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Found 17 Skills
Set up and maintain multiple isolated Claude Code CLI profiles so students and power users can run different LLM providers (Kimi K3, Kimi K2.7 highspeed, MiniMax M3, MiniMax M2.7, GLM, DeepSeek, StepFun, Anthropic) in separate terminal windows at the same time. Use this skill whenever the user asks about multi-provider Claude setup, multiple Claude Code windows, switching models or the csk/csd/csg aliases, CLAUDE_CONFIG_DIR, the ~/.claude-profiles directory, or running Kimi/MiniMax/GLM/DeepSeek/StepFun alongside Anthropic. It also owns profile-drift troubleshooting — a third-party profile missing skills/hooks/plugins the default profile has, claude-profiles-doctor reporting a real directory where a symlink belongs, or settings not converging — and per-provider context-window configuration (the [1m] marker or explicit CLAUDE_CODE_MAX_CONTEXT_TOKENS).
Reference skill for building production-ready crw integrations. Covers verb selection, call surfaces (CLI/MCP/REST), post-filtering strategies, context-window hygiene, Hybrid RAG patterns, common pitfalls, and crw-specific operational considerations (search backend limits, renderer pool, proxy rotation). Load this when writing application code that embeds crw, designing a multi-step agent workflow, or debugging an integration that isn't behaving as expected.
Proactively monitors estimated token usage during long sessions and triggers context-window-management before overflow, not after. Use at the start of any session expected to last more than 30 minutes.
Use when auditing, trimming, or restructuring AI skill files to reduce context-window consumption. Trigger whenever a SKILL.md exceeds 120 lines, skills share duplicated content, AGENTS.md has large inline blocks, or the user asks to optimize, slim down, or reduce token usage of their skills.
(NS) LangGraph.js agent-api — greenfield scaffold (bootstrap-agent-runtime), StateGraph, MCP tools, skill bind/inject, checkpointers, context-window trim, HITL/SSE, prompt/locale placement. Use for new agent-api from scratch, LangGraph graphs, MCP wiring, orphan layout, system-prompt compose, bind parity, graph-spec sync, or "fix my LangGraph agent" / "wire MCP tools" / "translations in the graph". Feature diffs via ns-coder; version features via ns-spec-driven; LangGraph vs CrewAI via ns-multi-agent-architect. Do NOT use for CrewAI crews, generic web apps, or SDD-only requirements with no agent-api.
Show whether each skill is earning its context-window cost — combined tokens-used view sorted by waste. Use when the user asks 'are my skills worth it', 'what's my context budget', 'which skills are dead weight', or wants to audit skill value, token cost, or usage. Trigger with '/janitor-value'.
Preserve critical session state when compacting context. Use when context window is filling up and you need to summarize/reduce while keeping essential debugging information.
Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.
Analyzes Claude Code session transcripts (JSONL files) to reveal context window content, token usage patterns, and decision-making processes using view_session_context.py tool. Use when debugging Claude behavior, investigating token patterns, tracking agent delegation, or analyzing context exhaustion. Triggers on "why did Claude do X", "analyze session", "check session logs", "context window exhaustion", or "track agent delegation".
Full-codebase audit using 1M context window. Security, architecture, and dependency analysis in a single pass. Use when you need whole-project analysis.
CRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with any AI model (OpenRouter, Gemini, OpenAI, local models). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, Gemini, OpenAI, Ollama, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.
Expert in managing the "Memory" of AI systems. Specializes in Vector Databases (RAG), Short/Long-term memory architectures, and Context Window optimization. Use when designing AI memory systems, optimizing context usage, or implementing conversation history management.