changelog-updater
Update CHANGELOG.md and TEST-CHANGELOG.md with new entries following Keep a Changelog format and token optimization principles. Use when adding changes to the changelog, documenting new features, fixes, or optimizations.
token-economy
Apply token optimization when writing docs, changelogs, MCP tasks. Quality #1, Tokens #2.
repomix
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
doc-organizer
Apply Progressive Disclosure principles to organize large documentation projects. Restructure docs into hierarchical structure, reduce token usage by 95%+, and create README files for navigation.
orchestrator
Use when managing agent state transitions (START/INIT/IMPLEMENT/TEST/COMPLETE), triggering context compression at 80% capacity, or handling session lifecycle. Load at session start, on state change, or when context exceeds threshold. Core skill for single-orchestrator architecture.
mcp-setup
Use when setting up MCP servers for the first time or verifying MCP configuration. Ensures token-efficient and context-graph MCP servers are properly installed and configured with API keys.
skill-creator
Use when creating a new skill, updating an existing skill, or learning skill best practices. Load for extending Claude's capabilities with specialized workflows, tool integrations, or domain expertise. Covers skill anatomy, progressive disclosure (98% token savings), and the critical description-as-trigger pattern.
funsloth-check
Validate datasets for Unsloth fine-tuning. Use when the user wants to check a dataset, analyze tokens, calculate Chinchilla optimality, or prepare data for training.
caveman
Compress and simplify prompts to preserve meaning while reducing use of context
Convex Agents Usage Tracking
Tracks LLM token consumption and usage metrics for billing, monitoring, and optimization. Use this to log token usage, calculate costs, generate invoices, and understand which agents or users consume the most resources.
Convex Agents Rate Limiting
Controls message frequency and token usage to prevent abuse and manage API budgets. Use this to implement per-user limits, global caps, burst capacity, and token quota management.
skill-debugging-assistant
Debug, diagnose, and troubleshoot skill issues including trigger failures, parameter problems, prompt conflicts, and SKILL.md structural issues. Use when skills don't activate as expected, trigger incorrectly, produce unexpected behavior, conflict with system instructions, or fail packaging validation. Analyzes YAML frontmatter, descriptions, progressive disclosure, token budget, absolute statements, and reference file organization. For skill creators reviewing, validating, or fixing skill problems.
prompt-optimization-analyzer
Active diagnostic tool for analyzing skill prompts to identify token waste, anti-patterns, trigger issues, and optimization opportunities. Use when reviewing skill prompts, debugging why skills aren't triggering, optimizing token usage, or preparing skills for publication. Provides specific, actionable suggestions with examples.
skill-performance-profiler
Analyzes skill usage patterns across conversations to track token consumption, identify heavy vs. lightweight skills, measure invocation frequency, detect co-occurrence patterns, and suggest consolidation opportunities. Use when the user asks to analyze skill performance, optimize skill usage, identify token-heavy skills, find consolidation opportunities, or review skill metrics.
learning-capture
Recognize and capture reusable patterns, workflows, and domain knowledge from work sessions into new skills. Use when completing tasks that involve novel approaches repeated 2+ times, synthesizing complex domain knowledge across conversations, discovering effective reasoning patterns, or developing workflow optimizations. Optimizes for high context window ROI by identifying patterns that will save 500+ tokens per reuse across 10+ future uses.
project-operations
Provides intelligent project setup and management with agent-based architecture to minimize token usage. Auto-activates when user mentions project setup, "add project", "configure project", "monorepo", "subdirectories", "switch project", or "project info". Uses three specialized agents internally: project-detector (detect active), project-config-loader (load settings with validation), project-context-manager (manage active project). Guides through four workflows: Add New Project (setup + templates), Configure Monorepo (pattern matching + subdirectories), Switch Between Projects (auto or manual), View Project Information. Provides templates for common architectures (fullstack-with-jira, fullstack-linear-only, mobile-app, monorepo). Validates configuration and suggests fixes for errors. Handles context-aware error handling with specific fix suggestions.
hook-optimization
Provides guidance on optimizing CCPM hooks for performance and token efficiency. Auto-activates when developing, debugging, or benchmarking hooks. Includes caching strategies, token budgets, performance benchmarking, and best practices for maintaining sub-5-second hook execution times.
session-context-management
Maintain "just enough" context across work sessions using CURRENT.md, STATUS.md, and LESSONS.md files. Activate when tasks take >15 minutes, touch 3+ files, interruptions likely, or scope uncertain. Includes /snapshot and /pickup commands for saving and resuming work. ADHD-friendly, token-efficient approach.
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