Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
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Prompt Safe
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.
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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.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/memory_standards.md, references/token_estimation.md, scripts/prompt_assemble.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
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- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.4
Provenance
- Publisher
- alexunitario-sketch
- Source page
- View original listing
- Canonical URL
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Documentation
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: โ Never fail due to memory-related token overflow โ Memory is always discardable enhancement, never rigid dependency โ Token budget decisions centralized at prompt assemble layer
When to Use
Use this skill when: Building or modifying any agent that constructs prompts Implementing memory retrieval systems Adding new prompt-related logic to existing agents Any scenario where token budget safety is required
Core Workflow
User Input โ Need-Memory Decision โ Minimal Context Build โ Memory Retrieval (Optional) โ Memory Summarization โ Token Estimation โ Safety Valve Decision โ Final Prompt โ LLM Call
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: โ Never downgrade system prompt โ Never truncate user input โ No "lucky splicing" โ Only memory layer is expendable
Phase 6: Final Assembly
final_prompt = assemble(base_context + summarized_memories) return final_prompt
Allowed in Long-Term Memory
โ User preferences / identity / long-term goals โ Confirmed important conclusions โ System-level settings and rules
Forbidden in Long-Term Memory
โ Raw conversation logs โ Reasoning traces โ Temporary discussions โ 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)
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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
3 Docs1 Scripts
- SKILL.md
- references/memory_standards.md
- references/token_estimation.md
- scripts/prompt_assemble.py