{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-kaymen99--upwork-ai-jobs-applier","slug":"kaymen99--upwork-ai-jobs-applier","name":"Upwork-AI-jobs-applier","type":"agent","url":"https://github.com/kaymen99/Upwork-AI-jobs-applier","page_url":"https://unfragile.ai/kaymen99--upwork-ai-jobs-applier","categories":["ai-agents"],"tags":["ai-agents","ai-automation","langchain","langgraph","llm-agent","llm-scraper","playwright","scraping","upwork","upwork-automation","upwork-jobs-scraping","upwork-scraper"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_0","uri":"capability://search.retrieval.upwork.job.listing.scraping.with.browser.automation","name":"upwork job listing scraping with browser automation","description":"Extracts job listings from Upwork search results using Playwright-based browser automation that navigates the DOM, handles dynamic content loading, and parses structured job metadata (title, description, budget, client history, skills required). The UpworkJobScraper class in src/scraper.py manages headless browser sessions, implements retry logic for network failures, and extracts job details into structured Pydantic models for downstream processing.","intents":["I need to automatically discover new Upwork job postings matching my skills without manual browsing","I want to extract job metadata (budget, client rating, required skills) at scale for analysis","I need to monitor Upwork for specific job categories and capture them before competitors apply"],"best_for":["Freelancers automating their job search workflow","Agencies building internal job discovery pipelines","Researchers analyzing Upwork job market trends"],"limitations":["Playwright browser automation adds 2-5 seconds per page load; not suitable for real-time streaming","Upwork's dynamic JavaScript rendering requires full browser context; cannot use lightweight HTTP clients","Rate limiting and IP blocking risk if scraping too aggressively without delays between requests","Requires maintaining CSS selectors as Upwork UI changes; brittle to DOM structure updates"],"requires":["Python 3.9+","Playwright 1.48.0+","Upwork account credentials (email/password or session token)","Sufficient system resources for headless browser (300MB+ RAM per instance)"],"input_types":["Upwork search URL or query parameters","Job category filters (e.g., 'Python', 'Web Development')","Pagination parameters"],"output_types":["Structured job objects (Pydantic models)","JSON serialized job listings","SQLite database records"],"categories":["search-retrieval","web-scraping"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_1","uri":"capability://planning.reasoning.ai.powered.job.scoring.and.qualification.filtering","name":"ai-powered job scoring and qualification filtering","description":"Evaluates scraped job listings against user profile using an LLM-based scoring system that analyzes skills match, budget alignment, client history, and project complexity. The score_jobs_batch node in src/nodes.py orchestrates batch processing through LangChain LLM calls with structured output parsing (Pydantic), filters jobs with scores ≥7/10, and persists qualified jobs to SQLite. Uses multi-provider LLM support (OpenAI, Google, Groq, Anthropic) via a provider factory pattern.","intents":["I want to automatically filter Upwork jobs that match my skills and budget requirements without manual review","I need to rank jobs by fit quality so I focus on highest-probability opportunities","I want to avoid low-quality or scam jobs by evaluating client reputation and project red flags"],"best_for":["Freelancers with diverse skill sets wanting intelligent job filtering","Agencies managing multiple freelancer profiles with different specializations","High-volume job applicants needing to prioritize limited application slots"],"limitations":["LLM scoring adds 1-3 seconds per job; batch processing required to stay within rate limits","Scoring quality depends on user profile completeness; sparse profiles produce unreliable matches","Threshold of 7/10 is arbitrary; requires tuning per user profile and market conditions","LLM hallucinations possible when evaluating ambiguous job descriptions; no human-in-the-loop validation"],"requires":["LLM API key (OpenAI, Google, Groq, or Anthropic)","User profile data (skills, experience, hourly rate, portfolio)","SQLite database for job persistence","LangChain 0.3.7+ and LangGraph 0.2.45+"],"input_types":["Structured job objects from scraper","User profile (skills array, experience level, rate, certifications)","Scoring criteria weights (optional)"],"output_types":["Scored job objects with numeric score (0-10)","Filtered job list (score ≥7)","Scoring rationale/explanation per job"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_10","uri":"capability://automation.workflow.langsmith.integration.for.workflow.observability.and.debugging","name":"langsmith integration for workflow observability and debugging","description":"Integrates LangSmith cloud-based monitoring platform to trace AI agent interactions, log LLM calls, and debug workflow failures. Environment configuration (.env.example) includes LANGSMITH_API_KEY and LANGSMITH_PROJECT settings; when enabled, all LLM calls, node executions, and state transitions are logged to LangSmith dashboard for analysis. Enables visualization of workflow DAG execution, token usage tracking, and error diagnosis without code instrumentation.","intents":["I want to monitor LLM API usage and costs across the entire workflow","I need to debug why specific jobs are failing or producing low scores","I want to visualize the workflow execution and identify bottlenecks"],"best_for":["Teams running production job automation systems requiring observability","Developers debugging workflow issues and LLM response quality","Users wanting to track API costs and optimize LLM usage"],"limitations":["LangSmith requires cloud account and API key; adds external dependency","Logging all LLM calls to cloud may have privacy implications (job descriptions, user profiles sent to LangSmith)","LangSmith dashboard has learning curve; requires familiarity with LangChain/LangGraph concepts","Tracing adds latency (~100-200ms per LLM call) due to network overhead"],"requires":["LangSmith account (free tier available)","LANGSMITH_API_KEY environment variable","LANGSMITH_PROJECT name configured","Network access to LangSmith API (api.smith.langchain.com)"],"input_types":["LLM calls and responses","Node execution events","State transitions"],"output_types":["LangSmith trace logs (visible in cloud dashboard)","Token usage metrics","Error logs and stack traces"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_11","uri":"capability://text.generation.language.markdown.based.output.generation.and.file.persistence","name":"markdown-based output generation and file persistence","description":"Generates human-readable markdown files for each processed job containing cover letter, interview preparation guide, and job metadata. The system writes separate markdown files to output directory (configurable path) with structured sections (Job Summary, Cover Letter, Interview Prep, Talking Points), enabling users to review and edit generated content before submission. Files are named by job ID and timestamp for easy organization and version tracking.","intents":["I want to review generated cover letters and interview prep before submitting to Upwork","I need to edit and customize generated content while preserving the original","I want to organize job applications by date and job ID for easy reference"],"best_for":["Users wanting to review and edit AI-generated content before submission","Freelancers maintaining a portfolio of applications for future reference","Teams collaborating on job applications (sharing markdown files for review)"],"limitations":["Markdown files are static; no automatic sync with Upwork submissions","File organization by job ID requires manual tracking; no built-in search or indexing","Large markdown files (10+ jobs) may be unwieldy for editing; no built-in editor integration","No version control; overwriting files loses previous versions unless backed up"],"requires":["Write access to filesystem for output directory","Markdown editor (VS Code, Obsidian, etc.) for reviewing content","Optional: Git for version control of markdown files"],"input_types":["Job object with metadata","Generated cover letter (text)","Generated interview preparation (text)"],"output_types":["Markdown file (.md) with structured sections","File path and naming convention (job_id_timestamp.md)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_12","uri":"capability://memory.knowledge.user.profile.configuration.and.skill.matching","name":"user profile configuration and skill matching","description":"Manages user profile data (skills, experience level, hourly rate, portfolio links, certifications) through configuration files or environment variables, enabling the system to match jobs against freelancer qualifications. The user profile is loaded at startup and used throughout the workflow for job scoring, cover letter personalization, and interview preparation. Supports multiple profile formats (JSON, YAML, environment variables) for flexibility.","intents":["I want to define my skills and experience once, then use it for all job matching and personalization","I need to update my profile (add new skills, change rate) without modifying code","I want the system to match jobs based on my specific expertise and experience level"],"best_for":["Freelancers with diverse skill sets wanting to match jobs accurately","Teams managing multiple freelancer profiles with different specializations","Users wanting to test different profiles (junior vs senior rates) without code changes"],"limitations":["Profile accuracy depends on user input; incomplete or outdated profiles produce poor job matches","No built-in validation of profile data; typos or incorrect skill names may not match job requirements","Profile changes require manual updates; no automatic skill discovery from portfolio or past projects","No support for dynamic skill levels (e.g., 'Python: 5 years') in matching; only binary skill presence"],"requires":["User profile configuration file (JSON, YAML, or environment variables)","Skills array with relevant technologies and expertise areas","Experience level (junior, mid, senior) or hourly rate"],"input_types":["Profile configuration (JSON/YAML file or environment variables)","Skills array (e.g., ['Python', 'Django', 'PostgreSQL'])","Experience metadata (years of experience, certifications, portfolio links)"],"output_types":["Loaded user profile object (Pydantic model)","Used for job scoring and personalization throughout workflow"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_2","uri":"capability://text.generation.language.personalized.cover.letter.generation.with.keyword.optimization","name":"personalized cover letter generation with keyword optimization","description":"Generates customized cover letters for qualified jobs using LLM-based text generation that incorporates job description keywords, user skills, relevant experience, and client-specific context. The generate_cover_letter subgraph node in src/nodes.py constructs prompts that reference the job posting, user profile, and previous successful proposals, then uses structured LLM output to produce markdown-formatted cover letters optimized for Upwork's proposal system. Results are persisted to markdown files and database.","intents":["I want to generate personalized cover letters for each job without manual writing","I need cover letters that incorporate job-specific keywords to improve proposal ranking","I want to maintain consistency in tone and professionalism across multiple applications"],"best_for":["Freelancers applying to 10+ jobs per day who need speed without sacrificing personalization","Non-native English speakers wanting AI-assisted proposal writing","Agencies managing applications for multiple freelancer profiles"],"limitations":["Generated cover letters may lack authentic voice or personal anecdotes; require human review/editing","LLM may over-optimize for keywords, producing awkward phrasing or keyword stuffing","Cannot access freelancer's previous successful proposals without explicit input; relies on user profile","Upwork's proposal character limits (5000 chars) may truncate longer generated content"],"requires":["LLM API key (OpenAI, Google, Groq, or Anthropic)","User profile with skills, experience summary, and portfolio links","Structured job object with description and requirements","LangChain 0.3.7+ for prompt templating"],"input_types":["Qualified job object (from scoring stage)","User profile (skills, experience, portfolio)","Optional: previous successful proposal examples"],"output_types":["Markdown-formatted cover letter","Plain text proposal","Structured metadata (word count, keyword density)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_3","uri":"capability://text.generation.language.interview.preparation.material.generation","name":"interview preparation material generation","description":"Generates interview talking points, potential questions, and discussion strategies for qualified jobs using LLM analysis of job description, client profile, and user expertise. The generate_interview_preparation subgraph node creates markdown documents with anticipated client questions, suggested answers referencing user experience, project discussion points, and rate negotiation strategies. Outputs are stored as markdown files and database records for reference during client calls.","intents":["I want to prepare for client interviews without manually researching each job posting","I need anticipated questions and talking points to discuss my relevant experience","I want strategies for discussing project scope, timeline, and rate with potential clients"],"best_for":["Freelancers preparing for initial client discovery calls","High-value project applicants wanting structured interview prep","Non-native English speakers wanting conversation frameworks"],"limitations":["Generated questions may not match actual client interview style or priorities","Talking points are generic; require personalization based on freelancer's actual experience","Cannot predict client-specific negotiation tactics or budget constraints","Interview preparation is only useful if freelancer actually gets interview; no guarantee of interview scheduling"],"requires":["LLM API key (OpenAI, Google, Groq, or Anthropic)","User profile with experience, past projects, and expertise areas","Structured job object with description and client information","LangChain 0.3.7+ for prompt templating"],"input_types":["Qualified job object (from scoring stage)","User profile (experience, past projects, expertise)","Optional: user's previous interview notes or outcomes"],"output_types":["Markdown document with interview preparation guide","Structured Q&A pairs (question, suggested answer)","Discussion points and negotiation strategies"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_4","uri":"capability://automation.workflow.langgraph.based.workflow.orchestration.with.state.management","name":"langgraph-based workflow orchestration with state management","description":"Orchestrates the entire job application pipeline using LangGraph's state machine pattern, where src/graph.py defines a directed acyclic graph (DAG) of processing nodes (scraping, scoring, cover letter generation, interview prep) with explicit state transitions and conditional routing. The UpworkAutomation class manages a TypedDict-based state object (src/state.py) that flows through nodes, persisting intermediate results and enabling resumable execution. Supports parallel batch processing and integrates LangSmith for observability.","intents":["I want to automate the entire job application workflow from discovery to preparation in a single orchestrated process","I need to resume interrupted workflows without reprocessing completed jobs","I want visibility into each workflow step and ability to debug failures"],"best_for":["Teams building production job automation systems requiring reliability and observability","Developers extending the workflow with custom nodes or conditional logic","Users wanting to run the system continuously with monitoring and error recovery"],"limitations":["LangGraph state machine adds complexity; requires understanding of DAG patterns and state management","State object must fit in memory; large job batches (1000+) may require pagination or streaming","Conditional routing logic is hardcoded; dynamic workflow changes require code modifications","LangSmith integration requires cloud account and API key; adds external dependency for observability"],"requires":["LangGraph 0.2.45+","LangChain 0.3.7+","Python 3.9+","SQLite3 for state persistence","Optional: LangSmith API key for monitoring"],"input_types":["User profile configuration (skills, experience, rate)","Job search parameters (category, budget range, keywords)","LLM provider credentials"],"output_types":["Processed job batch with scores, cover letters, and interview prep","SQLite database with job records and application history","Markdown files with generated content","LangSmith trace logs (if enabled)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_5","uri":"capability://data.processing.analysis.sqlite.based.job.deduplication.and.persistence","name":"sqlite-based job deduplication and persistence","description":"Maintains a SQLite database (src/database.py) that stores processed jobs, application history, and generated content to prevent reprocessing and enable historical analysis. The check_jobs_against_db node queries the database before scoring to identify previously seen jobs, stores qualified jobs with metadata (score, timestamp, cover letter, interview prep), and supports queries for analytics (jobs applied, success rate, average score). Database schema includes job_listings, applications, and metadata tables with indexed lookups.","intents":["I want to avoid applying to the same job twice across multiple workflow runs","I need to track which jobs I've applied to and their outcomes for analytics","I want to maintain a searchable history of generated cover letters and interview prep for future reference"],"best_for":["Freelancers running the automation system continuously or daily","Teams analyzing job application success rates and patterns","Users wanting to review and edit generated content before submission"],"limitations":["SQLite is single-writer; concurrent writes from multiple processes will cause contention","Database queries add ~50-100ms per job lookup; not suitable for real-time streaming","No built-in backup or replication; data loss risk if database file is corrupted or deleted","Schema migrations required if workflow changes (e.g., adding new fields); no automatic schema evolution"],"requires":["Python 3.9+ (SQLite3 built-in)","Write access to filesystem for database file","Optional: database migration tool (Alembic) for schema management"],"input_types":["Job object (title, URL, job_id)","Application metadata (score, cover letter, interview prep, timestamp)"],"output_types":["Boolean: job already processed (True/False)","Query results: list of previously applied jobs, success metrics","Exported data: CSV or JSON dump of application history"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_6","uri":"capability://tool.use.integration.multi.provider.llm.abstraction.with.runtime.provider.switching","name":"multi-provider llm abstraction with runtime provider switching","description":"Provides a provider factory pattern (src/utils.py) that abstracts LLM interactions across OpenAI, Google Generative AI, Groq, and Anthropic, allowing runtime provider selection via environment variables without code changes. Each provider is wrapped in a consistent interface that handles API authentication, request formatting, structured output parsing (via Pydantic), and error handling. Supports provider-specific features (e.g., Groq's fast inference, Anthropic's extended context) while maintaining API compatibility.","intents":["I want to switch between LLM providers (OpenAI, Google, Groq, Anthropic) without modifying code","I need cost optimization by selecting cheaper providers (Groq) for high-volume tasks","I want redundancy by falling back to alternative providers if primary provider is unavailable"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Users wanting to avoid vendor lock-in with a single LLM provider","Developers building extensible AI automation systems"],"limitations":["Provider APIs have different rate limits, context windows, and model availability; not all models support structured output","Prompt engineering may require tuning per provider (e.g., Anthropic's system prompts vs OpenAI's role-based)","Fallback logic not implemented; if primary provider fails, workflow stops (no automatic retry with alternative provider)","Cost tracking and billing are provider-specific; no unified cost monitoring across providers"],"requires":["LangChain 0.3.7+","API keys for at least one provider (OpenAI, Google, Groq, or Anthropic)","Environment variables configured (.env file with PROVIDER, API_KEY, etc.)","Python 3.9+"],"input_types":["Provider name (openai, google, groq, anthropic)","API key and configuration (model name, temperature, max_tokens)","Prompt and input data"],"output_types":["LLM response (text or structured Pydantic object)","Provider metadata (model used, tokens consumed, latency)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_7","uri":"capability://automation.workflow.batch.job.processing.with.parallel.execution.and.rate.limiting","name":"batch job processing with parallel execution and rate limiting","description":"Processes multiple jobs in parallel batches using LangGraph's batch execution capabilities while respecting API rate limits through configurable delays and batch sizes. The score_jobs_batch node groups jobs into batches, submits them to the LLM in parallel (up to N concurrent requests), and implements exponential backoff for rate limit errors. Batch size and concurrency are configurable via environment variables to balance speed vs. API quota consumption.","intents":["I want to process 100+ jobs per day without hitting LLM API rate limits","I need to maximize throughput by parallelizing independent scoring/generation tasks","I want to gracefully handle rate limit errors without losing progress"],"best_for":["High-volume job applicants processing 50+ jobs per run","Teams running the automation on a schedule (daily/hourly) with predictable job volumes","Cost-conscious users wanting to optimize API usage through batching"],"limitations":["Batch processing adds latency; cannot process single jobs in real-time (minimum batch size overhead)","Exponential backoff can cause long delays if rate limits are hit; no predictive rate limit management","Parallel execution requires sufficient system resources (CPU, memory); too many concurrent requests may crash","Batch size tuning is manual; no automatic optimization based on API quota or historical rate limit patterns"],"requires":["LangGraph 0.2.45+","LLM API key with sufficient quota for batch processing","Environment variables: BATCH_SIZE, CONCURRENCY_LIMIT, RATE_LIMIT_DELAY","Python 3.9+ with async support"],"input_types":["List of job objects to process","Batch size (e.g., 10 jobs per batch)","Concurrency limit (e.g., 5 parallel requests)"],"output_types":["Processed job batch with scores, cover letters, interview prep","Rate limit metadata (requests made, quota remaining, backoff delays)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_8","uri":"capability://data.processing.analysis.structured.output.parsing.with.pydantic.validation","name":"structured output parsing with pydantic validation","description":"Enforces type safety and consistent data structures across the workflow using Pydantic models (v2.9.2) for job objects, user profiles, scores, and generated content. LLM responses are parsed into Pydantic models with validation, ensuring required fields are present and types are correct. If parsing fails, the system retries with a corrected prompt or falls back to manual review. Models are defined in src/models.py and used throughout the pipeline for type hints and runtime validation.","intents":["I want to ensure LLM outputs are consistently structured and validated before downstream processing","I need type safety across the workflow to catch data errors early","I want to serialize/deserialize job data to JSON and database without manual conversion"],"best_for":["Teams building production systems requiring data quality guarantees","Developers extending the workflow with custom nodes that depend on consistent data structures","Users wanting to export job data to external systems (CRM, spreadsheets)"],"limitations":["Pydantic validation adds ~50-100ms per object; not suitable for real-time streaming","LLM may fail to produce valid JSON/structured output; requires retry logic or fallback","Schema changes require code updates and potential data migration","Overly strict validation may reject valid but unexpected LLM outputs"],"requires":["Pydantic 2.9.2+","Python 3.9+","LLM that supports structured output (JSON mode or function calling)"],"input_types":["LLM response (JSON string or structured output)","Pydantic model class definition"],"output_types":["Validated Pydantic model instance","Serialized JSON or database record","Type-safe Python objects for downstream processing"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-kaymen99--upwork-ai-jobs-applier__cap_9","uri":"capability://automation.workflow.docker.containerization.with.environment.based.configuration","name":"docker containerization with environment-based configuration","description":"Packages the entire automation system as a Docker container (Dockerfile, .dockerignore) with environment-based configuration via .env file, enabling deployment to cloud platforms (AWS, GCP, Azure) or local Docker environments without code changes. Container includes all dependencies (Python, Playwright, LangChain, LangGraph), exposes configuration via environment variables, and supports volume mounts for database persistence and output files. Deployment documentation covers Docker setup, environment variable configuration, and cloud deployment patterns.","intents":["I want to deploy the automation system to a cloud platform (AWS, GCP, Azure) without managing dependencies","I need to run the system on a schedule (daily/hourly) using cloud job schedulers (Lambda, Cloud Run, Kubernetes)","I want to isolate the automation from my local machine and run it in a controlled environment"],"best_for":["Teams deploying to cloud platforms requiring containerization","Users wanting to run the system 24/7 without keeping a local machine on","DevOps engineers building CI/CD pipelines for job automation"],"limitations":["Docker image size is large (~500MB+) due to Playwright and browser dependencies; slow to pull and deploy","Headless browser automation in containers requires special configuration (X11, display server); may fail on some cloud platforms","Persistent database and output files require volume mounts or external storage; adds complexity to cloud deployment","Environment variable management can become unwieldy with many secrets (API keys); requires secure secret management (AWS Secrets Manager, etc.)"],"requires":["Docker 20.10+","Docker Compose (optional, for local multi-container setup)","Environment variables configured (.env file or cloud secret manager)","Cloud platform account (AWS, GCP, Azure) for deployment"],"input_types":["Dockerfile with base image and dependencies","Environment variables (LLM_PROVIDER, API_KEY, UPWORK_EMAIL, etc.)","Volume mounts for database and output files"],"output_types":["Docker image (built and pushed to registry)","Running container with job automation system","Logs and output files (persisted to volumes or external storage)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":38,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Playwright 1.48.0+","Upwork account credentials (email/password or session token)","Sufficient system resources for headless browser (300MB+ RAM per instance)","LLM API key (OpenAI, Google, Groq, or Anthropic)","User profile data (skills, experience, hourly rate, portfolio)","SQLite database for job persistence","LangChain 0.3.7+ and LangGraph 0.2.45+","LangSmith account (free tier available)","LANGSMITH_API_KEY environment variable"],"failure_modes":["Playwright browser automation adds 2-5 seconds per page load; not suitable for real-time streaming","Upwork's dynamic JavaScript rendering requires full browser context; cannot use lightweight HTTP clients","Rate limiting and IP blocking risk if scraping too aggressively without delays between requests","Requires maintaining CSS selectors as Upwork UI changes; brittle to DOM structure updates","LLM scoring adds 1-3 seconds per job; batch processing required to stay within rate limits","Scoring quality depends on user profile completeness; sparse profiles produce unreliable matches","Threshold of 7/10 is arbitrary; requires tuning per user profile and market conditions","LLM hallucinations possible when evaluating ambiguous job descriptions; no human-in-the-loop validation","LangSmith requires cloud account and API key; adds external dependency","Logging all LLM calls to cloud may have privacy implications (job descriptions, user profiles sent to LangSmith)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.2519103148518843,"quality":0.5,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.550Z","last_scraped_at":"2026-05-03T13:59:57.743Z","last_commit":"2025-05-01T18:38:32Z"},"community":{"stars":137,"forks":39,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=kaymen99--upwork-ai-jobs-applier","compare_url":"https://unfragile.ai/compare?artifact=kaymen99--upwork-ai-jobs-applier"}},"signature":"0WSXua049XtNKRwngl2MwQc2qyQ2MhX4uDolcs45u4CpFEvcGJ9g08TArIqr//KO13kNyqBnSON+vBeTi2teBQ==","signedAt":"2026-06-20T08:43:58.907Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/kaymen99--upwork-ai-jobs-applier","artifact":"https://unfragile.ai/kaymen99--upwork-ai-jobs-applier","verify":"https://unfragile.ai/api/v1/verify?slug=kaymen99--upwork-ai-jobs-applier","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"}}