{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-affaan-m--everything-claude-code","slug":"affaan-m--everything-claude-code","name":"everything-claude-code","type":"agent","url":"https://ecc.tools","page_url":"https://unfragile.ai/affaan-m--everything-claude-code","categories":["ai-agents","code-review-security","app-builders"],"tags":["ai-agents","anthropic","claude","claude-code","developer-tools","llm","mcp","productivity"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-affaan-m--everything-claude-code__cap_0","uri":"capability://planning.reasoning.multi.agent.orchestration.with.delegation.patterns","name":"multi-agent orchestration with delegation patterns","description":"Implements a hierarchical agent system where multiple specialized agents (Observer, Skill Creator, Evaluator, etc.) coordinate through a central harness using pre/post-tool-use hooks and session-based context passing. Agents delegate subtasks via explicit hand-off patterns defined in agent.yaml, with state synchronized through SQLite-backed session persistence and strategic context window compaction to prevent token overflow during multi-step workflows.","intents":["I want Claude to autonomously break down complex development tasks into sub-agent responsibilities without losing context between steps","I need multiple specialized agents to work together on code review, testing, and optimization in a single session","I want to persist agent state and decisions across multiple IDE interactions without re-explaining the project"],"best_for":["teams building autonomous development workflows with Claude Code or Cursor","developers needing persistent multi-step agent coordination across sessions","organizations implementing research-first development with continuous learning loops"],"limitations":["Hook-based coordination adds latency per agent handoff (~50-200ms per PreToolUse/PostToolUse cycle)","Session compaction strategy requires manual tuning of context window thresholds per project","Agent delegation patterns are tightly coupled to MCP server availability — offline mode degrades gracefully but loses real-time coordination"],"requires":["Claude Code, Cursor, OpenCode, or compatible IDE with MCP support","Node.js 18+ for MCP server runtime","SQLite3 for session persistence layer","agent.yaml configuration with defined agent roles and delegation rules"],"input_types":["agent.yaml configuration","project context (codebase structure, CLAUDE.md)","user intent (natural language task description)"],"output_types":["structured agent decisions (JSON)","session state snapshots (SQLite records)","delegation logs with hook execution traces"],"categories":["planning-reasoning","tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_1","uri":"capability://memory.knowledge.continuous.learning.system.with.instinct.evolution","name":"continuous learning system with instinct evolution","description":"Implements a closed-loop learning pipeline (Continuous Learning v2 Architecture) where an Observer Agent monitors code execution patterns, detects recurring problems, and automatically generates new skills via the Skill Creator. Instincts are structured as pattern-matching rules stored in SQLite, evolved through an evaluation system that tracks skill health metrics, and scoped to individual projects to prevent cross-project interference. The evolution pipeline includes observation → pattern detection → skill generation → evaluation → integration into the active skill set.","intents":["I want Claude to learn from my project's patterns and automatically create reusable skills for recurring problems","I need the agent to detect when a skill is failing and evolve it without manual intervention","I want project-specific learning that doesn't pollute global skill definitions"],"best_for":["long-running projects where development patterns stabilize over time","teams wanting to build institutional knowledge into their AI agent harness","developers using research-first development workflows with eval-driven iteration"],"limitations":["Skill evolution requires minimum observation window (typically 5-10 pattern occurrences) before generating new skills, causing initial cold-start delay","Pattern detection relies on heuristic matching — complex domain-specific patterns may require manual skill definition","Project-scoped learning increases storage overhead (~5-50MB per project depending on codebase size and session history)","Skill health tracking is based on execution success rates; skills with high variance in effectiveness may oscillate between enabled/disabled states"],"requires":["Continuous Learning v2 configuration enabled in settings","SQLite state store with write access","Observer Agent and Skill Creator Agent installed","Evaluation System configured with success/failure metrics"],"input_types":["execution traces (tool use logs)","code patterns (AST analysis or regex-based detection)","project context (CLAUDE.md, codebase structure)"],"output_types":["generated skill definitions (SKILL.md format)","instinct structures (pattern → action mappings)","skill health metrics (success rate, latency, token cost)"],"categories":["memory-knowledge","planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_10","uri":"capability://automation.workflow.checkpoint.and.verification.workflow.with.rollback.capability","name":"checkpoint and verification workflow with rollback capability","description":"Provides a Checkpoint & Verification Workflow that creates savepoints of project state at key milestones, verifies code quality and functionality at each checkpoint, and enables rollback to previous checkpoints if verification fails. Checkpoints are stored in session state with full context snapshots, and verification uses the Plankton Code Quality System and Evaluation System to assess quality. The workflow integrates with version control to track checkpoint history.","intents":["I want to create savepoints of my project at key milestones and verify quality before proceeding","I need to rollback to a previous checkpoint if Claude's changes break something","I want to track the history of checkpoints and understand what changed between them"],"best_for":["teams working on critical systems where rollback capability is essential","developers wanting to verify quality at key milestones before committing changes","organizations implementing staged development with quality gates"],"limitations":["Checkpoint storage adds overhead (~5-50MB per checkpoint depending on codebase size)","Verification at each checkpoint adds latency (1-5 seconds per checkpoint) to development workflow","Rollback capability requires version control integration; projects without version control lose rollback history","Checkpoint snapshots are point-in-time; concurrent changes from other developers may conflict with rollback"],"requires":["Checkpoint & Verification Workflow configured in agent.yaml","Version control integration (Git, etc.) for checkpoint tracking","Plankton Code Quality System for structural verification","Evaluation System for quality metrics"],"input_types":["checkpoint trigger (manual or automatic at milestones)","project state (full codebase snapshot)","verification criteria (quality thresholds, test requirements)"],"output_types":["checkpoint records (state snapshots with metadata)","verification results (pass/fail per checkpoint)","rollback logs (changes reverted when rolling back)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_11","uri":"capability://planning.reasoning.autonomous.loop.patterns.with.self.directed.task.execution","name":"autonomous loop patterns with self-directed task execution","description":"Implements Autonomous Loop Patterns that enable agents to self-direct task execution without human intervention, using the planning-reasoning system to decompose tasks, execute them through agent delegation, and verify results through evaluation. Loops can be configured with termination conditions (max iterations, success criteria, token budget) and include safeguards to prevent infinite loops. The Observer Agent monitors loop execution and feeds patterns into continuous learning.","intents":["I want Claude to autonomously complete complex tasks by breaking them into subtasks and executing them without asking for help","I need safeguards to prevent infinite loops or runaway execution","I want to monitor autonomous execution and learn from patterns to improve future loops"],"best_for":["teams implementing fully autonomous development workflows","developers wanting to delegate complex tasks to agents with minimal oversight","organizations with high trust in agent capabilities and strong safety infrastructure"],"limitations":["Autonomous loops are unpredictable — agent behavior may diverge from intended path due to reasoning errors or hallucinations","Termination conditions are heuristic-based; loops may terminate prematurely or continue past intended stopping point","Token budget constraints may force incomplete task execution; large tasks may exceed budget before completion","Debugging autonomous loops is difficult — multi-step execution makes it hard to identify where things went wrong"],"requires":["Autonomous Loop Patterns configured in agent.yaml with termination conditions","Planning-Reasoning system for task decomposition","Agent Delegation & Orchestration for subtask execution","Evaluation System for result verification","Safety guardrails and monitoring infrastructure"],"input_types":["task description (natural language or structured spec)","loop configuration (termination conditions, max iterations, token budget)","safety constraints (forbidden operations, resource limits)"],"output_types":["task execution logs (step-by-step trace of autonomous execution)","final result (completed task output)","loop metrics (iterations, tokens used, success rate)"],"categories":["planning-reasoning","automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_12","uri":"capability://data.processing.analysis.token.optimization.and.context.window.management","name":"token optimization and context window management","description":"Provides Token Optimization Strategies that monitor token usage across agent execution, identify high-cost operations, and apply optimization techniques (context compaction, selective context inclusion, prompt compression) to reduce token consumption. Context Window Management tracks available tokens per platform and automatically adjusts context inclusion strategies to stay within limits. The system includes token budgeting per task and alerts when approaching limits.","intents":["I want to understand where my token budget is being spent and optimize high-cost operations","I need to fit my project context within token limits without losing essential information","I want to set token budgets per task and get alerts when approaching limits"],"best_for":["cost-conscious teams managing large token budgets across many projects","developers working with large codebases that exceed context window limits","organizations implementing token budgeting and cost controls"],"limitations":["Token optimization is lossy — context compaction and selective inclusion may discard important information","Token counting is approximate; actual token usage may differ from estimates due to tokenizer variations","Optimization strategies are heuristic-based; they may not find optimal solutions for complex context scenarios","Token budgeting requires manual tuning per task type; one-size-fits-all budgets may be too restrictive or too generous"],"requires":["Token Optimization Strategies configured in settings","Context Window Management enabled","Token counting library (tiktoken or equivalent) for accurate estimates","Project-specific token budget configuration"],"input_types":["agent execution traces (tool use logs with token counts)","context snapshots (conversation history, project context)","platform constraints (context window size per platform)"],"output_types":["token usage reports (breakdown by operation type)","optimization recommendations (specific techniques to apply)","context window utilization metrics (percentage of available tokens used)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_13","uri":"capability://tool.use.integration.package.manager.system.for.skill.and.rule.distribution","name":"package manager system for skill and rule distribution","description":"Implements a Package Manager System that enables installation, versioning, and distribution of skills, rules, and commands as packages. Packages are defined in manifest files (install-modules.json) with dependency specifications, and the package manager handles dependency resolution, conflict detection, and selective installation. Packages can be installed from local directories, Git repositories, or package registries, and the system tracks installed versions for reproducibility.","intents":["I want to share skills and rules across my team via a package manager","I need to manage dependencies between skills and ensure compatible versions are installed","I want to install only the skills I need for my project without bloating the configuration"],"best_for":["teams building shared skill libraries and wanting to distribute them","organizations managing multiple projects with different skill requirements","developers wanting reproducible skill configurations across machines"],"limitations":["Package manager adds complexity to skill management; dependency resolution can be slow for large dependency graphs","No built-in package registry — teams must host packages on Git or local servers","Dependency conflicts are detected but not automatically resolved; manual intervention may be required","Package versioning is semantic versioning based; pre-release versions may be unstable"],"requires":["Package Manager System configured in settings","Package manifests (install-modules.json) with dependency specifications","Git or local package repositories for package hosting","Node.js 18+ for package manager runtime"],"input_types":["package manifests (JSON with package specs and dependencies)","package sources (Git URLs, local paths, registry URLs)","installation requests (package names and versions)"],"output_types":["installed packages (skills, rules, commands in project directory)","dependency resolution logs (package versions selected)","conflict reports (incompatible package versions)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_14","uri":"capability://data.processing.analysis.project.detection.and.context.inference.system","name":"project detection and context inference system","description":"Automatically detects project type, framework, and structure by analyzing codebase patterns, package manifests, and configuration files. Infers project context (language, framework, testing patterns, coding standards) and uses this to select appropriate skills, rules, and commands. The system maintains a project detection cache to avoid repeated analysis and integrates with the CLAUDE.md context file for explicit project metadata.","intents":["I want Claude to automatically understand my project type and apply appropriate skills without manual configuration","I need Claude to infer my coding standards and testing patterns from my codebase","I want to avoid manually specifying project context in every session"],"best_for":["developers working across multiple projects with different tech stacks","teams wanting zero-configuration setup where Claude auto-detects project context","organizations with standardized project structures that enable reliable detection"],"limitations":["Project detection is heuristic-based and may misidentify projects with non-standard structures","Detection accuracy depends on presence of standard configuration files (package.json, requirements.txt, etc.); projects with custom structures may not be detected correctly","Inferred context may be incomplete or incorrect for hybrid projects (e.g., monorepos with multiple frameworks)","Detection cache may become stale if project structure changes; manual cache invalidation may be required"],"requires":["Project Detection System enabled in settings","Standard project configuration files (package.json, requirements.txt, Gemfile, etc.)","CLAUDE.md file for explicit project metadata (optional but recommended)","Codebase with detectable patterns (directory structure, file naming conventions)"],"input_types":["project directory (root path)","configuration files (package.json, requirements.txt, etc.)","codebase patterns (directory structure, file extensions)"],"output_types":["detected project type (string: 'nodejs', 'python', 'ruby', etc.)","detected framework (string: 'react', 'django', 'rails', etc.)","inferred context (JSON with language, framework, testing patterns, coding standards)","recommended skills (list of skills appropriate for detected project)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_15","uri":"capability://code.generation.editing.plankton.code.quality.system.with.structural.analysis","name":"plankton code quality system with structural analysis","description":"Integrates the Plankton Code Quality System for structural analysis of generated code using language-specific parsers (tree-sitter for 40+ languages) instead of regex-based matching. Provides metrics for code complexity, maintainability, test coverage, and style violations. Plankton integrates with the Evaluation System to track code quality trends and with the Skill Creator to generate quality-focused skills.","intents":["I want accurate code quality metrics based on structural analysis, not just regex patterns","I need to track code quality trends over time and identify regressions","I want to generate skills that improve code quality based on structural analysis"],"best_for":["teams with high code quality standards requiring accurate structural analysis","developers wanting to track code quality metrics across multiple projects","organizations implementing quality-driven development with quantitative metrics"],"limitations":["Plankton requires language-specific parsers; unsupported languages fall back to regex-based analysis with reduced accuracy","Structural analysis adds latency (100-500ms per analysis) to code review workflows","Metrics are based on static analysis; runtime quality issues (performance, memory leaks) are not detected","Quality thresholds are project-specific; one-size-fits-all thresholds may be inappropriate for different project types"],"requires":["Plankton Code Quality System installed and configured","Language-specific parsers (tree-sitter or equivalent) for target languages","Code quality metric definitions (complexity thresholds, coverage targets, etc.)","Integration with Evaluation System for metric tracking"],"input_types":["source code (full file or code snippet)","language identifier (string: 'javascript', 'python', 'rust', etc.)","quality thresholds (JSON with metric targets)"],"output_types":["code quality metrics (complexity, maintainability, coverage, style violations)","quality report (structured JSON with findings and recommendations)","quality trends (historical metrics for tracking improvement)"],"categories":["code-generation-editing","data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_16","uri":"capability://automation.workflow.tmux.worktree.orchestration.for.parallel.development","name":"tmux worktree orchestration for parallel development","description":"Provides Tmux Worktree Orchestration that enables parallel development across multiple Git worktrees using tmux sessions for isolation. Agents can spawn parallel tasks in separate worktrees, coordinate changes across worktrees, and merge results back to main branch. The system manages worktree lifecycle (creation, cleanup) and provides coordination primitives for synchronizing parallel execution.","intents":["I want Claude to work on multiple features in parallel using Git worktrees without conflicts","I need to coordinate changes across parallel worktrees and merge them back safely","I want to isolate experimental changes in separate worktrees while keeping main branch stable"],"best_for":["teams implementing parallel development workflows with multiple features in progress","developers wanting to experiment with changes in isolation without affecting main branch","organizations with complex merge workflows requiring careful coordination"],"limitations":["Tmux worktree orchestration requires Git and tmux to be installed and configured","Parallel execution increases complexity; merge conflicts may occur if worktrees modify overlapping code","Worktree cleanup is manual; orphaned worktrees may accumulate if cleanup fails","Coordination across worktrees adds latency (1-5 seconds per coordination point) to development workflow"],"requires":["Git with worktree support (Git 2.15+)","tmux for session management","Tmux Worktree Orchestration configured in agent.yaml","Project with Git repository initialized"],"input_types":["feature descriptions (natural language or structured specs)","worktree configuration (number of parallel worktrees, merge strategy)","coordination rules (conflict resolution, merge order)"],"output_types":["worktree creation logs (worktree names and branches)","parallel execution traces (per-worktree execution logs)","merge results (conflicts, successful merges, final state)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_17","uri":"capability://tool.use.integration.jira.integration.for.task.management.and.tracking","name":"jira integration for task management and tracking","description":"Provides JIRA Integration that enables agents to read tasks from JIRA, update task status as work progresses, and link code changes to JIRA issues. The integration includes a JIRA command for manual task management and automatic status updates based on agent execution results. Session state tracks JIRA issue context for multi-session continuity.","intents":["I want Claude to automatically pull tasks from JIRA and update status as work progresses","I need to link code changes to JIRA issues for traceability","I want to maintain context about JIRA issues across multiple IDE sessions"],"best_for":["teams using JIRA for task management and wanting AI integration","organizations requiring traceability between code changes and JIRA issues","developers wanting automated task status updates without manual intervention"],"limitations":["JIRA integration requires API credentials and network access to JIRA server","Status updates are based on agent execution results; incorrect status updates may occur if agent behavior diverges from expected","JIRA API rate limits may throttle status updates for high-volume workflows","Integration is read-only for task retrieval; complex JIRA workflows may not be fully supported"],"requires":["JIRA Integration configured in settings","JIRA API credentials (username and API token)","JIRA server URL and project key","Network access to JIRA server"],"input_types":["JIRA issue key (string: 'PROJ-123')","task description (from JIRA issue)","execution results (agent output for status update)"],"output_types":["task list (JIRA issues assigned to current user)","task context (issue description, acceptance criteria, linked issues)","status updates (logged to JIRA issue)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_2","uri":"capability://tool.use.integration.hook.based.tool.use.interception.and.transformation","name":"hook-based tool-use interception and transformation","description":"Provides a middleware layer for intercepting and transforming tool calls through PreToolUse and PostToolUse hooks that execute before/after any MCP tool invocation. Hooks are registered globally or per-session, support async execution, can modify tool arguments or results, and integrate with the hook testing infrastructure for validation. The hook lifecycle is tied to session events (session start, context refresh, tool execution) enabling fine-grained control over agent behavior without modifying core agent logic.","intents":["I want to validate or sanitize tool arguments before Claude executes them (e.g., prevent dangerous shell commands)","I need to transform tool results before Claude sees them (e.g., filter sensitive data, normalize output format)","I want to inject custom logic into the agent execution pipeline without forking the codebase"],"best_for":["security-conscious teams implementing guardrails and content filtering","developers building custom agent behaviors through middleware patterns","organizations needing audit trails or logging of all tool executions"],"limitations":["Hook execution is synchronous in the critical path — slow hooks (>100ms) directly impact agent latency","Hook ordering is not guaranteed when multiple hooks target the same tool; conflicts must be resolved manually","Hook state is session-scoped; cross-session hook coordination requires external state store","No built-in hook composition or conflict resolution — overlapping hooks may produce unexpected results"],"requires":["Hook definitions in project configuration or agent.yaml","JavaScript/TypeScript runtime for hook execution","MCP server with tool-use event emission support","Session context available at hook execution time"],"input_types":["tool name (string)","tool arguments (JSON object)","tool result (any type)","session context (metadata)"],"output_types":["modified tool arguments (JSON object)","modified tool result (any type)","hook execution logs (structured data)"],"categories":["tool-use-integration","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_3","uri":"capability://memory.knowledge.session.persistence.and.strategic.context.compaction","name":"session persistence and strategic context compaction","description":"Manages agent state across IDE sessions using SQLite-backed session files with structured JSON payloads, session aliases for easy recall, and a strategic compaction algorithm that summarizes old context to fit within token budgets. Sessions track conversation history, tool execution logs, agent decisions, and learned instincts. The Session Manager API provides CRUD operations, and session adapters enable format conversion for different platforms (Claude Code, Cursor, OpenCode). Compaction uses heuristics to identify low-value context (verbose logs, redundant explanations) and replace with summaries.","intents":["I want to close my IDE and resume work tomorrow without re-explaining the project context to Claude","I need to manage multiple parallel development sessions without context bleeding between them","I want to keep token usage under control while preserving essential project knowledge across long-running sessions"],"best_for":["developers working on long-running projects with multiple IDE sessions per day","teams needing persistent context across different team members' machines","organizations with strict token budgets requiring aggressive context optimization"],"limitations":["Strategic compaction is lossy — verbose execution traces and intermediate reasoning are discarded, potentially losing debugging information","Session file size grows linearly with conversation length; projects with >10k messages may see 50-200MB session files","Session aliases are user-scoped; no built-in sharing mechanism for team-based session collaboration","Compaction heuristics are rule-based and may incorrectly identify important context as low-value in domain-specific workflows"],"requires":["SQLite3 database with write access to project directory","Session Manager API configured in settings","Project-specific session configuration (retention policies, compaction thresholds)","IDE integration with session lifecycle hooks (on close, on resume)"],"input_types":["conversation history (message objects)","tool execution logs (structured traces)","agent decisions (JSON metadata)","instinct records (pattern definitions)"],"output_types":["session files (SQLite records with JSON payloads)","session aliases (string → session ID mappings)","compaction reports (summarization logs)"],"categories":["memory-knowledge","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_4","uri":"capability://memory.knowledge.skill.system.with.modular.capability.definitions","name":"skill system with modular capability definitions","description":"Defines reusable agent capabilities as discrete skill modules (SKILL.md format) that encapsulate domain knowledge, code patterns, testing strategies, and verification procedures. Skills are installed via a package manager system, can be enabled/disabled per project, and include metadata for skill health tracking (success rate, token cost, latency). The Skill Creator Agent generates new skills from observed patterns, and the Evaluation System tracks skill effectiveness. Skills integrate with the continuous learning pipeline to evolve based on execution feedback.","intents":["I want to package common development patterns (e.g., Django verification, backend patterns) as reusable skills that Claude can apply to new projects","I need to enable/disable skills per project without modifying agent configuration","I want to track which skills are actually helping vs. hurting performance and evolve them accordingly"],"best_for":["teams building domain-specific AI agent capabilities (e.g., framework-specific skills for Django, FastAPI)","organizations wanting to share learned patterns across projects via skill packages","developers implementing skill-driven development workflows with eval-driven iteration"],"limitations":["Skill definitions are static SKILL.md files; runtime skill modification requires skill regeneration and re-installation","No built-in skill versioning — multiple versions of the same skill must be managed manually","Skill dependencies are not explicitly declared; circular dependencies or missing prerequisites must be caught during testing","Skill health metrics are based on execution success rates; skills with high variance in effectiveness may be incorrectly disabled"],"requires":["Skill Creator Agent for generating new skills","Package Manager System for installing/managing skill packages","Evaluation System for tracking skill health metrics","Project-specific skill configuration (enabled skills, skill parameters)"],"input_types":["skill definitions (SKILL.md format with metadata)","execution traces (tool use logs for skill effectiveness)","project context (codebase patterns, framework type)"],"output_types":["skill modules (installed in skills/ directory)","skill health metrics (success rate, token cost, latency)","skill evolution logs (changes to skill definitions over time)"],"categories":["memory-knowledge","code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_5","uri":"capability://code.generation.editing.code.review.and.security.workflow.automation","name":"code review and security workflow automation","description":"Provides a specialized workflow (Code Review & Security Workflow) that combines multiple agents and skills to perform automated code review, security scanning, and quality verification. Integrates with the Plankton Code Quality System for structural analysis, uses PreToolUse hooks for security guardrails (preventing dangerous operations), and leverages the Evaluation System to track review effectiveness. The workflow can be triggered manually or as part of autonomous loops, with results persisted to session state for audit trails.","intents":["I want Claude to automatically review my code changes for security vulnerabilities, style violations, and architectural issues","I need to enforce security guardrails that prevent Claude from executing dangerous commands or accessing sensitive data","I want audit trails of all code review decisions for compliance and debugging purposes"],"best_for":["security-conscious teams implementing automated code review gates","organizations with compliance requirements (audit trails, review documentation)","developers using research-first development with continuous quality verification"],"limitations":["Security guardrails are rule-based and may have false positives/negatives for domain-specific dangerous operations","Code review effectiveness depends on skill quality — poorly-tuned review skills may produce noisy or irrelevant feedback","Plankton Code Quality System requires language-specific parsers; unsupported languages fall back to regex-based analysis with reduced accuracy","Review workflow adds latency (~1-5 seconds per review cycle) due to multi-agent coordination and hook execution"],"requires":["Code Review & Security Workflow configured in agent.yaml","Security guardrail hooks installed and configured","Plankton Code Quality System with language support for target codebase","Review skills (code-review, security-scanning, quality-verification) installed"],"input_types":["code changes (diff format or full file content)","project context (codebase structure, coding standards)","security policies (guardrail rules, forbidden operations)"],"output_types":["review findings (structured JSON with severity levels)","security alerts (violations of guardrail rules)","quality metrics (code complexity, test coverage, style violations)","audit logs (review decisions with timestamps and reasoning)"],"categories":["code-generation-editing","safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_6","uri":"capability://tool.use.integration.cross.platform.ide.integration.with.platform.specific.skills","name":"cross-platform ide integration with platform-specific skills","description":"Provides unified agent harness across multiple IDE platforms (Claude Code, Cursor, OpenCode, Codex CLI, Kiro IDE) through a platform abstraction layer that normalizes MCP server configuration, tool availability, and context window constraints. Platform-specific skills adapt to IDE capabilities (e.g., Swift 6.2 Concurrency for Kiro, FoundationModels for on-device LLM support). Session adapters enable session portability across platforms, and platform feature comparison documentation guides skill selection per platform.","intents":["I want to use the same agent harness across Claude Code, Cursor, and OpenCode without reconfiguring skills and rules","I need platform-specific optimizations (e.g., leveraging Cursor's native features) without maintaining separate configurations","I want to migrate sessions between IDEs without losing context or learned instincts"],"best_for":["teams using multiple IDE platforms and wanting consistent agent behavior across them","developers leveraging platform-specific features (e.g., Cursor's native code search) while maintaining portable configurations","organizations migrating between IDEs and needing session portability"],"limitations":["Platform abstraction layer adds complexity; platform-specific bugs may be difficult to diagnose due to abstraction overhead","Not all skills are available on all platforms — platform-specific skills (e.g., Swift 6.2 Concurrency) are unavailable on non-native platforms","Context window constraints vary by platform; sessions optimized for one platform may exceed token limits on another","Session adapters are lossy for some platform-specific features — migrating from Cursor to Claude Code may lose Cursor-native capabilities"],"requires":["IDE with MCP server support (Claude Code 1.0+, Cursor 0.40+, OpenCode 1.0+, Codex CLI 2.0+)","Platform-specific MCP server configuration in settings","Session adapters installed for target platforms","Platform feature comparison documentation for skill selection"],"input_types":["platform identifier (string: 'claude-code', 'cursor', 'opencode', 'codex-cli', 'kiro')","platform capabilities (JSON schema of available tools and features)","session data (for cross-platform migration)"],"output_types":["normalized MCP configuration (platform-agnostic)","platform-specific skill recommendations (JSON)","adapted session state (platform-specific format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_7","uri":"capability://planning.reasoning.eval.driven.development.workflow.with.automated.testing","name":"eval-driven development workflow with automated testing","description":"Implements an Eval-Driven Development Workflow that uses the Evaluation System to define success metrics, automatically generate test cases, and measure agent performance against those metrics. The workflow integrates with the Skill Creator to generate new skills based on eval results, and uses the Plankton Code Quality System for structural verification. Evals are defined as YAML configurations with input/output specifications, and results are persisted to session state for tracking improvement over time.","intents":["I want to define success metrics for my development tasks and have Claude automatically measure progress against them","I need to generate test cases automatically and verify that Claude's solutions meet quality standards","I want to track how well my skills are performing and evolve them based on eval results"],"best_for":["research-first development teams using eval-driven iteration","developers implementing continuous improvement loops for agent skills","organizations measuring AI agent effectiveness with quantitative metrics"],"limitations":["Eval definition requires manual specification of success metrics — complex domain-specific metrics may be difficult to formalize","Eval execution is expensive (multiple test cases × multiple agent runs); large eval suites may consume significant token budgets","Eval results are sensitive to randomness in agent behavior — flaky evals may produce inconsistent results","No built-in statistical significance testing — small sample sizes may produce misleading improvement signals"],"requires":["Evaluation System configured with eval definitions (YAML format)","Test case generation enabled (via Skill Creator or manual definition)","Plankton Code Quality System for structural verification","Token budget for eval execution (typically 10-100x normal usage)"],"input_types":["eval definitions (YAML with input/output specs and success metrics)","test cases (generated or manually defined)","agent output (code, text, structured data)"],"output_types":["eval results (pass/fail per test case, quantitative metrics)","skill health metrics (success rate, token cost, latency)","improvement reports (before/after comparisons)"],"categories":["planning-reasoning","automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_8","uri":"capability://search.retrieval.research.first.development.workflow.with.search.first.patterns","name":"research-first development workflow with search-first patterns","description":"Provides a Search-First Workflow that prioritizes information retrieval and analysis before code generation, using iterative retrieval patterns to gather context from documentation, codebase, and external sources. The workflow integrates with memory systems (RAG, knowledge bases) to retrieve relevant patterns before generating code, reducing hallucinations and improving code quality. Iterative retrieval allows Claude to refine searches based on initial results, enabling multi-step research workflows.","intents":["I want Claude to research the problem space before writing code, reducing hallucinations and improving solution quality","I need Claude to find relevant examples and patterns from my codebase before generating new code","I want to enable multi-step research workflows where Claude refines searches based on initial findings"],"best_for":["teams prioritizing code quality and correctness over speed","developers working with large, complex codebases where pattern discovery is critical","organizations implementing research-first development methodologies"],"limitations":["Search-first approach adds latency (1-5 seconds per research step) before code generation begins","Iterative retrieval requires multiple search rounds; poorly-tuned search queries may waste tokens on irrelevant results","RAG effectiveness depends on knowledge base quality and indexing strategy — incomplete or stale knowledge bases produce poor retrieval results","Research workflows may retrieve outdated patterns or anti-patterns if knowledge base is not actively maintained"],"requires":["Search-First Workflow configured in agent.yaml","Memory system (RAG, knowledge base, or vector store) with indexed codebase and documentation","Iterative Retrieval skill installed","Project context (CLAUDE.md, codebase structure) for search grounding"],"input_types":["user intent (natural language task description)","search queries (generated or user-provided)","knowledge base (indexed codebase, documentation, patterns)"],"output_types":["retrieved context (relevant code examples, documentation excerpts)","research findings (structured analysis of retrieved patterns)","generated code (informed by research results)"],"categories":["search-retrieval","memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__cap_9","uri":"capability://code.generation.editing.test.driven.development.workflow.with.automated.test.generation","name":"test-driven development workflow with automated test generation","description":"Implements a Test-Driven Development Workflow that generates test cases before code implementation, uses the Evaluation System to verify test quality, and integrates with the Plankton Code Quality System for structural validation. Tests are generated based on requirements and project patterns, and the workflow ensures code passes tests before considering tasks complete. Test generation leverages learned patterns from the continuous learning system.","intents":["I want Claude to write tests before implementing features, ensuring code quality from the start","I need automated test generation based on my project's testing patterns and conventions","I want to verify that generated code passes all tests before accepting it"],"best_for":["teams practicing test-driven development (TDD) with AI assistance","projects with high quality requirements and comprehensive test coverage expectations","developers wanting to enforce testing discipline through automated workflows"],"limitations":["Test generation quality depends on project's existing test patterns — projects with weak test coverage produce weak generated tests","Generated tests may have false positives/negatives if they don't accurately reflect requirements","Test execution adds latency (1-10 seconds per test suite) before code acceptance","Complex test scenarios (integration tests, performance tests) are difficult to generate automatically"],"requires":["Test-Driven Development Workflow configured in agent.yaml","Test generation skill installed (language-specific)","Plankton Code Quality System for test structure validation","Project testing framework configured (Jest, pytest, etc.)"],"input_types":["requirements (natural language or structured specs)","project patterns (existing test examples)","implementation code (for test execution)"],"output_types":["generated test cases (code in project's testing framework)","test execution results (pass/fail per test)","test quality metrics (coverage, assertion count, complexity)"],"categories":["code-generation-editing","automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-affaan-m--everything-claude-code__headline","uri":"capability://tool.use.integration.ai.performance.optimization.system.for.coding.agents","name":"ai performance optimization system for coding agents","description":"Everything Claude Code is a comprehensive performance optimization system designed for AI coding agents, enhancing their skills, memory, and security while ensuring efficient code quality and token usage.","intents":["best AI performance optimization system","AI coding agent for enhanced productivity","performance optimization for Claude Code","MCP server for AI agents","AI agent with memory and security features"],"best_for":[],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":61,"verified":false,"data_access_risk":"high","permissions":["Claude Code, Cursor, OpenCode, or compatible IDE with MCP support","Node.js 18+ for MCP server runtime","SQLite3 for session persistence layer","agent.yaml configuration with defined agent roles and delegation rules","Continuous Learning v2 configuration enabled in settings","SQLite state store with write access","Observer Agent and Skill Creator Agent installed","Evaluation System configured with success/failure metrics","Checkpoint & Verification Workflow configured in agent.yaml","Version control integration (Git, etc.) for checkpoint tracking"],"failure_modes":["Hook-based coordination adds latency per agent handoff (~50-200ms per PreToolUse/PostToolUse cycle)","Session compaction strategy requires manual tuning of context window thresholds per project","Agent delegation patterns are tightly coupled to MCP server availability — offline mode degrades gracefully but loses real-time coordination","Skill evolution requires minimum observation window (typically 5-10 pattern occurrences) before generating new skills, causing initial cold-start delay","Pattern detection relies on heuristic matching — complex domain-specific patterns may require manual skill definition","Project-scoped learning increases storage overhead (~5-50MB per project depending on codebase size and session history)","Skill health tracking is based on execution success rates; skills with high variance in effectiveness may oscillate between enabled/disabled states","Checkpoint storage adds overhead (~5-50MB per checkpoint depending on codebase size)","Verification at each checkpoint adds latency (1-5 seconds per checkpoint) to development workflow","Rollback capability requires version control integration; projects without version control lose rollback history","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.9691014916088856,"quality":0.5,"ecosystem":0.8,"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":"2026-05-24T12:16:21.549Z","last_scraped_at":"2026-05-03T13:57:01.478Z","last_commit":"2026-05-03T04:53:48Z"},"community":{"stars":172425,"forks":26718,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=affaan-m--everything-claude-code","compare_url":"https://unfragile.ai/compare?artifact=affaan-m--everything-claude-code"}},"signature":"XC/90iKHGonW936QNWmgybaebRucSZphNrijdYCZ6Apn1gcVZPMhXtDaXv20m2bF7Ng/NqZSs7M8HJWYMqEUAQ==","signedAt":"2026-06-20T11:25:53.154Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/affaan-m--everything-claude-code","artifact":"https://unfragile.ai/affaan-m--everything-claude-code","verify":"https://unfragile.ai/api/v1/verify?slug=affaan-m--everything-claude-code","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"}}