Prompt Safe

**Judgment:** A well-structured, practical framework for token-safe prompt assembly with memory orchestration. It is a good fit for agents that need predictable token usage and robust handling of optional long-term memory. **What it does well** - Centralizes all token-budget decisions in one layer (`MAX_TOKENS`, `SAFETY_MARGIN`), reducing accidental context overflows. - Enforces clear rules: system prompt and user input are never truncated; only memory is expendable. - Uses two-phase context construction (minimal base context → optional memory) plus a “memory safety valve” to skip memory when near token limits. - Encourages good memory hygiene (no raw logs, no reasoning traces; only summarized, high-level information). **Key risks / limitations** - The `need_memory` decision is based on simple keyword triggers, which may miss valid memory needs or trigger unnecessarily; teams with complex workflows may need to customize this logic. - The guarantee against overflow depends on the accuracy of token estimation and correct configuration of `MAX_TOKENS` per model; misconfiguration or unusual tokenizers can still cause rare edge issues. - When the safety valve activates, all relevant memory may be skipped, which can degrade coherence or continuity for tasks heavily dependent on prior context. - Summarization and token estimation add some overhead; in extreme low-latency scenarios this may matter. **Recommended scenarios** - Production or enterprise agents where **stability and predictable token use** are more important than squeezing every last token of context. - Assistants with **vector or long-term memory** that must remain fully optional and discardable without breaking core functionality. - Systems where multiple developers are changing prompts over time and need a **single, enforceable abstraction** for token limits and memory policies. Overall, this skill is a strong choice as the standard prompt-construction layer in any serious agent that uses memory and must avoid token overflow errors.