---
name: prompt-assemble-f6dac327adf8
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:**
- ✅ 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 trigger in triggers:
        if trigger.lower() in user_input.lower():
            return True
    return False
```

### Phase 3: Memory Retrieval (Optional)
```python
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)
```python
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"
- ✅ Only memory layer is expendable

### Phase 6: Final Assembly
```python
final_prompt = assemble(base_context + summarized_memories)
return final_prompt
```

## Memory Data Standards

### Allowed in Long-Term Memory
- ✅ User preferences / identity / long-term goals
- ✅ Confirmed important conclusions
- ✅ 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:

```python
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


---
Source attribution: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/alexunitario-sketch/prompt-assemble/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 clawdbot

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: kbarbel640-del
Original project: https://github.com/kbarbel640-del/skills/blob/e7e336ac628c3e3bd771aff1aaa469c93778f94a/skills/alexunitario-sketch/prompt-assemble/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management
Roles: Data Scientist, ML / AI Engineer, Product Manager
Specialties: API
Tools: Python
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: f4e785d9f771c5efe06833bcd6513f75a44b5349254335b701608109f1c310f4