{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_geniepm","slug":"geniepm","name":"GeniePM","type":"product","url":"https://genie.pm","page_url":"https://unfragile.ai/geniepm","categories":["app-builders"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_geniepm__cap_0","uri":"capability://text.generation.language.ai.driven.user.story.generation.from.requirements","name":"ai-driven user story generation from requirements","description":"Accepts high-level product requirements, epics, or feature descriptions and uses LLM-based generation to automatically produce structured user stories with standardized templates (As a [role], I want [feature], so that [benefit]). The system likely employs prompt engineering with domain-specific templates and acceptance criteria patterns to ensure consistency across generated stories, reducing manual template writing overhead by 60-80% for initial backlog creation.","intents":["I want to quickly convert a feature request into a properly formatted user story without manually writing the template","I need to generate 20+ user stories for a sprint in minutes instead of hours","I want AI to suggest acceptance criteria based on the story description so I don't start from a blank page"],"best_for":["Product managers and scrum masters managing backlogs with 50+ stories per quarter","Agile teams adopting AI-assisted workflows for the first time","Solo founders or small teams (2-5 people) without dedicated BA resources"],"limitations":["Generated stories often lack domain-specific nuance and require 20-40% manual refinement by domain experts","No context awareness across related stories—may generate duplicate acceptance criteria across similar stories","Template-driven generation produces formulaic output that may not capture complex non-functional requirements or edge cases"],"requires":["Active GeniePM account (free tier available)","High-level requirement text (minimum 50 characters for meaningful generation)","Web browser with JavaScript enabled"],"input_types":["plain text (feature descriptions, epic summaries)","structured text (existing requirements documents)"],"output_types":["structured user stories (JSON or markdown format)","acceptance criteria lists","story metadata (priority, complexity estimates)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_geniepm__cap_1","uri":"capability://planning.reasoning.contextual.task.decomposition.from.user.stories","name":"contextual task decomposition from user stories","description":"Takes a generated or existing user story and automatically breaks it down into granular, actionable tasks with estimated effort levels and dependencies. The system analyzes story acceptance criteria and generates subtasks mapped to development phases (design, implementation, testing, deployment), using pattern matching against common task taxonomies to ensure technical completeness and reduce ambiguity before sprint planning.","intents":["I want to break down a user story into concrete dev tasks without guessing what the team needs to do","I need task estimates and dependencies auto-suggested so sprint planning is faster","I want to ensure no phase (testing, deployment) is forgotten when decomposing stories"],"best_for":["Scrum teams planning 2-week sprints with 10+ stories per cycle","Cross-functional teams (frontend, backend, QA) needing shared task taxonomy","Teams transitioning from waterfall to agile and lacking task decomposition discipline"],"limitations":["Task decomposition is generic and may not account for team-specific workflows (e.g., security review gates, compliance checkpoints)","Effort estimates are relative and unvalidated—require calibration against team velocity data","No awareness of team capacity, skills, or availability—tasks are generated without resource constraints","Dependency detection is shallow; complex cross-story dependencies may be missed"],"requires":["Completed user story (with acceptance criteria)","GeniePM account with project context configured","Optional: team velocity baseline for effort calibration"],"input_types":["user story text","acceptance criteria lists","story metadata (priority, complexity)"],"output_types":["task lists with descriptions","effort estimates (story points or hours)","task dependencies (DAG format)","phase assignments (design/dev/test/deploy)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_geniepm__cap_2","uri":"capability://text.generation.language.acceptance.criteria.auto.generation.and.validation","name":"acceptance criteria auto-generation and validation","description":"Analyzes user story descriptions and generates comprehensive acceptance criteria using pattern matching against common acceptance criteria templates (Given-When-Then format, edge cases, non-functional requirements). The system validates generated criteria for completeness, testability, and alignment with the story intent, flagging ambiguous or missing criteria for manual review before the story enters the sprint.","intents":["I want AI to suggest acceptance criteria so I don't start from a blank page","I need to ensure acceptance criteria are testable and not vague before QA picks up the story","I want to catch missing edge cases or non-functional requirements early in backlog refinement"],"best_for":["Teams with weak BA discipline or no dedicated QA lead defining test criteria","Distributed teams where acceptance criteria clarity is critical to reduce rework","Rapid prototyping teams needing fast backlog refinement cycles"],"limitations":["Generated criteria may be overly generic or miss domain-specific edge cases (e.g., regulatory compliance, security constraints)","No integration with existing test automation frameworks—criteria are text-only, not executable","Validation is pattern-based, not semantic; may flag valid criteria as incomplete if they deviate from common templates","No feedback loop to improve criteria quality based on actual test results or story rework"],"requires":["User story text with clear feature description","GeniePM account","Optional: team's existing acceptance criteria templates for pattern training"],"input_types":["user story description","story title and role/feature/benefit structure"],"output_types":["acceptance criteria lists (Given-When-Then format)","edge case suggestions","non-functional requirement flags","testability validation report"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_geniepm__cap_3","uri":"capability://planning.reasoning.backlog.organization.and.prioritization.assistance","name":"backlog organization and prioritization assistance","description":"Organizes generated or imported user stories into epics, features, and sprints using AI-driven clustering and priority scoring. The system analyzes story relationships, dependencies, and business value signals to suggest groupings and ordering, helping teams structure their backlog without manual reorganization. Prioritization uses heuristics based on story complexity, dependencies, and estimated business impact.","intents":["I want to organize 50+ generated stories into logical epics without manually grouping them","I need AI to suggest which stories should be prioritized based on dependencies and business value","I want to see my backlog structured by feature area so sprint planning is easier"],"best_for":["Product managers managing large backlogs (100+ stories) across multiple features","Teams new to agile lacking established backlog structure or prioritization discipline","Cross-team initiatives requiring coordinated story sequencing"],"limitations":["Prioritization is heuristic-based and lacks business context—requires manual override by product owner","Epic grouping is based on story similarity, not strategic business roadmap alignment","No integration with external prioritization frameworks (RICE, MoSCoW, Kano model)","Dependency detection is shallow; complex cross-epic dependencies may be missed","No capacity-aware scheduling—suggestions don't account for team velocity or resource constraints"],"requires":["Collection of user stories (generated or imported)","GeniePM account with project context","Optional: historical velocity data for better prioritization"],"input_types":["user story list (title, description, acceptance criteria)","story metadata (complexity, business value)"],"output_types":["epic groupings (hierarchical structure)","prioritized story lists","dependency graphs","sprint assignment suggestions"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_geniepm__cap_4","uri":"capability://data.processing.analysis.bulk.story.import.and.template.mapping","name":"bulk story import and template mapping","description":"Accepts bulk story data from external sources (CSV, Jira exports, spreadsheets, or free-form text) and automatically maps fields to GeniePM's story structure (title, description, acceptance criteria, priority, epic). The system uses fuzzy matching and NLP to infer missing fields and standardize story format across heterogeneous sources, enabling teams to migrate existing backlogs or import requirements from non-agile tools.","intents":["I want to import 100+ stories from a spreadsheet without manually re-entering each one","I need to migrate our backlog from Jira to GeniePM and preserve all story metadata","I want to standardize story format across stories written by different team members"],"best_for":["Teams migrating from legacy PM tools or spreadsheet-based backlog management","Organizations consolidating multiple project backlogs into a single system","Teams with existing story archives that need bulk reformatting"],"limitations":["Field mapping is heuristic-based; complex custom fields or non-standard formats may require manual correction","Bulk import may lose custom metadata or relationships not supported by GeniePM schema","No validation of imported story quality—malformed or incomplete stories are imported as-is","Large imports (1000+ stories) may have latency or timeout issues","No rollback capability if import results are unsatisfactory"],"requires":["GeniePM account with import permissions","Source data in supported format (CSV, JSON, Jira export, or plain text)","Mapping configuration (optional; system attempts auto-detection)"],"input_types":["CSV files","JSON exports","Jira backlog exports","plain text story lists","spreadsheet data"],"output_types":["standardized user stories in GeniePM format","import report with field mapping results","error log for malformed entries"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_geniepm__cap_5","uri":"capability://automation.workflow.story.refinement.and.collaborative.editing","name":"story refinement and collaborative editing","description":"Provides a collaborative editing interface where team members can refine AI-generated stories, add comments, suggest edits, and track changes. The system supports real-time collaboration (or async comment threads) with version history, allowing product managers, developers, and QA to iteratively improve story quality before sprint commitment. AI suggestions for improvements (e.g., 'acceptance criteria missing edge case') are surfaced alongside manual edits.","intents":["I want my team to review and refine AI-generated stories before they enter the sprint","I need to track who changed what in a story and why (audit trail)","I want AI to suggest improvements while my team manually edits"],"best_for":["Distributed teams needing async story refinement workflows","Teams with strong backlog refinement ceremonies requiring collaborative editing","Organizations with compliance or audit requirements for story change tracking"],"limitations":["Real-time collaboration may have latency or conflict resolution issues with simultaneous edits","AI suggestions are non-binding and may be ignored, reducing their value","No integration with external communication tools (Slack, Teams) for notification of story changes","Version history is local to GeniePM; no export of audit trail for compliance reporting","Comment threads may become unwieldy for stories with many iterations"],"requires":["GeniePM account with team members invited","Web browser with JavaScript enabled","Optional: team communication norms for refinement workflow"],"input_types":["user stories (generated or imported)","team comments and suggestions","manual edits to story fields"],"output_types":["refined user stories","change history and audit trail","comment threads","AI improvement suggestions"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_geniepm__cap_6","uri":"capability://planning.reasoning.sprint.planning.automation.with.velocity.based.capacity","name":"sprint planning automation with velocity-based capacity","description":"Automatically suggests story assignments to sprints based on team velocity, story complexity estimates, and sprint capacity constraints. The system analyzes historical velocity data (if available) to predict sprint capacity and recommends which prioritized stories fit within the sprint without overloading the team. Capacity planning accounts for team size, story point estimates, and configurable sprint duration.","intents":["I want to know how many stories my team can realistically complete in a 2-week sprint","I need AI to suggest which stories to pull into the sprint based on our velocity","I want to avoid overcommitting the team by auto-calculating sprint capacity"],"best_for":["Scrum teams with established velocity metrics and consistent sprint cadence","Teams new to velocity-based planning seeking data-driven sprint sizing","Multi-team organizations needing consistent capacity planning across teams"],"limitations":["Velocity-based capacity is historical and may not account for team composition changes, skill gaps, or external blockers","Story point estimates are often inaccurate; garbage-in-garbage-out if estimates are poor","No integration with team calendars, holidays, or time-off; capacity assumes full availability","Assumes uniform story complexity; doesn't account for technical risk or dependency complexity","No feedback loop to improve velocity predictions based on actual sprint outcomes"],"requires":["GeniePM account with historical sprint data (minimum 3 sprints for reliable velocity)","Story complexity estimates (story points or t-shirt sizing)","Team size and sprint duration configured"],"input_types":["prioritized story list with complexity estimates","historical sprint velocity data","team capacity (size, availability)"],"output_types":["sprint capacity forecast (story points or story count)","recommended story assignments to sprint","capacity utilization percentage","overflow/backlog stories"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_geniepm__cap_7","uri":"capability://search.retrieval.ai.powered.story.search.and.recommendation","name":"ai-powered story search and recommendation","description":"Provides semantic search across the backlog to find similar stories, duplicates, or related work. The system uses embeddings-based similarity matching to surface related stories when creating new ones, helping teams avoid duplicate work and identify opportunities to consolidate stories. Recommendations are ranked by relevance and can be used to suggest story dependencies or related epics.","intents":["I want to check if a similar story already exists before creating a new one","I need to find all stories related to a specific feature or epic","I want AI to suggest related stories that should be grouped together"],"best_for":["Large backlogs (500+ stories) where duplicate detection is critical","Teams with distributed story creation (multiple PMs) needing consistency","Organizations consolidating multiple product backlogs"],"limitations":["Semantic similarity is based on embeddings; may miss duplicates with different wording or context","No integration with external knowledge bases or documentation; search is limited to backlog stories","Recommendations are suggestions only; no enforcement to prevent duplicate story creation","Search performance may degrade with very large backlogs (10,000+ stories)","No ranking by business impact or priority; all similar stories are treated equally"],"requires":["GeniePM account with populated backlog (minimum 50 stories for meaningful recommendations)","Story descriptions and titles for embedding generation"],"input_types":["search query (natural language or story title)","story description (for similarity matching)"],"output_types":["ranked list of similar stories","duplicate detection results","related story recommendations","similarity scores"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Active GeniePM account (free tier available)","High-level requirement text (minimum 50 characters for meaningful generation)","Web browser with JavaScript enabled","Completed user story (with acceptance criteria)","GeniePM account with project context configured","Optional: team velocity baseline for effort calibration","User story text with clear feature description","GeniePM account","Optional: team's existing acceptance criteria templates for pattern training","Collection of user stories (generated or imported)"],"failure_modes":["Generated stories often lack domain-specific nuance and require 20-40% manual refinement by domain experts","No context awareness across related stories—may generate duplicate acceptance criteria across similar stories","Template-driven generation produces formulaic output that may not capture complex non-functional requirements or edge cases","Task decomposition is generic and may not account for team-specific workflows (e.g., security review gates, compliance checkpoints)","Effort estimates are relative and unvalidated—require calibration against team velocity data","No awareness of team capacity, skills, or availability—tasks are generated without resource constraints","Dependency detection is shallow; complex cross-story dependencies may be missed","Generated criteria may be overly generic or miss domain-specific edge cases (e.g., regulatory compliance, security constraints)","No integration with existing test automation frameworks—criteria are text-only, not executable","Validation is pattern-based, not semantic; may flag valid criteria as incomplete if they deviate from common templates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.892Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=geniepm","compare_url":"https://unfragile.ai/compare?artifact=geniepm"}},"signature":"RAb5AU0YyU3ciZxzcw1I+4FH6GBpCOAkq7cKYS3lTwCcH51JalmQeJ2b6ixW3OvA+fAYgcyn5oRa3SNs/qX9Aw==","signedAt":"2026-06-22T20:08:21.028Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/geniepm","artifact":"https://unfragile.ai/geniepm","verify":"https://unfragile.ai/api/v1/verify?slug=geniepm","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"}}