{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-gmickel--flow-next","slug":"gmickel--flow-next","name":"flow-next","type":"agent","url":"https://mickel.tech/apps/flow-next","page_url":"https://unfragile.ai/gmickel--flow-next","categories":["ai-agents","automation"],"tags":["ai-agent","ai-workflow","anthropic","autonomous-agent","claude-code","claude-code-plugin","cli","code-review","codex","execution","factory-droid","marketplace","openai-codex","planning","plugin","ralph-mode","workflow"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-gmickel--flow-next__cap_0","uri":"capability://planning.reasoning.plan.first.task.decomposition.with.hierarchical.workflow.generation","name":"plan-first task decomposition with hierarchical workflow generation","description":"Generates structured task plans before execution by analyzing user intent and decomposing complex workflows into atomic subtasks with dependency graphs. Uses a planning-first architecture where Claude or Codex models create explicit task hierarchies (with parent-child relationships, sequencing constraints, and resource requirements) that are then validated and executed by worker subagents. The planner outputs a machine-readable task DAG that prevents execution until the full workflow structure is validated.","intents":["I want the AI to think through the entire workflow before executing any code","I need to see the task breakdown and dependencies before running automation","I want to catch planning errors before they cause execution failures"],"best_for":["teams building complex multi-step CI/CD pipelines","developers automating code refactoring across large codebases","engineers who need explainability in AI-driven workflows"],"limitations":["Planning overhead adds latency for simple single-task workflows (100-500ms per plan generation)","Plan quality depends on model capability — weaker models may generate suboptimal task decompositions","No automatic plan re-optimization if execution conditions change mid-workflow"],"requires":["Claude 3+ or OpenAI Codex API access with function calling support","Python 3.8+","flow-next plugin installed in Claude Code or compatible IDE"],"input_types":["natural language task description","code context/codebase snapshot","structured task specifications"],"output_types":["task DAG (JSON/YAML)","execution plan with sequencing","dependency graph visualization"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_1","uri":"capability://automation.workflow.worker.subagent.orchestration.with.role.based.task.assignment","name":"worker subagent orchestration with role-based task assignment","description":"Spawns and manages multiple specialized subagents (workers) that execute assigned tasks in parallel or sequence based on the task DAG. Each worker receives a scoped task context, execution constraints, and access to specific tools/APIs. The orchestrator handles worker lifecycle (creation, monitoring, cleanup), inter-worker communication via a message queue, and aggregates results back to the main workflow. Workers are stateless and can be horizontally scaled.","intents":["I want multiple AI workers to execute different parts of my workflow simultaneously","I need to assign specific tasks to specialized agents (e.g., code reviewer, refactorer, tester)","I want to parallelize independent tasks to reduce total execution time"],"best_for":["large teams running distributed code review and refactoring workflows","CI/CD pipelines requiring parallel task execution","organizations with high-volume automation needs"],"limitations":["Worker communication overhead increases with task count — diminishing returns beyond 8-10 parallel workers","No built-in worker failure recovery — requires external orchestration (Kubernetes, etc.) for production resilience","Subagent context window is limited per worker — large codebases may require chunking"],"requires":["Claude Code or Factory Droid with plugin support","API credentials for primary LLM (Claude/Codex)","Optional: message queue (Redis/RabbitMQ) for inter-worker communication at scale"],"input_types":["task specification with role/capability requirements","execution context (code, files, environment variables)","dependency constraints from task DAG"],"output_types":["task execution results","worker logs and telemetry","aggregated workflow output"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_10","uri":"capability://automation.workflow.workflow.execution.monitoring.and.telemetry.with.structured.logging","name":"workflow execution monitoring and telemetry with structured logging","description":"Captures detailed execution telemetry (task start/end times, worker IDs, API calls, token usage, errors) and logs it in structured format (JSON) for analysis. Provides real-time monitoring dashboard (optional) showing task progress, worker status, and resource usage. Logs are queryable and can be exported for external analysis. Supports custom metrics and event hooks.","intents":["I want to understand what my workflows are doing and how long they take","I need to debug failed workflows by inspecting detailed execution logs","I want to track API costs and token usage across workflows"],"best_for":["teams running production workflows","organizations tracking AI API costs","developers debugging complex multi-step workflows"],"limitations":["Structured logging adds ~5-10% overhead to execution time","Log storage grows unbounded — requires archival/cleanup policy","Real-time dashboard requires additional infrastructure (optional)"],"requires":["flow-next with telemetry enabled","Filesystem or cloud storage for log persistence","Optional: log aggregation system (ELK, Datadog, etc.)"],"input_types":["execution events (task start/end, errors)"],"output_types":["structured logs (JSON)","telemetry metrics (duration, token count, cost)","monitoring dashboard (optional)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_11","uri":"capability://automation.workflow.workflow.composition.and.reusability.with.task.templates.and.macros","name":"workflow composition and reusability with task templates and macros","description":"Enables creation of reusable task templates and workflow macros that can be composed into larger workflows. Templates define parameterized task specifications (e.g., 'code-review' template with configurable rubric), and macros combine multiple templates into common patterns (e.g., 'review-and-refactor' macro). Composition is declarative and supports nesting. Templates are versioned and can be shared across projects.","intents":["I want to reuse common task patterns across multiple workflows","I need to parameterize tasks so they can be customized per workflow","I want to build complex workflows from simple, composable pieces"],"best_for":["teams with multiple similar workflows","organizations standardizing on common patterns","projects requiring workflow modularity"],"limitations":["Template composition adds complexity — requires careful design to avoid over-abstraction","Parameter passing can become unwieldy with deeply nested templates","Template versioning requires careful management to avoid breaking changes"],"requires":["flow-next with template support","Template definitions (YAML/JSON)","Version control for template management"],"input_types":["template specification","template parameters","macro definition"],"output_types":["composed workflow","instantiated task specifications"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_2","uri":"capability://automation.workflow.ralph.autonomous.mode.with.minimal.human.intervention","name":"ralph autonomous mode with minimal human intervention","description":"Enables fully autonomous workflow execution where the system makes execution decisions without human approval gates. Ralph mode uses a confidence-scoring mechanism to determine when human review is necessary vs. when the system can proceed autonomously. The system maintains an audit trail of autonomous decisions and can roll back if issues are detected post-execution. Autonomy is configurable per task type (e.g., code generation requires review, file deletion requires approval).","intents":["I want the AI to execute routine tasks without waiting for my approval","I need to set guardrails for what the AI can do autonomously vs. what requires review","I want to reduce latency by removing human approval bottlenecks for low-risk operations"],"best_for":["teams with high-trust in AI systems and strong rollback procedures","organizations running 24/7 automation with async human oversight","low-risk workflows (linting, formatting, test execution)"],"limitations":["Autonomous decisions are irreversible for destructive operations (file deletion, database writes) — requires external backup/recovery","Confidence scoring is heuristic-based and may fail on edge cases — false confidence can cause unintended changes","Audit trail adds storage overhead (~1-5MB per workflow execution)"],"requires":["flow-next with Ralph mode enabled","Explicit policy configuration defining autonomous vs. gated operations","External logging/monitoring system for audit trail persistence"],"input_types":["task specification with risk classification","autonomy policy (JSON/YAML)","execution context"],"output_types":["execution result with confidence score","audit log entry","rollback instructions if needed"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_3","uri":"capability://code.generation.editing.cross.model.code.review.with.multi.provider.consensus","name":"cross-model code review with multi-provider consensus","description":"Executes code review tasks across multiple LLM providers (Claude, Codex, etc.) in parallel and aggregates findings using a consensus mechanism. Each model reviews the same code independently, and the system identifies common issues (high-confidence findings) vs. divergent opinions (model-specific concerns). Results are ranked by consensus strength and presented with model attribution. Supports custom review rubrics and can weight models by historical accuracy.","intents":["I want multiple AI models to review my code and highlight issues they all agree on","I need to reduce false positives in automated code review by using consensus","I want to understand which issues are model-specific vs. universally recognized problems"],"best_for":["teams requiring high-confidence code review automation","organizations with multiple LLM provider contracts","projects where code quality is critical (security, safety-critical systems)"],"limitations":["Multi-model review multiplies API costs by provider count (3x cost for 3 models)","Consensus mechanism adds latency — slowest model determines review time","Model disagreement on style issues creates noise — requires tuning consensus thresholds"],"requires":["API keys for 2+ LLM providers (Claude, Codex, etc.)","flow-next with cross-model review plugin","Custom review rubric (optional, defaults to standard linting rules)"],"input_types":["source code (any language supported by providers)","review rubric/guidelines (JSON)","model weights/preferences"],"output_types":["consensus findings (high-confidence issues)","model-specific findings (with attribution)","review report with confidence scores"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_4","uri":"capability://automation.workflow.zero.dependency.task.tracking.and.state.management","name":"zero-dependency task tracking and state management","description":"Maintains workflow execution state and task progress without external databases or state stores. Uses in-memory task registry with optional file-based persistence (JSON/YAML snapshots). Task state includes status (pending/running/completed/failed), execution metadata (start time, duration, worker ID), and result artifacts. State is immutable and versioned — each state change creates a new snapshot. Supports local-first operation with optional cloud sync.","intents":["I want to track task progress without setting up a database","I need to inspect task state and execution history locally","I want to resume interrupted workflows from the last completed task"],"best_for":["solo developers and small teams","local development and testing workflows","environments where external dependencies are restricted"],"limitations":["In-memory state is lost on process restart — requires file persistence for durability","No built-in distributed consensus — unsuitable for multi-process workflows without external coordination","Task history grows unbounded — requires manual cleanup or archival for long-running systems"],"requires":["Python 3.8+","Filesystem write access for state snapshots","Optional: cloud storage credentials for remote sync"],"input_types":["task specification","execution events (start, complete, fail)"],"output_types":["task state snapshot (JSON/YAML)","execution history","state diff for auditing"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_5","uri":"capability://tool.use.integration.ide.integrated.workflow.execution.with.claude.code.and.factory.droid.plugins","name":"ide-integrated workflow execution with claude code and factory droid plugins","description":"Provides native plugins for Claude Code and Factory Droid IDEs that embed workflow execution directly in the editor. Workflows are triggered via IDE commands or inline annotations, and results are displayed in editor panels or inline. The plugin maintains context awareness of the current file/project and passes relevant code context to the workflow engine. Supports VS Code-style command palette integration and keybinding customization.","intents":["I want to run AI workflows without leaving my IDE","I need the workflow to understand my current code context automatically","I want to see results inline without context switching"],"best_for":["developers using Claude Code or Factory Droid as primary IDE","teams with standardized IDE tooling","workflows tightly coupled to code editing (refactoring, review, generation)"],"limitations":["Plugin API stability depends on IDE vendor updates — breaking changes possible","IDE context window is limited — large codebases may not fit in editor context","Inline result display is constrained by editor UI — complex visualizations not supported"],"requires":["Claude Code or Factory Droid IDE installed","flow-next plugin installed in IDE marketplace","IDE version 1.80+ (Claude Code) or equivalent"],"input_types":["current file content","project context (file tree, imports)","user selection/cursor position"],"output_types":["inline code suggestions","editor panel results","file modifications"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_6","uri":"capability://tool.use.integration.openai.codex.and.claude.api.multi.model.support.with.provider.abstraction","name":"openai codex and claude api multi-model support with provider abstraction","description":"Abstracts LLM provider differences behind a unified interface, allowing workflows to switch between Claude, Codex, and other providers without code changes. Handles provider-specific API differences (function calling schemas, token limits, rate limits) transparently. Supports model-specific optimizations (e.g., using Claude for reasoning, Codex for code generation) and automatic fallback if a provider is unavailable. Provider configuration is declarative (YAML/JSON).","intents":["I want to use multiple LLM providers without rewriting my workflow code","I need to switch models based on task type (reasoning vs. code generation)","I want automatic fallback if my primary provider is down"],"best_for":["organizations with multiple LLM provider contracts","teams experimenting with different models","workflows requiring model-specific optimizations"],"limitations":["Abstraction layer adds ~50-100ms latency per API call due to request translation","Provider-specific features (e.g., Claude's vision) may not be fully exposed through abstraction","Cost tracking across providers requires manual aggregation — no built-in cost reporting"],"requires":["API keys for 1+ LLM providers (Claude, Codex, etc.)","Provider configuration file (YAML/JSON)","flow-next with multi-provider support"],"input_types":["provider configuration","model selection criteria","task specification"],"output_types":["LLM response (normalized format)","provider metadata (model used, tokens consumed)"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_7","uri":"capability://automation.workflow.cli.based.workflow.invocation.with.argument.parsing.and.environment.integration","name":"cli-based workflow invocation with argument parsing and environment integration","description":"Exposes workflow execution via command-line interface with structured argument parsing, environment variable support, and shell integration. Workflows can be invoked as `flow-next run <workflow-name> --arg1 value1 --arg2 value2`, with arguments validated against a schema. Supports piping input/output for shell composition and exit codes for CI/CD integration. Environment variables are automatically injected into workflow context.","intents":["I want to run workflows from the command line without GUI","I need to integrate workflows into shell scripts and CI/CD pipelines","I want to pass arguments and environment variables to workflows"],"best_for":["DevOps engineers integrating workflows into CI/CD","developers preferring CLI over GUI","teams with existing shell script automation"],"limitations":["CLI output is text-based — complex visualizations not supported","Argument parsing is strict — typos cause failures rather than fuzzy matching","No interactive CLI mode — workflows must be fully specified upfront"],"requires":["flow-next CLI installed (Python 3.8+)","Workflow definitions in accessible location","Shell environment (bash, zsh, etc.)"],"input_types":["command-line arguments","environment variables","stdin (for piped input)"],"output_types":["stdout (workflow results)","stderr (logs/errors)","exit code (0 for success, non-zero for failure)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_8","uri":"capability://automation.workflow.workflow.definition.as.code.with.yaml.json.schema.validation","name":"workflow definition as code with yaml/json schema validation","description":"Allows workflows to be defined declaratively in YAML or JSON with a strict schema that validates structure before execution. Definitions include task specifications, dependencies, worker assignments, and execution policies. Schema validation catches configuration errors early (missing required fields, invalid task references, circular dependencies). Supports schema versioning and migration for backward compatibility.","intents":["I want to define workflows as configuration files, not code","I need validation to catch workflow definition errors before execution","I want version control and code review for workflow changes"],"best_for":["teams with non-technical workflow designers","organizations requiring workflow auditability","projects with frequently changing workflows"],"limitations":["Schema validation is static — cannot catch runtime errors (e.g., API failures)","Complex conditional logic is awkward in YAML/JSON — may require custom extensions","Schema versioning adds maintenance burden as features evolve"],"requires":["YAML or JSON editor","flow-next schema definition (provided)","Optional: JSON Schema validator (built-in)"],"input_types":["YAML/JSON workflow definition"],"output_types":["validated workflow object","schema validation errors"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-gmickel--flow-next__cap_9","uri":"capability://memory.knowledge.execution.context.and.codebase.awareness.with.automatic.code.indexing","name":"execution context and codebase awareness with automatic code indexing","description":"Automatically indexes the current codebase (file tree, imports, function signatures, dependencies) and makes this context available to workflow tasks. Indexing uses AST parsing or language-specific tools to extract semantic information (not just text search). Context is passed to LLM tasks as structured data, enabling model-aware code generation and refactoring. Supports incremental indexing for large codebases.","intents":["I want the AI to understand my codebase structure without me explaining it","I need code generation that respects my project's architecture and conventions","I want to refactor code with full awareness of dependencies and usage"],"best_for":["teams with large, complex codebases","projects requiring architecture-aware code generation","developers working on refactoring tasks"],"limitations":["Indexing latency grows with codebase size — 10k+ files may take 10-30 seconds","AST parsing is language-specific — not all languages equally supported","Index staleness — changes not reflected until re-indexing"],"requires":["flow-next with indexing support","Language-specific parsers (tree-sitter, etc.) for target languages","Filesystem access to codebase"],"input_types":["codebase root directory","language configuration"],"output_types":["indexed codebase structure (JSON)","semantic information (functions, classes, imports)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":44,"verified":false,"data_access_risk":"high","permissions":["Claude 3+ or OpenAI Codex API access with function calling support","Python 3.8+","flow-next plugin installed in Claude Code or compatible IDE","Claude Code or Factory Droid with plugin support","API credentials for primary LLM (Claude/Codex)","Optional: message queue (Redis/RabbitMQ) for inter-worker communication at scale","flow-next with telemetry enabled","Filesystem or cloud storage for log persistence","Optional: log aggregation system (ELK, Datadog, etc.)","flow-next with template support"],"failure_modes":["Planning overhead adds latency for simple single-task workflows (100-500ms per plan generation)","Plan quality depends on model capability — weaker models may generate suboptimal task decompositions","No automatic plan re-optimization if execution conditions change mid-workflow","Worker communication overhead increases with task count — diminishing returns beyond 8-10 parallel workers","No built-in worker failure recovery — requires external orchestration (Kubernetes, etc.) for production resilience","Subagent context window is limited per worker — large codebases may require chunking","Structured logging adds ~5-10% overhead to execution time","Log storage grows unbounded — requires archival/cleanup policy","Real-time dashboard requires additional infrastructure (optional)","Template composition adds complexity — requires careful design to avoid over-abstraction","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3586908760102202,"quality":0.49,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"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":"2026-05-03T13:57:09.058Z","last_commit":"2026-05-02T13:07:43Z"},"community":{"stars":581,"forks":45,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=gmickel--flow-next","compare_url":"https://unfragile.ai/compare?artifact=gmickel--flow-next"}},"signature":"5Py7dKM2eH3NFmD5pnwOaAyen7oWyvSNoUivx1DK8TX9W8X64p9iqwoK9oDOQNJZ+TAMy8icHnO63XflbiPlAw==","signedAt":"2026-06-20T16:26:32.604Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gmickel--flow-next","artifact":"https://unfragile.ai/gmickel--flow-next","verify":"https://unfragile.ai/api/v1/verify?slug=gmickel--flow-next","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"}}