enable time-series analysis of market map discovery patterns"],"best_for":["Researchers analyzing discovery lag and information diffusion in VC market","Analysts studying how quickly new market maps are discovered and shared","Quality assurance teams validating date accuracy","Developers building temporal analysis tools"],"limitations":["Git commit date reflects repository addition, not actual market map publication date","Bulk edits or rebasing can distort commit history and invalidate date inference","Requires access to full Git history — shallow clones will miss historical commits","No distinction between initial addition and subsequent edits — all commits treated equally","Timezone information from Git commits may not match source publication timezone"],"requires":["Git repository access with full history","Git command-line tools or Python GitPython library","Understanding of Git commit metadata structure"],"input_types":["Git commit history for README.md","Commit timestamps and author information","Explicit publication dates from market map sources (optional)"],"output_types":["Inferred publication dates (when explicit dates unavailable)","Discovery lag metrics (days between publication and repository addition)","Validated date fields for RSS and CSV export"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["GitHub account to access repository","Markdown parser to read README.md structure","Internet access to follow external links to original market maps","GitHub Actions enabled on repository","Python 3.7+ runtime for generate_rss.py script","RSS reader or aggregator supporting standard RSS 2.0 format","Network access to feeds/AIMarketMaps.xml endpoint","API credentials for Twitter, LinkedIn, and/or Slack","OAuth setup and token management","GitHub Actions workflow configuration for automation (optional)"],"failure_modes":["Manual curation process creates latency between map publication and inclusion (typically 1-7 days)","No automated deduplication of maps published by multiple sources — requires manual review","Taxonomy is fixed at collection time; retroactive category changes require README.md edits","No semantic analysis of map content — only metadata (title, source, date, URL) is indexed","RSS generation is fully automated but only triggers on GitHub pushes — manual README.md edits required","Text sanitization for XML may strip formatting (bold, italics) from original markdown","Feed entries include only metadata (title, source, date, URL) — no full market map content or summaries","No incremental feed updates — entire feed regenerated on each push, potentially causing duplicate entries in some RSS readers","External platform integrations depend on third-party APIs and rate limits","Cross-posting creates additional latency and potential for sync failures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.39,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.27,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:20.516Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=awesome-ai-market-maps","compare_url":"https://unfragile.ai/compare?artifact=awesome-ai-market-maps"}},"signature":"g94LULvY1xK9f1KQY3V/EaKZFUiZ9veL68HuX57Ak1/S88mBhB6C2JxQOCy6Xskjx0fLSQbHh7i820sHeSd3DA==","signedAt":"2026-06-20T16:20:27.346Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/awesome-ai-market-maps","artifact":"https://unfragile.ai/awesome-ai-market-maps","verify":"https://unfragile.ai/api/v1/verify?slug=awesome-ai-market-maps","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}