Fitness tracker dashboard for AI coding agents (Claude Code, Codex). Visualize usage, cost, tokens, and productivity from local conversation logs.
Local-first tests for AGENTS.md and coding-agent instructions.
MCP server for agentfit: token-aware message truncation. Fit a chat history into the model's context budget with drop-oldest, drop-middle, or priority strategies.
Fit your messages into the LLM context window. Token-aware truncation with multiple strategies (drop-oldest, drop-middle, priority), pluggable tokenizers, zero dependencies.
The agent reliability stack: fit, guard, snap, vet, cast. One install for all five sibling libraries (token-aware truncation, network-egress firewall, snapshot tests, tool-arg validation, structured-output enforcer).
Fit messages to an LLM context window. Token-aware truncation with pluggable tokenizers and multiple strategies.
Approximate token counts for LLM messages, system prompts, and tools. Zero-config chars/4 heuristic, BYO tokenizer, content-block aware (text/image/tool_use/tool_result/document). One serde_json dep.