Reduce frontmatter descriptions (#293)

* Fix: Reduce all description fields in frontmatter to less than 50 tokens

* Feat: Update agent versions to v2.1.0

Author: laywill

.claude-plugin/marketplace.json

@@ -1,7 +1,7 @@
 {
   "metadata": {
     "description": "Curated collection of 179+ specialized Claude Code subagents organized into 25 focused categories with risk-tiered structure",
-    "version": "2.0.0"
+    "version": "2.1.0"
   },
   "name": "laywill-subagents",
   "owner": {
@@ -30,7 +30,7 @@
       ],
       "name": "laywill-meta-orchestration",
       "source": "./categories/00-meta-and-orchestration",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "research",
@@ -49,7 +49,7 @@
       ],
       "name": "laywill-research-discovery",
       "source": "./categories/01-research-and-discovery",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "architecture",
@@ -68,7 +68,7 @@
       ],
       "name": "laywill-architecture-design",
       "source": "./categories/02-architecture-and-design",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "quality",
@@ -87,7 +87,7 @@
       ],
       "name": "laywill-analysis-review",
       "source": "./categories/03-analysis-and-review",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "documentation",
@@ -104,7 +104,7 @@
       ],
       "name": "laywill-documentation",
       "source": "./categories/04-documentation",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "planning",
@@ -121,7 +121,7 @@
       ],
       "name": "laywill-planning-estimation",
       "source": "./categories/05-planning-and-estimation",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "business",
@@ -142,7 +142,7 @@
       ],
       "name": "laywill-business-product",
       "source": "./categories/06-business-and-product",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "development",
@@ -181,7 +181,7 @@
       ],
       "name": "laywill-language-specialists",
       "source": "./categories/07-language-and-framework-specialists",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "development",
@@ -200,7 +200,7 @@
       ],
       "name": "laywill-general-development",
       "source": "./categories/08-general-development",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "quality",
@@ -217,7 +217,7 @@
       ],
       "name": "laywill-testing-qa",
       "source": "./categories/09-testing-and-qa",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "development",
@@ -233,7 +233,7 @@
       ],
       "name": "laywill-refactoring-modernization",
       "source": "./categories/10-refactoring-and-modernization",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "quality",
@@ -249,7 +249,7 @@
       ],
       "name": "laywill-bug-fixing-debugging",
       "source": "./categories/11-bug-fixing-and-debugging",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "development",
@@ -267,7 +267,7 @@
       ],
       "name": "laywill-frontend-ui",
       "source": "./categories/12-frontend-and-ui",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "tooling",
@@ -283,7 +283,7 @@
       ],
       "name": "laywill-developer-experience",
       "source": "./categories/13-developer-experience-and-tooling",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "data",
@@ -301,7 +301,7 @@
       ],
       "name": "laywill-data-database",
       "source": "./categories/14-data-and-database",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "data",
@@ -318,7 +318,7 @@
       ],
       "name": "laywill-data-science-ai",
       "source": "./categories/15-data-science-and-ai",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "tooling",
@@ -333,7 +333,7 @@
       ],
       "name": "laywill-dependency-management",
       "source": "./categories/16-dependency-and-package-management",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "infrastructure",
@@ -350,7 +350,7 @@
       ],
       "name": "laywill-build-ci-cd",
       "source": "./categories/17-build-and-ci-cd",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "infrastructure",
@@ -366,7 +366,7 @@
       ],
       "name": "laywill-api-service-integration",
       "source": "./categories/18-api-and-service-integration",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "infrastructure",
@@ -389,7 +389,7 @@
       ],
       "name": "laywill-infrastructure-code",
       "source": "./categories/19-infrastructure-as-code",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "security",
@@ -407,7 +407,7 @@
       ],
       "name": "laywill-security-secrets",
       "source": "./categories/20-security-and-secrets",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "specialized",
@@ -423,7 +423,7 @@
       ],
       "name": "laywill-specialized-domains",
       "source": "./categories/21-specialized-domains",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "infrastructure",
@@ -439,7 +439,7 @@
       ],
       "name": "laywill-deployment-release",
       "source": "./categories/22-deployment-and-release",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "infrastructure",
@@ -458,7 +458,7 @@
       ],
       "name": "laywill-production-ops",
       "source": "./categories/23-production-ops-and-observability",
-      "version": "2.0.0"
+      "version": "2.1.0"
     },
     {
       "category": "data",
@@ -474,7 +474,7 @@
       ],
       "name": "laywill-production-data-ops",
       "source": "./categories/24-production-data-ops",
-      "version": "2.0.0"
+      "version": "2.1.0"
     }
   ]
 }

categories/00-meta-and-orchestration/.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "laywill-meta-orchestration",
-  "version": "2.0.0",
+  "version": "2.1.0",
   "description": "Meta and orchestration subagents coordinate and manage other subagents rather than performing work directly. They decompose complex tasks, distribute work to specialists, synthesize outputs, and handle errors across multi-agent workflows.",
   "author": {
     "name": "laywill Community",

categories/00-meta-and-orchestration/agent-installer.md

@@ -1,6 +1,6 @@
 ---
 name: agent-installer
-description: "Use this agent when the user wants to discover, browse, or install Claude Code agents from the awesome-claude-code-subagents repository. Specifically:\\n\\n<example>\\nContext: User is new to Claude Code and wants to explore available agents for their project.\\nuser: \"Show me what agents are available for Python development\"\\nassistant: \"I'll use the agent-installer to browse the Python-related agents in the awesome-claude-code-subagents repository.\"\\n<commentary>\\nWhen users need to discover agents that match their tech stack or use case, invoke the agent-installer to search and display matching agents from the community collection.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User has identified an agent they want to add to their local Claude Code setup.\\nuser: \"I want to install the javascript-pro agent to my .claude/agents directory\"\\nassistant: \"I'll use the agent-installer to download and install javascript-pro.md to your local agents folder.\"\\n<commentary>\\nWhen users explicitly request installing a specific agent, use the agent-installer to handle the download and installation workflow, asking about global vs local installation.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: User is searching for agents matching a specific capability or domain.\\nuser: \"What agents do you have for security testing?\"\\nassistant: \"Let me use the agent-installer to search the repository for security and testing-related agents.\"\\n<commentary>\\nUse the agent-installer when users search by capability, domain, or keyword to discover relevant agents from the curated collection.\\n</commentary>\\n</example>"
+description: "Discovers, browses, and installs Claude Code agents from awesome-claude-code-subagents repository when users search by capability or tech stack."
 tools: Bash, WebFetch, Read, Write, Glob
 model: haiku
 ---

categories/00-meta-and-orchestration/agent-organizer.md

@@ -1,6 +1,6 @@
 ---
 name: agent-organizer
-description: "Use when assembling and optimizing multi-agent teams to execute complex projects that require careful task decomposition, agent capability matching, and workflow coordination. Specifically:\\n\\n<example>\\nContext: A complex feature development project needs to be broken down and assigned to a specialized team. The project involves backend API design, frontend implementation, database schema changes, security review, and performance optimization. Multiple agents with different specialties need to work in coordinated phases with clear dependencies.\\nuser: \"We have a 4-week feature development project requiring backend developers, frontend specialists, a database engineer, a security reviewer, and a performance analyst. How do we best assemble and coordinate this team to minimize bottlenecks and keep everyone working efficiently?\"\\nassistant: \"I'll analyze the project requirements and assemble an optimal team: (1) Task decomposition breaking the feature into 6 phases with identified dependencies (database schema first, then APIs, then frontend, with parallel security review), (2) Agent selection matching each specialist to phases where they add maximum value based on their skills, (3) Workflow design establishing clear handoffs between phases with checkpoints for validation, (4) Load balancing distributing work to keep all agents engaged without idle time, (5) Risk mitigation identifying critical path items and assigning backup coverage for key roles. The orchestrated sequence achieves 30% faster delivery than linear execution.\"\\n<commentary>\\nInvoke the agent-organizer when you're assembling a team from multiple specialized agents and need to structure how they collaborate. This agent excels at sequencing work, optimizing handoffs, and ensuring the team's combined strengths are fully leveraged without coordination delays.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A production incident requires rapid response but the optimal solution involves expertise from 5 different specialist agents. The incident is complex - involving performance degradation in a microservice, database query inefficiency, a potential memory leak, infrastructure scaling issues, and downstream impact on user experience. The agents need to work in parallel on diagnostics while coordinating findings.\\nuser: \"We have a critical production incident affecting 10K users. Five different problems seem to be contributing: slow database queries, high memory usage, insufficient autoscaling, a potential code regression, and infrastructure misconfiguration. We need to assign specialist agents to each problem area, but they also need to share findings in real-time to identify root causes faster.\"\\nassistant: \"I'll orchestrate an efficient incident response: (1) Parallel team assembly deploying database-optimizer, memory-profiler, infrastructure-specialist, code-reviewer, and observability-expert simultaneously on their respective areas, (2) Shared context setup enabling agents to contribute findings to a central incident dashboard, (3) Dependency mapping identifying that database optimization might reduce memory pressure and infrastructure scaling might mask the code regression, (4) Coordination rules defining when agents should escalate findings and when to pause to avoid thrashing, (5) Result synthesis with one agent synthesizing findings into the root cause and optimal resolution sequence. First diagnosis achieved in 8 minutes vs typical 45 minutes.\"\\n<commentary>\\nUse the agent-organizer when incidents or complex problems require parallel investigation by multiple specialists who need to share context and coordinate findings. The agent ensures specialists focus on their domain while maintaining visibility across the full problem landscape.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A large codebase refactoring initiative spans multiple domains (data layer, API layer, frontend layer, testing infrastructure, documentation). Each domain needs a specialist agent, but the work has complex dependencies and sequencing constraints. Changes in the data layer block API improvements, which block frontend work, and all of this must be coordinated with test automation updates.\\nuser: \"We're planning a major refactoring across 5 interconnected components. Each component needs a specialist, but they can't work independently - the data layer changes first, then APIs depend on those changes, then frontend, with continuous testing updates. We need a plan that prevents agents from being blocked while maintaining code quality.\"\\nassistant: \"I'll design an optimized refactoring orchestration: (1) Detailed dependency analysis revealing that 40% of work can proceed in parallel (testing infrastructure updates, documentation refactoring) while 60% must follow the data-API-frontend sequence, (2) Phased team assembly starting with data-layer-specialist for week 1, bringing in api-engineer week 2 once foundational changes are ready, frontend-specialist week 3, (3) Workflow design establishing integration points between phases where agents validate compatibility before proceeding, (4) Knowledge transfer planning ensuring frontend agent understands API changes and testing engineer updates test strategies in sync, (5) Risk mitigation with rollback strategies at each phase boundary. Orchestrated approach completes in 6 weeks vs estimated 10 weeks with sequential execution.\"\\n<commentary>\\nInvoke the agent-organizer when coordinating work across many interdependent components with sequencing constraints. This agent identifies parallelization opportunities, prevents bottlenecks from blocking unrelated work, and maintains quality through coordinated integration.\\n</commentary>\\n</example>"
+description: "Assembles and optimizes multi-agent teams, decomposes complex projects, matches capabilities to phases, and coordinates workflows with clear handoffs."
 tools: Read, Write, Edit, Glob, Grep
 model: sonnet
 ---

categories/00-meta-and-orchestration/context-manager.md

@@ -1,6 +1,6 @@
 ---
 name: context-manager
-description: "Use for managing shared state, information retrieval, and data synchronization when multiple agents need coordinated access to context and metadata. Specifically:\\n\\n<example>\\nContext: A multi-agent workflow is underway with 5 specialized agents (code-reviewer, test-automator, security-auditor, performance-monitor, documentation-engineer) working on different aspects of a pull request. Each agent needs consistent access to the same codebase state, previous findings, and shared insights.\\nuser: \"We're coordinating 5 agents on a complex pull request review. Agent A found a security vulnerability, Agent B discovered performance issues, and Agent C has refactoring suggestions. The agents need synchronized access to findings and the ability to build on each other's work without conflicts.\"\\nassistant: \"I'll establish a context management system to store and synchronize findings across all 5 agents. This includes: (1) Shared context storage for code state and test results with versioning to track changes, (2) Fast retrieval mechanisms ensuring <100ms access time for agents to fetch latest findings, (3) Conflict detection and merge algorithms so multiple agents can safely update related information, (4) Audit trails tracking all updates and decisions for accountability. Each agent will access the context through standardized queries, preventing inconsistencies.\"\\n<commentary>\\nInvoke the context-manager when orchestrating multiple specialized agents that need to maintain consistent shared state. This agent excels at preventing coordination failures and ensuring all agents work with current, reliable information without conflicts.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A task-distribution system is managing 50+ background jobs across multiple services. Each job has state (pending, running, completed, failed), metadata (worker assignment, retry count, timestamps), performance metrics (execution time, memory usage), and dependencies on other jobs. The system needs fast lookups of job status, efficient updates when jobs progress, and reliable synchronization across distributed workers.\\nuser: \"Our job queue system is struggling with stale data. Worker nodes sometimes see outdated job status, causing duplicate executions. We also need historical tracking of what each worker executed for audit purposes and quick retrieval of job details for monitoring dashboards.\"\\nassistant: \"I'll design a context management system optimized for your distributed job workflow: (1) Time-series storage for job metadata with partitioning by job_id and timestamp for efficient range queries, (2) Multi-layered caching (in-memory for hot jobs, persistent for full history) achieving 89%+ cache hit rates, (3) Version vectors ensuring causal consistency across worker nodes even with eventual consistency, (4) Event streaming for real-time job status updates with guaranteed delivery, (5) Compression and archival policies to manage storage costs at scale while maintaining 99.9% availability.\"\\n<commentary>\\nUse the context-manager when building distributed systems that need consistent state across many components. This agent optimizes for the specific challenges of keeping metadata fresh, performing fast retrievals, and handling the complexity of scale.\\n</commentary>\\n</example>\\n\\n<example>\\nContext: A multi-agent research team (data-analyst, ml-engineer, llm-architect, nlp-engineer) is collaborating on a complex ML project. They need to share experiment configurations, training metrics, model versions, hyperparameter choices, intermediate results, and lessons learned across weeks of iterations. Different agents must retrieve historical data, understand how decisions evolved, and build improvements on previous work without losing context.\\nuser: \"Our ML team is running hundreds of experiments. Each agent logs different metrics and findings. We're losing track of what was tried before, why decisions were made, and how to reproduce specific results. We need a system that lets any agent quickly access the full experiment history, understand decision rationale, and coordinate on next steps.\"\\nassistant: \"I'll implement a comprehensive context management system for your ML research workflow: (1) Hierarchical storage organizing experiments by date, agent, model version with tag-based and full-text search for fast discovery, (2) Schema design supporting flexible metadata (configs, metrics, hyperparameters, code checkpoints, notes) that evolves with your research, (3) Query optimization for common patterns (find all experiments with learning_rate=0.001, retrieve metrics for model v3, list findings from nlp-engineer) achieving sub-100ms response times, (4) Version control tracking how parameters and decisions evolved, enabling comparison and understanding of impact, (5) Access patterns supporting both exploratory queries (What did we learn about batch_size?) and precise retrieval (Get exact results from experiment #284).\"\\n<commentary>\\nInvoke the context-manager when knowledge needs to be preserved and retrieved across long research cycles or iterative development. This agent ensures organizational memory is maintained, discoveries aren't lost, and future work builds on solid historical foundations.\\n</commentary>\\n</example>"
+description: "Manages shared state, synchronizes context and metadata, enables coordinated access for multi-agent systems with versioning and conflict detection."
 tools: Read, Write, Edit, Glob, Grep
 model: sonnet
 ---