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BytesAgain β€Ί Skills β€Ί Prompt Safe
πŸ¦€ ClawHub

Prompt Safe

by @alexunitario-sketch

Token-safe prompt assembly with memory orchestration. Use for any agent that needs to construct LLM prompts with memory retrieval. Guarantees no API failure due to token overflow. Implements two-phase context construction, memory safety valve, and hard limits on memory injection.

Versionv1.0.4
Downloads2,918
Stars⭐ 4
#clawhub
TERMINAL
clawhub install prompt-assemble

πŸ“– About This Skill

name: prompt-assemble description: Token-safe prompt assembly with memory orchestration. Use for any agent that needs to construct LLM prompts with memory retrieval. Guarantees no API failure due to token overflow. Implements two-phase context construction, memory safety valve, and hard limits on memory injection.

Prompt Assemble

Overview

A standardized, token-safe prompt assembly framework that guarantees API stability. Implements Two-Phase Context Construction and Memory Safety Valve to prevent token overflow while maximizing relevant context.

Design Goals:

  • βœ… Never fail due to memory-related token overflow
  • βœ… Memory is always discardable enhancement, never rigid dependency
  • βœ… Token budget decisions centralized at prompt assemble layer

    • When to Use

    Use this skill when: 1. Building or modifying any agent that constructs prompts 2. Implementing memory retrieval systems 3. Adding new prompt-related logic to existing agents 4. Any scenario where token budget safety is required

    Core Workflow

    User Input
    ↓
Need-Memory Decision
    ↓
Minimal Context Build
    ↓
Memory Retrieval (Optional)
    ↓
Memory Summarization
    ↓
Token Estimation
    ↓
Safety Valve Decision
    ↓
Final Prompt β†’ LLM Call

    Phase Details

    Phase 0: Base Configuration

    # Model Context Windows (2026-02-04)

    - MiniMax-M2.1: 204,000 tokens (default)
    

    - Claude 3.5 Sonnet: 200,000 tokens
    

    - GPT-4o: 128,000 tokens
    MAX_TOKENS = 204000  # Set to your model's context limit
SAFETY_MARGIN = 0.75 * MAX_TOKENS  # Conservative: 75% threshold = 153,000 tokens
MEMORY_TOP_K = 3                     # Max 3 memories
MEMORY_SUMMARY_MAX = 3 lines        # Max 3 lines per memory

    Design Philosophy:

  • Leave 25% buffer for safety (model overhead, estimation errors, spikes)
  • Better to underutilize capacity than to overflow

    • Phase 1: Minimal Context

  • System prompt
  • Recent N messages (N=3, trimmed)
  • Current user input
  • No memory by default

    • Phase 2: Memory Need Decision

    def need_memory(user_input):
    triggers = [
        "previously",
        "earlier we discussed",
        "do you remember",
        "as I mentioned before",
        "continuing from",
        "before we",
        "last time",
        "previously mentioned"
    ]
    for trigger in triggers:
        if trigger.lower() in user_input.lower():
            return True
    return False

    Phase 3: Memory Retrieval (Optional)

    memories = memory_search(query=user_input, top_k=MEMORY_TOP_K)
for mem in memories:
    summarized_memories.append(summarize(mem, max_lines=MEMORY_SUMMARY_MAX))

    Phase 4: Token Estimation

    Calculate estimated tokens for base_context + summarized_memories.

    Phase 5: Safety Valve (Critical)

    if estimated_tokens > SAFETY_MARGIN:
    base_context.append("[System Notice] Relevant memory skipped due to token budget.")
    return assemble(base_context)

    Hard Rules:

  • ❌ Never downgrade system prompt
  • ❌ Never truncate user input
  • ❌ No "lucky splicing"
  • βœ… Only memory layer is expendable

    • Phase 6: Final Assembly

    final_prompt = assemble(base_context + summarized_memories)
return final_prompt

    Memory Data Standards

    Allowed in Long-Term Memory

  • βœ… User preferences / identity / long-term goals
  • βœ… Confirmed important conclusions
  • βœ… System-level settings and rules

    • Forbidden in Long-Term Memory

  • ❌ Raw conversation logs
  • ❌ Reasoning traces
  • ❌ Temporary discussions
  • ❌ Information recoverable from chat history

    • Quick Start

    Copy scripts/prompt_assemble.py to your agent and use:

    from prompt_assemble import build_prompt
    In your agent's prompt construction:
    
final_prompt = build_prompt(user_input, memory_search_fn, get_recent_dialog_fn)

    Resources

    scripts/

  • prompt_assemble.py - Complete implementation with all phases (PromptAssembler class)

    • references/

  • memory_standards.md - Detailed memory content guidelines
  • token_estimation.md - Token counting strategies

    • ⚑ When to Use

    TriggerAction
    1. Building or modifying any agent that constructs prompts
    2. Implementing memory retrieval systems
    3. Adding new prompt-related logic to existing agents
    4. Any scenario where token budget safety is required

    πŸ’‘ Examples

    Copy scripts/prompt_assemble.py to your agent and use:

    from prompt_assemble import build_prompt
    In your agent's prompt construction:
    
final_prompt = build_prompt(user_input, memory_search_fn, get_recent_dialog_fn)