What makes Token Cost Optimization notable?

955 downloads. Trust evidence available

How should Token Cost Optimization be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Token Cost Optimization

Token savings and API cost optimization. Provides token calculator, three-tier optimization strategies (prompt compression / cache reuse / model downgrade),... Skill: Token Cost Optimization Owner: openlark Summary: Token savings and API cost optimization. Provides token calculator, three-tier optimization strategies (prompt compression / cache reuse / model downgrade),... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-04-22T02:41:26.011Z | user - Initial release of token-cost-optimization skill. - Provides a token calculator script for quick estimation of API usage and

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

955

Updated

Oct 12, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 955 downloads reported by the source. Last updated 10/12/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 12, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 12, 2026
Adoption signal: 955 downloadsadoption · observed Oct 12, 2026
Latest release: 1.0.0release · observed Apr 22, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.0release
- Initial release of token-cost-optimization skill. - Provides a token calculator script for quick estimation of API usage and potential savings. - Outlines a three-tiered token optimization strategy: prompt compression, conversation summary caching, and model downgrade/task routing. - Includes step-by-step phased implementation guides and best practices for each optimization tier. - Offers quantified cost analysis examples and detailed configuration references for each strategy.
Observed Apr 22, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s1727wv2g20pc729snzcm4nf8183hy72:token-cost-optimization

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-openlark-token-cost-optimization/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

9,522 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: token-cost-optimization
description: Token savings and API cost optimization. Provides token calculator, three-tier optimization strategies (prompt compression / cache reuse / model downgrade), specific configuration guides, and quantified effect analysis.
---

# Token Cost Optimization

## Use Cases

User mentions token savings, API cost optimization, prompt compression, cache strategy, model downgrade, cost analysis.

## Quick Start

### Token Calculator
Run the calculation script, input conversation scale, and quickly estimate current token consumption and optimization potential:

```bash
python scripts/token_calculator.py
```

The script will prompt for:
- Number of conversation history items / average length
- Model and pricing used
- Current optimization status

Output: Current cost, optimized cost, savings percentage.

### Three-Tier Optimization Strategy
Ranked by effect / implementation cost:

| Tier | Strategy | Effect | Implementation Cost |
|------|----------|--------|---------------------|
| L1   | Prompt compression & output truncation | 10-30% | Low |
| L2   | Conversation summary caching | 30-50% | Medium |
| L3   | Model downgrade + task routing | 50-70% | High |

**Priority Recommendation**: Implement in order L1 → L2 → L3, verifying results at each stage before proceeding.

Detailed strategies, configuration guides, and pitfalls → See `references/tier-strategies.md`

## Phased Implementation Guide

### Phase 1: L1 Compression (Immediate Effect)
- Clean up redundant descriptions in system prompt
- Set max_tokens limits for long responses
- Remove outdated/unused messages from conversation history

### Phase 2: L2 Caching (1-3 Days)
- Establish FAQ shortcuts for high-frequency repeat questions
- Add summary compression at the beginning of conversations (execute every N rounds)

### Phase 3: L3 Routing (1-2 Weeks)
- Route simple tasks to cheaper models (e.g., 4o-mini / Haiku)
- Retain strong models for complex tasks
- Configure model routing rules

## Quantifiable Comparison Example

See the "Quantified Comparison" section in `references/tier-strategies.md` for details.

_meta.json

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  "ownerId": "kn75qrtb885pznwsjwsh18dvf1813bv0",
  "slug": "token-cost-optimization",
  "version": "1.0.0",
  "publishedAt": 1776825686011
}

references/tier-strategies.md

# Three-Tier Optimization Strategies Explained

## L1: Prompt Compression & Output Truncation (Effect 10-30%)

### Specific Actions

**1. System Prompt Streamlining**
```diff
- "You are a professional, patient, and meticulous AI assistant, skilled at analyzing problems and providing comprehensive, organized, and in-depth answers..."
+ "Professional AI assistant. Provide concise answers."
```

**2. max_tokens Limiting**
Set output limits based on task type:
- Simple Q&A: max_tokens=200
- Routine tasks: max_tokens=500
- Long-form generation: max_tokens=1500

**3. History Message Pruning**
When conversations exceed 20 rounds, delete the earliest 1/3 of history messages (preserving recent context).

### Quantified Effect

| Scenario | Tokens Before | Tokens After | Savings |
|----------|---------------|--------------|---------|
| Simple Q&A | 800/session | 500/session | 37% |
| Code Review | 2000/session | 1400/session | 30% |
| Long-form Text | 5000/session | 3500/session | 30% |

---

## L2: Conversation Summary Caching (Effect 30-50%)

### Specific Actions

**1. High-Frequency Question FAQ Shortcuts**
```python
# Pseudocode example
faq_cache = {
    "What's the weather today?": "Beijing, sunny, 25°C",
    "What's my name?": "Determined based on context",
}
# Cache hit → Return directly, no API call
```

**2. Dynamic Summary Compression**
After every N conversation rounds, compress history into a single summary:
```
[Summary] User is working on project X, discussed issues Y and Z, currently at stage Z.
```

**3. Reference Document Caching**
Store user-provided documents, code, contracts, etc., in a vector database; subsequent questions are answered via retrieval.

### Quantified Effect

| Scenario | Cost/Month Before | Cost/Month After | Savings |
|----------|-------------------|------------------|---------|
| 10 rounds/day × 30d | $90 | $45 | 50% |
| 30 rounds/day × 30d | $270 | $162 | 40% |
| 100 rounds/day × 30d | $900 | $450 | 50% |

---

## L3: Model Downgrade + Task Routing (Effect 50-70%)

### Model Routing Strategy

| Task Type | Route To | Cost Reduction |
|-----------|----------|----------------|
| Simple Q&A, Translation | gpt-4o-mini / claude-haiku | 90% |
| Normal Tasks, Summarization | gpt-4o / claude-sonnet | 50% |
| Complex Reasoning, Long Context | gpt-4-turbo / claude-opus | Baseline |

### Configuration Example (OpenAI API)

```python
def route_model(task_type, complexity):
    if complexity == "low":
        return "gpt-4o-mini"
    elif complexity == "medium":
        return "gpt-4o"
    else:
        return "gpt-4-turbo"
```

### Quantified Effect

| Monthly Calls | All 4o | Smart Routing | Savings |
|---------------|--------|---------------|---------|
| 5,000 | $75 | $23 | 69% |
| 20,000 | $300 | $95 | 68% |
| 50,000 | $750 | $240 | 68% |

---

## Pitfalls to Avoid

### ❌ Common Mistakes

1. **Over-compressing system prompt**
   - Consequence: Unstable model behavior, degraded output quality
   - Suggestion: Retain core direc

skill-card.md

## Description:

Helps estimate and reduce API token costs with a calculator, prompt compression, cache reuse, model routing, configuration guidance, and quantified savings examples.

This skill is ready for commercial/non-commercial use.

## Publisher:

[openlark](https://clawhub.ai/user/openlark)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and engineers use this skill to estimate model API costs and plan phased token-reduction work across prompt compression, caching, and model routing.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Caching user documents, source code, contracts, or customer data can expose private or regulated information if implemented without safeguards.

Mitigation: Require explicit consent, minimize or redact stored content, apply access controls, define retention limits, and provide a deletion path before enabling document or vector-database caching.

Risk: Over-compression, overly low token limits, or one-size-fits-all model downgrades can reduce answer quality or cause incomplete outputs.

Mitigation: Measure a baseline, validate each optimization phase, keep task-appropriate token buffers, and use fallback routing when cheaper models do not meet quality requirements.

## Reference(s):

- [Three-Tier Optimization Strategies Explained](references/tier-strategies.md)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance]

**Output Format:** [Markdown guidance with inline shell commands and Python snippets; the calculator prints terminal cost estimates.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Includes estimated monthly before/after costs and savings percentages based on user-entered usage assumptions.]

## Skill Version(s):

1.0.0 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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