msiShariful/claude-token-inspector
Plugin Claude Code
Claude Code plugins by Shariful Islam — token inspection and context optimization tools.
35 tagged context window, measured the same way as everything else here.
msiShariful/claude-token-inspector
Plugin Claude Code
Claude Code plugins by Shariful Islam — token inspection and context optimization tools.
msiShariful/claude-token-inspector
Plugin Claude Code
Inspect and identify bloated context — find large files, redundant instructions, and unnecessary content consuming your token budget.
Plugin Claude Code
MCP server and hook toolkit that compresses responses and keeps explicitly indexed output searchable.
Plugin Claude Code
Compress tool responses and keep explicitly indexed output searchable without flooding the agent context window.
Plugin Claude Code
Context optimization for LLM agents: measure where a session's tokens actually go, and compress context only where compression pays for the prompt cache it invalidates.
Plugin Claude Code
Skills and MCP server for context optimization — decide whether compressing an agent's context pays for the prompt cache it invalidates, and compress it when the answer is yes. Bundles the distil MCP server so install wires everything in one step.
Plugin Claude Code
Compress documentation, prompts, and context into minimal tokens for AGENTS.md. Based on Vercel's research showing compressed passive context achieves 100% pass rate vs 53% baseline.
Plugin Claude Code
Working-memory shelf for Claude Code: the /shelve skill plus a SessionStart hook that injects the shelf INDEX (recall bootstrap) and a SessionEnd/PreCompact hook that pushes the shelf so episodes survive an ephemeral container.
Plugin Claude Code
Protects your AI context window via sandboxed execution and smart indexing.
Plugin Claude Code
Winnow — use many MCP servers without context bloat.
Plugin Claude Code
Use many MCP servers without context bloat. Winnow hides all your MCP servers behind one gateway exposing just 4 tools (searchtools / loadtool / calltool / runcode), so the model spends a fraction of the context.