{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-apurvsinghgautam--robin","slug":"apurvsinghgautam--robin","name":"robin","type":"repo","url":"https://github.com/apurvsinghgautam/robin","page_url":"https://unfragile.ai/apurvsinghgautam--robin","categories":["automation"],"tags":["ai-tool","darkweb","darkweb-osint","investigation-tool","llm-powered","osint","osint-tool"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-apurvsinghgautam--robin__cap_0","uri":"capability://text.generation.language.llm.powered.query.refinement.for.dark.web.search.optimization","name":"llm-powered query refinement for dark web search optimization","description":"Transforms raw user investigation queries into optimized search terms by routing them through a pluggable multi-provider LLM layer (OpenAI, Anthropic, Google, Ollama). The system uses prompt engineering to expand queries with domain-specific dark web terminology, synonyms, and alternative phrasings that improve hit rates across heterogeneous dark web search engines. Implementation delegates to llm.refine_query() which constructs a system prompt contextualizing the dark web domain, then streams the LLM response to generate semantically richer search queries.","intents":["I want to automatically expand vague threat intelligence queries into multiple search variations without manual keyword research","I need to translate law enforcement terminology into dark web marketplace vernacular to find relevant listings","I want to generate alternative phrasings of a ransomware group name to catch misspellings and aliases across search engines"],"best_for":["threat intelligence analysts automating repetitive query expansion","law enforcement investigators scaling dark web searches across multiple jurisdictions","security researchers tracking credential exposure campaigns with evolving naming conventions"],"limitations":["LLM-based refinement adds 2-5 second latency per query due to API round-trip","Query expansion quality depends on LLM model capability; smaller models (Ollama 7B) produce less sophisticated synonyms than GPT-4","No caching of refined queries — identical raw queries trigger redundant LLM calls if not deduplicated upstream"],"requires":["Python 3.8+","API credentials for at least one LLM provider (OpenAI API key, Anthropic API key, Google API key, or local Ollama instance on localhost:11434)","llm.py module with refine_query() function","Network connectivity to LLM provider endpoints or local Ollama service"],"input_types":["plain text query string (e.g., 'ransomware payment tracking')","unstructured investigation notes"],"output_types":["list of refined search query strings","structured JSON with original query, refinement rationale, and generated variants"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_1","uri":"capability://search.retrieval.multi.engine.concurrent.dark.web.search.with.result.aggregation","name":"multi-engine concurrent dark web search with result aggregation","description":"Queries multiple dark web search engines (Torch, Ahmia, Candle, etc.) concurrently using a thread-pooled orchestration pattern implemented in search.py:get_search_results(). Each search engine query is wrapped in a timeout-protected thread to prevent hanging on slow .onion sites; results are aggregated into a unified list of URLs and titles. The system handles search engine-specific response formats through adapter patterns, normalizing heterogeneous HTML/JSON responses into a common data structure for downstream LLM filtering.","intents":["I want to query all major dark web search engines in parallel to maximize coverage without sequential latency","I need to deduplicate results across search engines to avoid processing the same .onion page multiple times","I want to rank search results by frequency across engines to identify the most indexed/relevant pages"],"best_for":["OSINT investigators needing comprehensive dark web coverage without manual multi-engine queries","threat intelligence teams automating large-scale dark web monitoring across multiple search indices","security researchers comparing search engine indexing patterns across the dark web"],"limitations":["Search engine availability is unpredictable — individual .onion search engines may be offline or rate-limited, causing partial result loss","Concurrent requests to multiple search engines increase Tor exit node load and may trigger rate-limiting or IP bans","Result normalization is lossy — search engine-specific metadata (relevance scores, date indexed) is discarded during aggregation","No built-in deduplication of URLs across engines — requires downstream processing to identify mirror pages"],"requires":["Python 3.8+","Tor service running on 127.0.0.1:9050 (SOCKS5 proxy)","search.py module with get_search_results() function","requests library configured with Tor proxy","Network connectivity to at least 2-3 dark web search engines (Torch, Ahmia, Candle, etc.)"],"input_types":["refined search query string","optional search engine whitelist/blacklist"],"output_types":["list of dicts with keys: url (str), title (str), source_engine (str), snippet (str optional)","raw HTML/JSON responses from individual search engines"],"categories":["search-retrieval","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_10","uri":"capability://automation.workflow.configuration.management.via.environment.variables.and.config.files","name":"configuration management via environment variables and config files","description":"Manages Robin configuration through a two-tier system: environment variables for sensitive credentials (API keys, Tor proxy address) and YAML/JSON config files for operational settings (model selection, timeout values, search engine whitelist). The system reads environment variables first (highest priority), then falls back to config file values, then uses hardcoded defaults. Configuration is loaded at startup in main.py and passed through the investigation pipeline. This approach enables secure credential management (via environment variables in Docker/Kubernetes) while allowing flexible operational configuration (via config files for different investigation types).","intents":["I want to configure Robin with API keys without hardcoding them in the source code","I need to adjust investigation parameters (timeout, concurrency, model) without modifying code","I want to deploy Robin to different environments (dev, staging, prod) with different configurations"],"best_for":["DevOps teams deploying Robin to multiple environments with different configurations","organizations with security requirements for credential management (no hardcoded keys)","developers customizing Robin behavior without modifying source code"],"limitations":["No built-in validation of configuration values — invalid settings may cause runtime errors","Configuration is loaded once at startup — changes require container restart","No built-in secrets management — credentials are stored as plaintext environment variables (requires external secrets manager)","Config file format is not standardized — YAML and JSON support may diverge","No configuration versioning — no audit trail of configuration changes"],"requires":["Python 3.8+","Environment variables set in shell, Docker, or Kubernetes","Optional config file (YAML or JSON) in working directory or specified path","Configuration schema documentation (if provided)"],"input_types":["environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, TOR_PROXY_ADDRESS, etc.)","config file (YAML or JSON) with keys: model, timeout, concurrency, search_engines, etc."],"output_types":["parsed configuration dict used throughout Robin","validation errors if configuration is invalid"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_2","uri":"capability://planning.reasoning.llm.based.intelligent.result.filtering.with.relevance.scoring","name":"llm-based intelligent result filtering with relevance scoring","description":"Filters dark web search results using LLM-powered relevance scoring implemented in llm.py:filter_results(). The system constructs a prompt containing the original investigation query and candidate search results, then uses the LLM to score each result's relevance to the investigation objective. Results are ranked by LLM-assigned relevance scores and filtered to retain only high-confidence matches, reducing noise from off-topic .onion pages. This approach captures semantic relevance beyond keyword matching — e.g., identifying a marketplace listing as relevant to 'ransomware payment tracking' even if it doesn't contain the exact phrase.","intents":["I want to automatically filter out irrelevant dark web search results without manual review of hundreds of pages","I need to identify which search results are most likely to contain actionable threat intelligence for my investigation","I want to prioritize scraping resources by focusing on high-relevance pages first, given limited bandwidth/time"],"best_for":["threat intelligence analysts processing large result sets (100+ pages) from dark web searches","automated OSINT pipelines that need to reduce downstream scraping load by filtering early","investigators with limited time who need to focus on the most relevant 10-20% of results"],"limitations":["LLM filtering adds 3-8 second latency per batch of results due to API round-trip and token processing","Relevance scoring is subjective and model-dependent — GPT-4 may score differently than Claude or Ollama 7B","Batch size is limited by LLM context window (4K-8K tokens) — cannot efficiently filter 500+ results in a single call","False negatives are possible — LLM may filter out relevant pages if the query is ambiguous or the page uses unconventional terminology","No explainability — LLM does not provide reasoning for relevance scores, making it difficult to debug false positives/negatives"],"requires":["Python 3.8+","LLM provider credentials (OpenAI, Anthropic, Google, or local Ollama)","llm.py module with filter_results() function","Original investigation query string","List of search results with URL, title, and optional snippet"],"input_types":["investigation query string","list of search result objects (url, title, snippet)","optional relevance threshold (0.0-1.0)"],"output_types":["filtered list of search results ranked by relevance score","relevance scores per result (float 0.0-1.0)","optional reasoning/explanation per result"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_3","uri":"capability://data.processing.analysis.tor.routed.anonymous.content.scraping.from.onion.sites","name":"tor-routed anonymous content scraping from .onion sites","description":"Extracts HTML content from dark web .onion sites by routing HTTP requests through a Tor SOCKS5 proxy (127.0.0.1:9050) implemented in scrape.py:scrape_multiple(). The system uses a thread-pooled architecture to scrape multiple URLs concurrently with per-request timeout protection (default 30 seconds) to prevent hanging on slow/offline sites. Responses are parsed with BeautifulSoup to extract text content, and failures (connection timeouts, 404s, Tor circuit failures) are gracefully handled with fallback retry logic. The implementation maintains request anonymity by routing all HTTP traffic through Tor and rotating user agents to avoid fingerprinting.","intents":["I want to extract full page content from .onion marketplace listings without exposing my IP address","I need to scrape 50+ dark web pages in parallel to accelerate threat intelligence gathering","I want to handle scraping failures gracefully — if one .onion site is down, continue scraping others without blocking"],"best_for":["OSINT investigators extracting marketplace listings, forum posts, and leaked data from .onion sites","threat intelligence teams automating large-scale dark web content harvesting for analysis","security researchers studying dark web marketplace dynamics and pricing patterns"],"limitations":["Tor routing adds 2-10 second latency per request due to circuit establishment and exit node selection","Concurrent scraping may trigger rate-limiting or IP bans from .onion sites if too aggressive (recommend max 5-10 concurrent threads)","JavaScript-heavy .onion sites cannot be scraped — only static HTML is extracted; dynamic content requires Selenium/Playwright","Tor circuit failures are unpredictable — some requests may fail even with retry logic if Tor network is congested","Extracted text is unstructured — no automatic parsing of marketplace fields (price, seller, date) without additional NLP/regex","No built-in handling of CAPTCHA or Cloudflare protection — sites with bot detection will fail silently"],"requires":["Python 3.8+","Tor service running on 127.0.0.1:9050 (SOCKS5 proxy)","requests library with PySocks for SOCKS5 support","BeautifulSoup4 for HTML parsing","scrape.py module with scrape_multiple() function","Network connectivity to Tor network"],"input_types":["list of .onion URLs to scrape","optional timeout per request (seconds)","optional concurrency level (number of threads)"],"output_types":["list of dicts with keys: url (str), content (str), status_code (int), error (str optional)","raw HTML content from successful requests","error messages for failed requests"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_4","uri":"capability://text.generation.language.structured.osint.report.generation.from.raw.dark.web.content","name":"structured osint report generation from raw dark web content","description":"Synthesizes raw scraped content, search results, and metadata into structured intelligence reports using LLM-powered summarization implemented in llm.py:generate_summary(). The system constructs a prompt containing the investigation query, filtered search results, and scraped page content, then uses the LLM to extract key findings, identify threat indicators (IOCs), and organize information into a structured report with sections like 'Threat Overview', 'Key Findings', 'Indicators of Compromise', and 'Recommendations'. The report is formatted as JSON or markdown for downstream consumption by SIEM systems, threat intelligence platforms, or human analysts.","intents":["I want to automatically generate a structured threat intelligence report from 20+ dark web pages without manual synthesis","I need to extract IOCs (IP addresses, domains, hashes) from unstructured dark web content and organize them by type","I want to produce a markdown/JSON report that can be directly imported into my threat intelligence platform"],"best_for":["threat intelligence teams automating report generation from dark web investigations","SOC analysts needing quick summaries of dark web findings for incident response","security researchers publishing dark web threat landscape reports"],"limitations":["LLM summarization adds 5-15 second latency due to processing large content batches","Report quality depends on LLM model capability — smaller models (Ollama 7B) produce less structured/actionable reports","IOC extraction is not guaranteed to be complete — LLM may miss indicators if they're embedded in unusual formats or obfuscated","No validation of extracted IOCs — LLM may hallucinate indicators that don't actually exist in the source content","Report structure is fixed — customization requires modifying the prompt template in llm.py","Context window limits prevent processing very large content batches (>50 pages) in a single call"],"requires":["Python 3.8+","LLM provider credentials (OpenAI, Anthropic, Google, or local Ollama)","llm.py module with generate_summary() function","Scraped content from dark web pages","Original investigation query for context"],"input_types":["investigation query string","list of scraped page content (text)","optional metadata (URLs, timestamps, sources)"],"output_types":["structured JSON report with sections: threat_overview, key_findings, iocs, recommendations","markdown-formatted report for human review","extracted IOCs as structured list (ips, domains, hashes, etc.)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_5","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.unified.interface","name":"multi-provider llm abstraction with unified interface","description":"Provides a pluggable abstraction layer for multiple LLM providers (OpenAI, Anthropic, Google, Ollama) implemented in llm_utils.py:get_llm(). The system uses a factory pattern to instantiate the appropriate LLM client based on environment variables or configuration, enabling seamless provider switching without modifying downstream code. Each provider is wrapped with a consistent interface supporting streaming responses, token counting, and error handling. Configuration is managed through environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.) and a config file, allowing users to specify model selection, temperature, and max tokens per provider.","intents":["I want to switch between OpenAI, Anthropic, and Ollama without rewriting my investigation code","I need to compare query refinement quality across different LLM models (GPT-4 vs Claude vs Llama)","I want to run Robin on-premises using Ollama without sending data to cloud LLM providers"],"best_for":["OSINT teams evaluating different LLM providers for cost/quality tradeoffs","organizations with data residency requirements needing on-premises LLM execution (Ollama)","developers extending Robin with custom LLM providers"],"limitations":["Provider-specific features are not exposed — advanced features (function calling, vision) are abstracted away","Error handling is generic — provider-specific errors (rate limits, quota exceeded) are normalized, losing diagnostic detail","Token counting is approximate — different providers count tokens differently, leading to inconsistent cost estimates","Streaming responses are not fully supported across all providers — some providers require buffering entire response","No built-in fallback logic — if primary provider fails, investigation halts rather than automatically switching to backup provider"],"requires":["Python 3.8+","At least one LLM provider configured: OpenAI API key, Anthropic API key, Google API key, or local Ollama instance","llm_utils.py module with get_llm() function","Configuration file or environment variables specifying provider and model"],"input_types":["provider name string (openai, anthropic, google, ollama)","model name string (gpt-4, claude-3-opus, gemini-pro, llama2, etc.)","optional configuration dict with temperature, max_tokens, etc."],"output_types":["LLM client instance with unified interface","provider-specific metadata (model name, context window, cost per token)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_6","uri":"capability://automation.workflow.six.stage.investigation.pipeline.orchestration","name":"six-stage investigation pipeline orchestration","description":"Orchestrates a complete dark web OSINT investigation workflow through a six-stage pipeline implemented in main.py:cli(). The pipeline sequentially executes: (1) LLM initialization, (2) query refinement, (3) multi-engine search, (4) result filtering, (5) content scraping, and (6) report generation. Each stage is implemented as a modular function with clear input/output contracts, enabling easy insertion of custom stages or modification of existing ones. The orchestration layer handles error propagation, logging, and progress reporting across stages, with optional checkpointing to resume interrupted investigations.","intents":["I want to run a complete dark web investigation from query to report with a single command","I need to automate the entire OSINT workflow without manually chaining together search, scraping, and analysis steps","I want to insert custom processing logic (e.g., custom filtering, external API calls) into the investigation pipeline"],"best_for":["OSINT teams automating repetitive investigation workflows","threat intelligence platforms integrating Robin as a dark web data source","developers building custom OSINT tools on top of Robin's pipeline"],"limitations":["Pipeline is sequential — stages cannot run in parallel, limiting throughput for large-scale investigations","No built-in checkpointing — if a stage fails, the entire investigation must be restarted from the beginning","Stage outputs are tightly coupled — modifying one stage's output format may break downstream stages","Error handling is basic — failures in one stage halt the entire pipeline rather than gracefully degrading","No built-in monitoring/alerting — long-running investigations have no visibility into progress or ETA"],"requires":["Python 3.8+","All prerequisites for individual stages: Tor service, LLM provider credentials, search engine access","main.py module with cli() function","All dependency modules: llm.py, search.py, scrape.py, llm_utils.py"],"input_types":["investigation query string","optional model selection (openai, anthropic, google, ollama)","optional configuration overrides (timeout, concurrency, relevance threshold)"],"output_types":["structured OSINT report (JSON or markdown)","intermediate artifacts (refined queries, search results, scraped content)","execution logs with timing and error information"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_7","uri":"capability://automation.workflow.dual.mode.interface.cli.and.streamlit.web.ui","name":"dual-mode interface: cli and streamlit web ui","description":"Exposes the investigation pipeline through two distinct interfaces: command-line (main.py:cli()) for automation and scripting, and Streamlit web UI (ui.py) for interactive exploration. The CLI mode accepts query and model arguments, executes the investigation pipeline, and outputs results to stdout/files. The Streamlit UI provides a web dashboard with form inputs for query/model selection, real-time progress updates, and interactive result visualization. Both interfaces share the same underlying pipeline implementation, ensuring consistency while accommodating different user workflows (batch automation vs. interactive investigation).","intents":["I want to run dark web investigations from the command line and integrate them into my automation scripts","I need a web interface for non-technical analysts to run investigations without command-line knowledge","I want to visualize dark web investigation results in an interactive dashboard"],"best_for":["DevOps teams integrating Robin into CI/CD pipelines and automation workflows","SOC analysts using a web UI for interactive threat investigation","organizations with mixed technical/non-technical users needing both CLI and web access"],"limitations":["CLI and web UI are separate entry points — no unified session management across interfaces","Streamlit UI is single-threaded — concurrent investigations from multiple users will queue sequentially","Web UI does not persist investigation history — results are lost on page refresh unless explicitly saved","CLI output is text-based — no built-in visualization of results (requires piping to external tools)","No authentication/authorization in web UI — anyone with network access can run investigations"],"requires":["Python 3.8+","For CLI: main.py module","For web UI: Streamlit library (pip install streamlit), ui.py module","All prerequisites for investigation pipeline (Tor, LLM provider, etc.)"],"input_types":["CLI: command-line arguments (query, model, optional config)","Web UI: form inputs (query text field, model dropdown, optional advanced settings)"],"output_types":["CLI: stdout text, JSON files, markdown reports","Web UI: interactive Streamlit components (text, tables, expandable sections)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_8","uri":"capability://tool.use.integration.tor.socks5.proxy.integration.for.anonymous.network.access","name":"tor socks5 proxy integration for anonymous network access","description":"Routes all HTTP traffic (search queries, content scraping) through a Tor SOCKS5 proxy running on 127.0.0.1:9050, implemented at the HTTP client level using the requests library with PySocks support. The system configures the Tor proxy globally for all outbound requests, ensuring that search engine queries and .onion site scraping are anonymized. Tor circuit failures are handled with retry logic, and user agents are rotated to avoid fingerprinting. The implementation assumes a local Tor service is running (typically via Docker or system package) and does not manage Tor lifecycle.","intents":["I want to query dark web search engines without exposing my IP address","I need to scrape .onion sites anonymously to avoid detection and blocking","I want to ensure all Robin traffic is routed through Tor for operational security"],"best_for":["OSINT investigators conducting sensitive dark web investigations requiring anonymity","threat intelligence teams operating in jurisdictions with restricted internet access","security researchers studying dark web without exposing their identity"],"limitations":["Tor routing adds 2-10 second latency per request due to circuit establishment","Tor exit nodes may be rate-limited or blocked by some dark web sites, causing request failures","Tor network congestion can cause unpredictable latency spikes and circuit failures","No built-in Tor lifecycle management — requires external Tor service (Docker, system package, etc.)","Tor circuit failures are not automatically recovered — requires manual intervention or external monitoring","No built-in support for Tor bridges — cannot bypass ISP-level Tor blocking"],"requires":["Python 3.8+","Tor service running on 127.0.0.1:9050 (SOCKS5 proxy)","requests library with PySocks support (pip install requests[socks])","Network connectivity to Tor network","Configuration of Tor SOCKS5 proxy address in Robin config or environment variables"],"input_types":["HTTP requests (implicitly routed through Tor)","optional Tor proxy address (default 127.0.0.1:9050)"],"output_types":["HTTP responses from .onion sites and dark web search engines","error messages for Tor circuit failures"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-apurvsinghgautam--robin__cap_9","uri":"capability://automation.workflow.docker.containerization.with.tor.service.bundling","name":"docker containerization with tor service bundling","description":"Packages Robin and its dependencies (Python, requests, BeautifulSoup, Streamlit) into a Docker image with an integrated Tor service, enabling single-command deployment without manual dependency installation. The Dockerfile installs Tor, configures SOCKS5 proxy on 127.0.0.1:9050, and starts both Tor and Robin services on container startup. Environment variables for LLM provider credentials are passed at runtime, allowing users to deploy the container without modifying the image. The Docker Compose configuration (if provided) orchestrates the Robin container with optional additional services (e.g., Redis for caching, PostgreSQL for result storage).","intents":["I want to deploy Robin with a single docker run command without installing Python, Tor, and dependencies manually","I need to run Robin in a containerized environment for isolation and reproducibility","I want to deploy Robin to cloud platforms (AWS, GCP, Azure) using Docker images"],"best_for":["DevOps teams deploying Robin to Kubernetes or Docker Swarm clusters","organizations with containerization requirements for security/compliance","developers testing Robin in isolated environments without affecting system dependencies"],"limitations":["Docker image size is large (500MB+) due to Tor and Python dependencies, increasing deployment time","Tor service inside container may have reduced anonymity compared to system-level Tor (shared exit nodes across containers)","No built-in health checks — container may appear running but Tor service may be offline","Environment variable injection is required for LLM credentials — no built-in secrets management","Docker networking may interfere with Tor circuit establishment if misconfigured"],"requires":["Docker 20.10+ or Docker Desktop","Docker Compose 1.29+ (optional, for multi-container deployments)","LLM provider credentials passed as environment variables at runtime","Network connectivity from container to Tor network and LLM provider endpoints"],"input_types":["Docker image (apurvsinghgautam/robin or self-built)","environment variables for LLM credentials (OPENAI_API_KEY, ANTHROPIC_API_KEY, etc.)","optional volume mounts for configuration files or result storage"],"output_types":["running Docker container with Robin CLI/web UI accessible","container logs with investigation progress and errors"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","API credentials for at least one LLM provider (OpenAI API key, Anthropic API key, Google API key, or local Ollama instance on localhost:11434)","llm.py module with refine_query() function","Network connectivity to LLM provider endpoints or local Ollama service","Tor service running on 127.0.0.1:9050 (SOCKS5 proxy)","search.py module with get_search_results() function","requests library configured with Tor proxy","Network connectivity to at least 2-3 dark web search engines (Torch, Ahmia, Candle, etc.)","Environment variables set in shell, Docker, or Kubernetes","Optional config file (YAML or JSON) in working directory or specified path"],"failure_modes":["LLM-based refinement adds 2-5 second latency per query due to API round-trip","Query expansion quality depends on LLM model capability; smaller models (Ollama 7B) produce less sophisticated synonyms than GPT-4","No caching of refined queries — identical raw queries trigger redundant LLM calls if not deduplicated upstream","Search engine availability is unpredictable — individual .onion search engines may be offline or rate-limited, causing partial result loss","Concurrent requests to multiple search engines increase Tor exit node load and may trigger rate-limiting or IP bans","Result normalization is lossy — search engine-specific metadata (relevance scores, date indexed) is discarded during aggregation","No built-in deduplication of URLs across engines — requires downstream processing to identify mirror pages","No built-in validation of configuration values — invalid settings may cause runtime errors","Configuration is loaded once at startup — changes require container restart","No built-in secrets management — credentials are stored as plaintext environment variables (requires external secrets manager)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6098449002064114,"quality":0.32,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.75,"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":"active","updated_at":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:13.677Z","last_commit":"2026-03-31T19:31:21Z"},"community":{"stars":4939,"forks":966,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=apurvsinghgautam--robin","compare_url":"https://unfragile.ai/compare?artifact=apurvsinghgautam--robin"}},"signature":"XTNBMs9g5v+UvWzu2bmWP3HCutCMD+FyihWmQRwUYudChkesyR3uJsWQ6HUgW6Tk9R5tiySmvxYoJ+1k8bZ8Bw==","signedAt":"2026-06-20T16:29:47.432Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/apurvsinghgautam--robin","artifact":"https://unfragile.ai/apurvsinghgautam--robin","verify":"https://unfragile.ai/api/v1/verify?slug=apurvsinghgautam--robin","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"}}