{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-rohitg00--pro-workflow","slug":"rohitg00--pro-workflow","name":"pro-workflow","type":"agent","url":"https://rohitg00.github.io/pro-workflow/infographic.html","page_url":"https://unfragile.ai/rohitg00--pro-workflow","categories":["ai-agents","automation"],"tags":["agent-orchestration","ai-agents","ai-coding","ai-workflow","claude","claude-code","claude-code-plugin","claude-code-skills","claude-skills","codex","context-engineering","cursor","developer-tools","gemini-cli","hooks","productivity","self-correction","workflow","worktrees"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-rohitg00--pro-workflow__cap_0","uri":"capability://memory.knowledge.persistent.self.correction.memory.with.sqlite.fts5.indexing","name":"persistent self-correction memory with sqlite fts5 indexing","description":"Captures developer corrections (style preferences, architectural constraints, bug fixes) into a local SQLite database with full-text search (FTS5) indexing. On every session start, learnings are automatically replayed to the AI agent, creating a compounding correction loop that reduces correction rate toward zero over 50+ sessions. Uses omitClaudeMd token optimization to minimize context overhead while maximizing retention of learned patterns.","intents":["I want the AI to remember my coding style and architectural preferences across multiple sessions without repeating corrections","I need to track what corrections I've made and replay them automatically to reduce repetitive feedback","I want to measure how much the AI improves over time as it learns from my corrections"],"best_for":["solo developers working on long-term projects with consistent style guides","teams building internal coding standards that need to be enforced across AI sessions","developers who want to reduce token usage by avoiding repeated context injection"],"limitations":["SQLite database is local-only — no built-in cloud sync or multi-device sharing","FTS5 search is limited to text-based corrections — cannot index binary or image-based feedback","Correction replay happens at session start only, not dynamically during conversation","No built-in deduplication of similar corrections — may accumulate redundant entries over time"],"requires":["SQLite 3.8.0+ (for FTS5 support)","Local file system write access for database persistence","Claude Code or Cursor agent integration"],"input_types":["text corrections from developer feedback","code diffs and refactoring suggestions","structured metadata about correction context"],"output_types":["replayed correction context injected into agent prompt","searchable correction history with FTS5 ranking","correction statistics and learning metrics"],"categories":["memory-knowledge","self-correction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_1","uri":"capability://planning.reasoning.multi.agent.orchestration.with.hierarchical.command.routing","name":"multi-agent orchestration with hierarchical command routing","description":"Implements a three-tier command hierarchy (Command > Agent > Skill) that routes user intent through 8 specialized agents (Orchestrator, Context Engineer, Development Lifecycle agents, Quality & Review agents) to 24 modular skills. The Orchestrator manages a Research > Plan > Implement > Review workflow, coordinating parallel agent execution via a centralized event dispatcher. Each agent has role-specific token optimization and can be composed into agent teams for complex multi-phase tasks.","intents":["I want to decompose a complex development task into parallel work streams handled by different specialized agents","I need to enforce a consistent workflow (research, planning, implementation, review) across all AI-assisted development","I want to coordinate multiple AI agents without manually managing context passing between them"],"best_for":["teams building complex features that require research, planning, and review phases","developers who want to enforce governance gates (code review, security checks) between development phases","organizations standardizing on multi-agent workflows across Claude Code and Cursor"],"limitations":["Agent coordination is synchronous — no built-in support for asynchronous agent communication or message queues","Parallel execution is limited to agents that don't share mutable state — complex state management requires manual coordination","Agent team composition is static (defined in config) — no dynamic agent spawning based on task complexity","No built-in fallback or retry logic if an agent fails mid-workflow"],"requires":["Node.js 18+ for event dispatcher and async coordination","config.json with agent and skill definitions","Claude Code or Cursor with plugin manifest support"],"input_types":["slash commands (e.g., /develop, /commit, /wrap-up)","task descriptions with implicit phase requirements","agent configuration JSON"],"output_types":["routed skill invocations with context","phase-specific outputs (research notes, implementation code, review feedback)","workflow execution logs and agent telemetry"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_10","uri":"capability://tool.use.integration.skill.composition.and.reuse.across.agents.and.workflows","name":"skill composition and reuse across agents and workflows","description":"Defines 24 modular skills that encapsulate specific capabilities (git operations, context optimization, quality checks, etc.) and can be composed into workflows. Skills are organized into four categories: Workflow & Orchestration Skills (git commit, branch management), Quality & Memory Skills (test execution, correction capture), Context & Cost Management Skills (token budgeting, context compaction), and Security & Governance Skills (secret scanning, permission checks). Skills can be reused across different agents and commands, reducing code duplication and enabling consistent behavior.","intents":["I want to reuse common capabilities (git operations, testing, context optimization) across multiple agents without duplicating code","I need to compose complex workflows from simple, testable skills","I want to ensure consistent behavior across different commands and agents"],"best_for":["teams building custom workflows that need to reuse common capabilities","organizations standardizing on a set of core skills across multiple projects","developers who want to test skills independently before composing them into workflows"],"limitations":["Skills are tightly coupled to Pro Workflow's architecture — difficult to reuse outside the framework","Skill composition is static (defined in config) — no dynamic skill selection based on runtime conditions","Skill dependencies are implicit — no built-in dependency management or ordering","Skill failures are logged but don't automatically trigger fallback skills"],"requires":["Understanding of skill definitions and composition","config.json with skill and workflow definitions"],"input_types":["skill definitions (name, description, parameters)","skill composition (which skills to execute in which order)","skill parameters (specific to each skill)"],"output_types":["skill execution results","composed workflow output","skill execution logs and telemetry"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_11","uri":"capability://planning.reasoning.development.lifecycle.workflow.orchestration.research.plan.implement.review","name":"development lifecycle workflow orchestration (research > plan > implement > review)","description":"Implements a structured four-phase workflow (Research > Plan > Implement > Review) that guides development from problem understanding to code review. Each phase is handled by specialized agents and skills, with explicit handoffs and context passing between phases. The Orchestrator agent manages phase transitions, ensuring that outputs from one phase become inputs to the next. Developers can skip phases or run them in parallel using worktrees, but the default workflow enforces a sequential, quality-focused approach.","intents":["I want to follow a structured development process that ensures thorough research and planning before implementation","I need to enforce code review as a mandatory phase before code is merged","I want to understand what happened in each phase and why decisions were made"],"best_for":["teams with formal development processes that require research and planning phases","organizations building mission-critical code that needs thorough review","developers who want to improve code quality by enforcing a structured workflow"],"limitations":["Four-phase workflow may be overkill for simple bug fixes or documentation changes","Phase transitions are sequential by default — parallel execution requires explicit worktree setup","Phase outputs are context-dependent — skipping a phase may leave the next phase with incomplete information","No built-in rollback if a phase fails — developers must manually undo changes"],"requires":["Understanding of the four-phase workflow and when each phase is appropriate","Agents configured for each phase (Research, Plan, Implement, Review)"],"input_types":["feature request or bug description","phase-specific inputs (research notes, design documents, code changes)"],"output_types":["phase-specific outputs (research summary, implementation plan, code, review comments)","workflow execution log with phase transitions","decision rationale for each phase"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_12","uri":"capability://memory.knowledge.correction.capture.and.replay.with.semantic.matching","name":"correction capture and replay with semantic matching","description":"Captures developer corrections (code changes, style feedback, architectural decisions) and stores them with semantic metadata (context, intent, affected code patterns). On subsequent sessions, similar corrections are automatically replayed using FTS5 semantic search. The system learns which corrections are most frequently applied and prioritizes them in context injection. Corrections can be manually reviewed, edited, or deleted before replay to ensure accuracy.","intents":["I want to capture my coding preferences once and have them automatically applied to future code","I need to understand which corrections are most impactful and prioritize them in context","I want to review and refine my corrections over time as my preferences evolve"],"best_for":["developers with consistent coding styles who want to enforce them automatically","teams building shared correction libraries that can be reused across projects","organizations tracking how coding standards evolve over time"],"limitations":["Semantic matching is based on text similarity (FTS5) — may match unrelated corrections if they use similar keywords","Correction replay is automatic — developers must manually review and reject incorrect matches","Corrections are stored locally — no built-in sharing mechanism across teams or projects","No built-in versioning of corrections — old corrections are overwritten by new ones"],"requires":["SQLite 3.8.0+ for FTS5 support","Developer feedback on code changes (corrections)"],"input_types":["code changes with developer feedback","correction metadata (context, intent, affected patterns)","semantic search query (similar corrections)"],"output_types":["captured correction stored in SQLite","semantic search results (similar corrections from history)","correction replay context injected into agent prompt"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_13","uri":"capability://automation.workflow.git.integration.with.automated.commit.messages.and.branch.management","name":"git integration with automated commit messages and branch management","description":"Integrates with git to automate commit operations, branch creation, and merge workflows. Agents can generate commit messages based on code changes, create feature branches with semantic naming, and manage branch lifecycle (creation, switching, deletion). Git hooks are used to enforce quality gates before commits. The system maintains a git history that can be queried to understand code evolution and correlate changes with corrections.","intents":["I want to generate meaningful commit messages automatically based on code changes","I need to create feature branches with consistent naming conventions","I want to enforce quality gates (tests, linting, security scans) before commits are allowed"],"best_for":["teams with strict commit message standards who want to enforce them automatically","projects with complex branching strategies that need automation","organizations using git hooks for CI/CD integration"],"limitations":["Commit message generation is AI-driven — may miss important context or generate misleading messages","Branch naming conventions are configurable but not enforced by git — developers can still create branches with non-standard names","Git hooks are synchronous — blocking hooks can slow down commit operations","No built-in support for rebasing or squashing commits"],"requires":["Git 2.15+ with hook support","Git repository with write access"],"input_types":["code changes (git diff)","branch naming convention configuration","commit message template"],"output_types":["generated commit message","created feature branch","git hook execution results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_14","uri":"capability://memory.knowledge.session.based.context.isolation.and.cleanup","name":"session-based context isolation and cleanup","description":"Manages session lifecycle with automatic context isolation and cleanup. Each session maintains its own context window, correction history, and worktree state. Sessions can be explicitly started, paused, resumed, or ended. On session end, temporary files and worktrees are cleaned up, and session metadata (duration, corrections applied, tokens used) is logged for analysis. Sessions can be resumed later with full context restoration.","intents":["I want to work on a task, pause it, and resume it later without losing context","I need to isolate context between different development tasks to prevent interference","I want to understand how long tasks take and how many corrections are needed"],"best_for":["developers working on multiple tasks and switching between them frequently","teams tracking development metrics (time per task, corrections per session)","organizations with long-running development tasks that span multiple days"],"limitations":["Session state is stored locally — no cloud sync or multi-device access","Session resumption requires the same git repository and worktree — moving code to a different machine breaks session state","Session cleanup is automatic but may delete important files if not configured correctly","No built-in session sharing or collaboration — each developer has independent sessions"],"requires":["Local file system for session state storage","Git repository for worktree management"],"input_types":["session start command with task description","session pause/resume commands","session cleanup configuration"],"output_types":["session ID and metadata","session context (corrections, worktree state)","session metrics (duration, corrections, tokens used)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_15","uri":"capability://data.processing.analysis.cost.estimation.and.budget.enforcement.with.multi.model.support","name":"cost estimation and budget enforcement with multi-model support","description":"Provides cost estimation for commands before execution, supporting multiple models (Claude 3.5 Sonnet, GPT-4, Gemini, etc.) with their respective pricing. Estimates include token count, model cost, and total cost across all agents in a workflow. Budget enforcement can be configured as warnings (alert but allow) or hard blocks (prevent execution). The system tracks cumulative costs per session and per project, enabling cost analysis and optimization.","intents":["I want to know how much a command will cost before executing it","I need to enforce a monthly or per-project budget to control API costs","I want to understand which commands and agents are most expensive"],"best_for":["organizations with strict API budgets who need cost control","developers using expensive models (GPT-4, Claude 3 Opus) who want to optimize costs","teams tracking development costs per project or per feature"],"limitations":["Cost estimation is approximate — actual costs may vary due to token count variations and model pricing changes","Budget enforcement is advisory (warnings) by default — hard blocks require custom hook configuration","Cost tracking is local-only — no cloud-based cost aggregation across teams","Pricing data must be manually updated when models change pricing"],"requires":["Model pricing configuration in config.json","API key for token estimation (if using API-based estimation)"],"input_types":["command context (code, corrections, agent state)","model identifier and pricing","budget configuration (max cost, enforcement level)"],"output_types":["cost estimate with breakdown by model and agent","budget status (under/over budget)","cost analysis report (most expensive commands, agents)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_16","uri":"capability://automation.workflow.doctor.command.for.system.diagnostics.and.configuration.validation","name":"doctor command for system diagnostics and configuration validation","description":"Provides a /doctor command that validates the entire Pro Workflow installation and configuration. Checks include: git version and worktree support, Node.js version, required dependencies, config.json syntax and completeness, plugin manifest compatibility, database integrity, hook script syntax, and API key validity. Generates a detailed report with warnings, errors, and suggestions for fixing issues. Can be run before starting work to ensure the system is properly configured.","intents":["I want to verify that Pro Workflow is properly installed and configured before starting work","I need to troubleshoot configuration issues and understand what's missing or broken","I want to ensure all dependencies are installed and compatible"],"best_for":["developers setting up Pro Workflow for the first time","teams onboarding new developers who need to validate their setup","organizations troubleshooting Pro Workflow issues"],"limitations":["Doctor command only validates configuration — doesn't fix issues automatically","Some checks require external tools (git, Node.js) to be installed — can't validate if tools are missing","API key validation requires making a test API call — may fail if network is unavailable","No built-in update mechanism — doctor doesn't upgrade Pro Workflow to the latest version"],"requires":["Pro Workflow installation","Access to config.json and plugin manifests"],"input_types":["/doctor command with optional flags (--verbose, --fix)"],"output_types":["diagnostic report with status of each check","error messages with suggestions for fixing issues","configuration summary"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_2","uri":"capability://safety.moderation.event.driven.hook.system.with.29.interceptor.scripts.across.24.events","name":"event-driven hook system with 29 interceptor scripts across 24 events","description":"Provides a pluggable event-driven architecture that intercepts 24 system events (file writes, commits, context changes, permission checks) with 29 custom scripts. Hooks can perform AI-powered secret scanning, anti-pattern detection, token budget enforcement, and permission validation before actions execute. Hooks are organized into three categories: Tool Execution Hooks (pre/post command), Session & Context Hooks (lifecycle events), and Environment & Permission Hooks (security gates).","intents":["I want to automatically scan code for secrets before it gets committed or sent to the AI","I need to enforce token budget limits and prevent context overflow before expensive operations","I want to detect anti-patterns in code and flag them before the AI incorporates them into future sessions"],"best_for":["security-conscious teams that need secret scanning and compliance gates in AI workflows","organizations with strict token budgets who want to prevent runaway context usage","teams building custom governance rules on top of Claude Code or Cursor"],"limitations":["Hook execution is synchronous — blocking hooks can add latency to every command (no async hook support)","Hook scripts are JavaScript-only — no support for Python, Go, or other languages","No built-in hook ordering or dependency management — complex multi-hook scenarios require manual sequencing","Hook failures are logged but don't prevent action execution by default — requires explicit error handling in hook script"],"requires":["Node.js 18+ for hook script execution","JavaScript knowledge to write custom hook scripts","Write access to .claude-plugin/hooks/ or .cursor-plugin/hooks/ directory"],"input_types":["event context object (file path, command, user, timestamp)","pre-action state (code content, git diff, token count)","hook configuration (enabled/disabled, priority)"],"output_types":["hook execution result (allow/deny/warn)","modified context (sanitized code, adjusted token budget)","audit logs with hook decision rationale"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_3","uri":"capability://memory.knowledge.context.aware.token.budget.management.with.compaction.strategies","name":"context-aware token budget management with compaction strategies","description":"The Context Engineer agent audits token budgets before expensive operations and applies compaction strategies to keep context within limits. Implements omitClaudeMd optimization (removes markdown formatting from code blocks), context pruning (removes low-relevance historical messages), and split memory templates that separate long-term learnings from session-specific context. Provides /context and /cost commands to inspect token usage and simulate context overhead before executing commands.","intents":["I want to know how many tokens a command will consume before executing it","I need to automatically compact context when approaching token limits without losing critical information","I want to understand which parts of my codebase and correction history are consuming the most tokens"],"best_for":["developers working with large codebases who need to optimize token usage to reduce API costs","teams with strict token budgets who want to prevent expensive context overflow","organizations using expensive models (GPT-4, Claude 3 Opus) where token optimization directly impacts cost"],"limitations":["Token estimation is approximate — actual token count may vary by ±5% due to tokenizer differences between models","Context compaction is lossy — omitClaudeMd removes formatting that may be semantically important for some code","Split memory templates require manual configuration — no automatic detection of long-term vs session-specific context","Token budget enforcement is advisory (warns but doesn't block) — no hard limit enforcement without custom hooks"],"requires":["API key for Claude or OpenAI (to estimate token counts via API)","config.json with tokenBudget and compactionStrategy settings","Understanding of your model's token limits (e.g., Claude 3.5 Sonnet: 200k tokens)"],"input_types":["command context (code files, correction history, agent state)","token budget configuration (max tokens, compaction thresholds)","model identifier (to select correct tokenizer)"],"output_types":["token count estimate with breakdown by component","compacted context with compression ratio","cost estimate in USD based on model pricing","compaction recommendations (which components to prune)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_4","uri":"capability://memory.knowledge.rule.based.code.style.and.architecture.enforcement.via.mdc.files","name":"rule-based code style and architecture enforcement via .mdc files","description":"Stores coding rules, architectural constraints, and style preferences in declarative .mdc (Markdown Config) files that are automatically injected into agent context at session start. Rules are organized by domain (naming conventions, error handling, testing patterns, security constraints) and can reference external templates or context mode files. The system supports rule inheritance and overrides, allowing team-wide rules to be customized per-project or per-developer.","intents":["I want to define team coding standards once and have them automatically enforced across all AI-assisted development","I need to capture architectural constraints (e.g., 'all async operations must use async/await, not promises') and replay them to the AI","I want to version control my coding rules alongside my codebase"],"best_for":["teams with established coding standards who want to enforce them consistently across AI agents","organizations migrating from manual code review to AI-assisted development with governance","projects with complex architectural constraints that need to be communicated to every agent session"],"limitations":[".mdc files are text-based — no built-in validation that rules are syntactically correct or enforceable","Rule inheritance is simple (single-level override) — no support for complex rule composition or conditional rules","Rules are injected as context, not enforced by the agent — the AI can ignore rules if it chooses","No built-in conflict detection if rules contradict each other"],"requires":["Understanding of .mdc file format and rule syntax","Git repository to version control .mdc files","Claude Code or Cursor with rule injection support"],"input_types":[".mdc files with rule definitions (text format)","rule templates (reusable rule snippets)","context mode files (external rule references)"],"output_types":["injected rule context in agent prompt","rule compliance report (which rules were followed/violated)","rule suggestion based on code patterns"],"categories":["memory-knowledge","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_5","uri":"capability://automation.workflow.parallel.worktree.management.with.git.integration","name":"parallel worktree management with git integration","description":"Manages multiple git worktrees (isolated working directories) to enable parallel development streams without branch conflicts. Each worktree is associated with a specific agent or task, and the system automatically handles worktree creation, switching, and cleanup. Integrates with git hooks to enforce quality gates (linting, testing, secret scanning) before commits. Supports worktree-specific context and memory, allowing different agents to work on different features simultaneously without context pollution.","intents":["I want to work on multiple features in parallel without switching branches or managing complex merge conflicts","I need each agent to have its own isolated working directory so they don't interfere with each other","I want to automatically clean up worktrees after a feature is merged or abandoned"],"best_for":["teams with multiple developers or agents working on different features simultaneously","projects with complex branching strategies where branch switching is expensive","CI/CD pipelines that need to validate multiple feature branches in parallel"],"limitations":["Worktrees require Git 2.15+ and don't work with all git workflows (e.g., shallow clones)","Worktree cleanup is manual or requires explicit commands — no automatic cleanup on agent completion","Worktree-specific context increases memory overhead — each worktree maintains separate correction history","No built-in conflict resolution if two worktrees modify the same file"],"requires":["Git 2.15+ with worktree support","Local file system with sufficient disk space for multiple worktrees","Bash or shell script support for worktree automation"],"input_types":["task description or feature branch name","base branch (e.g., 'main' or 'develop')","worktree configuration (cleanup policy, context isolation)"],"output_types":["created worktree path and git branch","worktree-specific context and memory","worktree status (active, idle, ready for cleanup)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_6","uri":"capability://tool.use.integration.slash.command.interface.with.21.development.and.workflow.commands","name":"slash command interface with 21 development and workflow commands","description":"Exposes a CLI-like interface with 21 slash commands (e.g., /develop, /commit, /wrap-up, /doctor) that trigger specific agent workflows. Commands are organized into three categories: Development Commands (code generation, refactoring), Memory & Learning Commands (correction capture, history replay), and Context & Cost Commands (token budgeting, context inspection). Each command is mapped to a specific agent and skill combination, with built-in help, argument validation, and error handling.","intents":["I want a quick, memorable way to trigger common development workflows without navigating menus","I need to understand what each command does and what arguments it requires","I want to automate command sequences (e.g., /develop then /test then /commit) in scripts or CI/CD pipelines"],"best_for":["developers who prefer CLI-style interfaces over GUI menus","teams building CI/CD pipelines that need to invoke AI workflows programmatically","organizations standardizing on a consistent command vocabulary across Claude Code and Cursor"],"limitations":["Commands are synchronous — no built-in support for long-running commands with progress reporting","Command arguments are positional or flag-based — no support for complex nested arguments","No built-in command history or undo — commands execute immediately without confirmation","Command discovery is manual (requires /help or documentation) — no auto-complete in all editors"],"requires":["Claude Code or Cursor with slash command support","Understanding of command syntax and arguments"],"input_types":["slash command string (e.g., '/develop --feature=auth')","command arguments (flags, positional args)","context from current editor state (selected code, open files)"],"output_types":["command execution result (success/failure)","command output (generated code, test results, commit message)","command logs and telemetry"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_7","uri":"capability://tool.use.integration.cross.platform.agent.compatibility.with.plugin.manifests","name":"cross-platform agent compatibility with plugin manifests","description":"Supports 32+ AI agents (Claude Code, Cursor, Gemini, etc.) through a unified plugin manifest system. Each agent has a plugin.json manifest that defines how Pro Workflow integrates with that agent's API and UI. Manifests specify command routing, context injection points, hook integration, and UI elements. The system abstracts away agent-specific differences, allowing the same workflow definition to run on multiple agents without modification.","intents":["I want to use Pro Workflow with multiple AI agents (Claude Code, Cursor, Gemini) without rewriting my workflows","I need to ensure my coding standards and corrections are consistent across different agents","I want to migrate from one agent to another without losing my workflow configuration"],"best_for":["organizations evaluating multiple AI agents and want to avoid vendor lock-in","teams using different agents for different tasks (e.g., Claude Code for implementation, Cursor for refactoring)","developers who want to switch agents without reconfiguring their entire workflow"],"limitations":["Agent compatibility is limited to agents that support plugin manifests — proprietary agents without plugin APIs are not supported","Feature parity across agents is not guaranteed — some agents may not support all Pro Workflow features (e.g., hooks, worktrees)","Agent-specific optimizations (e.g., omitClaudeMd for Claude) may not apply to other agents","Cross-agent context sharing is limited — each agent maintains its own session state"],"requires":["Agent with plugin manifest support (Claude Code, Cursor, Gemini, etc.)","Plugin manifest file (.claude-plugin/plugin.json or .cursor-plugin/plugin.json)","Agent API key or authentication"],"input_types":["plugin.json manifest with agent-specific configuration","agent API schema (command definitions, context injection points)","workflow configuration (commands, agents, skills)"],"output_types":["agent-specific command routing","agent-specific context injection","agent compatibility report (supported features, limitations)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_8","uri":"capability://safety.moderation.quality.gate.enforcement.with.automated.testing.and.review.agents","name":"quality gate enforcement with automated testing and review agents","description":"Implements Quality & Review agents that enforce automated testing, linting, and code review gates before code is committed. Agents can run test suites, check code coverage, scan for security vulnerabilities, and generate review comments. Gates are configurable (can be warnings or hard blocks) and can be skipped with explicit override commands. Integrates with git hooks to prevent commits that fail quality gates unless explicitly approved.","intents":["I want to automatically run tests and linting before committing code without manual intervention","I need to enforce minimum code coverage or security standards before code reaches production","I want to generate code review comments automatically and flag them for human review"],"best_for":["teams with strict quality standards who want to enforce them automatically","projects with expensive test suites that need to run before every commit","organizations with security or compliance requirements that need automated enforcement"],"limitations":["Quality gates are agent-driven (AI-powered) — may miss edge cases that static analysis tools catch","Test execution is limited to tests that can run in the agent's environment — integration tests or tests requiring external services may fail","Code review comments are AI-generated — may have false positives or miss subtle issues","Gate overrides are logged but not enforced — developers can bypass gates if they choose"],"requires":["Test suite configured and runnable in the agent's environment","Linting tools (ESLint, Prettier, etc.) installed and configured","Security scanning tools (e.g., npm audit, Snyk) available"],"input_types":["code changes (git diff or file content)","test configuration (test runner, coverage thresholds)","quality gate configuration (enabled gates, severity levels)"],"output_types":["test execution results (pass/fail, coverage report)","linting violations and fixes","security scan results","code review comments with severity levels"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-rohitg00--pro-workflow__cap_9","uri":"capability://memory.knowledge.context.mode.files.for.dynamic.context.injection.based.on.task.type","name":"context mode files for dynamic context injection based on task type","description":"Supports context mode files that dynamically inject different context based on the current task or agent role. Context modes can include different rule sets, code examples, architectural diagrams, or reference implementations depending on whether the agent is implementing, reviewing, or refactoring code. Modes are selected automatically based on the command or can be explicitly set by the developer. 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