OpenClaw

prompt-assemble

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.

alexunitario-sketch0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install alexunitario-sketch/prompt-assemble

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

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:

✅ Never fail due to memory-related token overflow
✅ Memory is always discardable enhancement, never rigid dependency
✅ 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
    ↓
Need-Memory Decision
    ↓
Minimal Context Build
    ↓
Memory Retrieval (Optional)
    ↓
Memory Summarization
    ↓
Token Estimation
    ↓
Safety Valve Decision
    ↓
Final Prompt → 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...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/alexunitario-sketch/prompt-assemble
Version: —
Commit: 64a7d9a45f3bbc16775f418e33d3c39eb40e940b
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# 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:**
- ✅ Never fail due to memory-related token overflow
- ✅ Memory is always discardable enhancement, never rigid dependency
- ✅ 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 → LLM Call
```

## Phase Details

### Phase 0: Base Configuration
```python
# 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
```python
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...

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