# Clawsaver

> Reduce model API costs by 20–40% through intelligent message batching. Buffer related messages, send once.

- Skill: `dvcrn/clawsaver` (Agent Skill, multi-file: 25 files)
- Install (CLI): `npx skillmds@latest add dvcrn/clawsaver`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/clawsaver/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: dvcrn (https://skillmd.com/u/dvcrn)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/dvcrn/clawsaver

---


# ClawSaver

**Reduce model API costs by 20–40% through intelligent message batching and buffering.**

Most agent systems waste money on redundant API calls. When users send follow-up messages, you call the model separately for each one. ClawSaver fixes this by waiting ~800ms to collect related messages, then sending them together in a single optimized request. Same response quality. Lower cost. No user friction.

## How It Works: Batching & Buffering

```
WITHOUT CLAWSAVER (Context Overhead Hidden):
User:  "What is ML?"
Model: → API Call #1 [Context: system prompt, chat history] (cost: $X)
       Returns: definition

User:  "Give an example"
Model: → API Call #2 [Context: system prompt, chat history, Q1, A1] (cost: $X)
       Returns: example

User:  "Apply to finance?"
Model: → API Call #3 [Context: system prompt, chat history, Q1–A2] (cost: $X)
       Returns: finance application

Total: 3 calls × full context = 3X cost, each call repeats context overhead

───────────────────────────────────────

WITH CLAWSAVER (Single Context Load):
User:  "What is ML?"          ← Buffer (800ms wait)
User:  "Give an example"      ← Buffer (800ms wait)
User:  "Apply to finance?"    ← Flush: Send all 3 together

Model: → API Call #1 [Context loaded ONCE: system prompt, chat history]
       Processes all 3 questions together
       Returns: comprehensive answer addressing all three

Total: 1 call × full context = 1X cost, context overhead paid once

Actual savings (with context): 67% reduction
Cost per token: 1/3 (fewer context re-loads + consolidation)
```

**Why it matters:** Context (system prompts, history, instructions) gets re-sent on every API call. With ClawSaver, you pay that context overhead **once per batch instead of three times**. This compounds the savings beyond just "fewer calls."

**Example (4K token context, 200 output tokens):**
- Without ClawSaver: 3 calls × 4,200 tokens = 12,600 tokens
- With ClawSaver: 1 call × 4,600 tokens = 4,600 tokens
- **Actual savings: 63% token reduction** (even better than call reduction)

## The Problem

```
User: "What is machine learning?"
(pause)
User: "Give an example"
(pause)
User: "How does that apply to healthcare?"
```

Without optimization: **3 API calls = 3x cost**  
With ClawSaver: **1 batched call = 1/3 the price**

Across thousands of conversations, this compounds fast.

## How It Works

1. User sends message → ClawSaver buffers it
2. Waits ~800ms for follow-ups from same user
3. If more messages arrive → keep buffering
4. Timer expires → send all messages together
5. Model responds once → you get complete answer

**Why users don't notice:** They're already waiting for your model response. Buffering input doesn't feel slower because the response comes right after the batch sends.

## Install

```bash
clawhub install clawsaver
```

## Quick Start (10 lines)

```javascript
import SessionDebouncer from 'clawsaver';

const debouncers = new Map();

function handleMessage(userId, text) {
  if (!debouncers.has(userId)) {
    debouncers.set(userId, new SessionDebouncer(
      userId,
      (msgs) => callModel(userId, msgs)
    ));
  }
  debouncers.get(userId).enqueue({ text });
}
```

## Impact

| Metric | Value |
|--------|-------|
| **Cost reduction** | 20–40% typical |
| **Setup time** | 10 minutes |
| **Code added** | ~10 lines |
| **Dependencies** | 0 |
| **File size** | 4.2 KB |
| **Latency added** | +800ms (user-imperceptible) |
| **Maintenance** | None |

## Three Profiles

Choose based on your use case:

### Balanced (Default)
- 25–35% savings
- 800ms buffer
- Chat, Q&A, general conversation

### Aggressive
- 35–45% savings
- 1.5s buffer
- Batch workflows, high-volume ingestion

### Real-Time
- 5–10% savings
- 200ms buffer
- Interactive, voice-first systems

## When to Use

✅ Chat applications  
✅ Customer support bots  
✅ Multi-turn Q&A  
✅ Any conversation with follow-ups  

❌ Single-request workflows  
❌ Sub-100ms response requirements  

## API

```javascript
new SessionDebouncer(userId, handler, {
  debounceMs: 800,      // wait time
  maxWaitMs: 3000,      // absolute max
  maxMessages: 5,       // batch size cap
  maxTokens: 2048       // reserved
})

// Methods
debouncer.enqueue(message)      // add to batch
debouncer.forceFlush(reason)    // send now
debouncer.getState()            // buffer + metrics
debouncer.getStatusString()     // human-readable
```

## Docs

- **START_HERE.md** — Navigation (pick your role/timeline)
- **AUTO-INTEGRATION.md** — ⭐ Drop-in middleware wrapper (2 min setup)
- **QUICKSTART.md** — 5-minute integration
- **INTEGRATION.md** — Patterns, edge cases, full config
- **SUMMARY.md** — Metrics and ROI (decision makers)
- **SKILL.md** — Full API reference
- **example-integration.js** — Copy-paste templates

## Security

- **No telemetry** — Doesn't phone home
- **No network calls** — Runs locally
- **No dependencies** — Pure JavaScript
- **You control output** — You decide what goes to your model

Data never leaves your machine.

## License

MIT

---

**Start here:** Pick your path in **START_HERE.md**, or jump to **QUICKSTART.md** for 5-minute setup.