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2.6K downloads. Trust evidence available

How should repair-or-replace be evaluated before use?

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repair-or-replace

Decide whether to fix, replace, or recycle a broken item. Takes item type, age, symptoms, and repair estimate, then produces a scored recommendation across cost, lifespan, sentimental value, and environmental impact. Skill: repair-or-replace Owner: voronindenis5 Summary: Decide whether to fix, replace, or recycle a broken item. Takes item type, age, symptoms, and repair estimate, then produces a scored recommendation across cost, lifespan, sentimental value, and environmental impact. Tags: latest:0.1.2 Version history: v0.1.2 | 2026-08-14T06:19:14.590Z | auto - Removed the file skill-card.md. - No user-facing or functional change

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

2.6k

Updated

Oct 9, 2026

Version

0.1.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 2.6K downloads reported by the source. Last updated 10/9/2026.

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 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 2.6K downloadsadoption · observed Oct 9, 2026
Latest release: 0.1.2release · observed Aug 14, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: medium.

clawhub skill install s17b6amkd3wzqgg640v03a9r1n83gxs1:repair-or-replace

Setup complexity is MEDIUM. Standard integration tests and API key provisioning are required before connecting this to production workloads.
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-voronindenis5-repair-or-replace/snapshot"

Documentation

CLAWHUB

94,624 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: repair-or-replace
description: >
  Decide whether to fix, replace, or recycle a broken item. Takes item type,
  age, symptoms, and repair estimate, then produces a scored recommendation
  across cost, lifespan, sentimental value, and environmental impact.
version: 1.0.0
author: Denis Voronin
license: MIT
tags:
  - decision-making
  - consumer
  - repair
  - sustainability
  - cost-analysis
  - environment
---

# Repair or Replace

> Don't guess. Score it.

`Repair or Replace` is a skill that helps you decide whether to fix a broken
item, buy a replacement, or recycle it. It builds a weighted decision matrix
across five factors — repair cost, replacement cost, remaining lifespan,
sentimental value, and environmental impact — then outputs a clear,
score-backed recommendation with reasoning.

## When to Use

Activate this skill when:

- An appliance, device, or tool is broken and you're unsure whether to fix it
- You have a repair estimate and want to compare it against replacement
- You want to factor environmental impact into a purchasing decision
- You're helping someone else decide what to do with a broken item
- You want a structured, defensible decision rather than a gut call

## How It Works

The script takes structured input about the item and its condition:

| Parameter           | Description                                         | Example            |
| ------------------- | --------------------------------------------------- | ------------------ |
| `--item`            | What the item is                                    | "washing machine"  |
| `--age`             | How old it is (years)                               | `8`                |
| `--repair-cost`     | Estimated repair cost                               | `250`              |
| `--replacement-cost`| Cost of a new equivalent                            | `800`              |
| `--expected-lifespan`| Expected total lifespan of this item type (years)  | `12`               |
| `--symptoms`        | What's wrong (free text)                            | "won't spin"       |
| `--sentimental`     | Sentimental value (1-10)                            | `3`                |
| `--condition`       | Overall condition aside from the fault (1-10)      | `6`                |

It then scores the decision across five dimensions, applies weights, and
produces a recommendation: **Repair**, **Replace**, or **Recycle/Donate**.

## Decision Matrix

| Factor              | Weight | Repair Favors                          | Replace Favors                      |
| ------------------- | ------ | -------------------------------------- | ----------------------------------- |
| **Cost Ratio**      | 30%    | Repair < 50% of replacement            | Repair > 50% of replacement         |
| **Remaining Life**  | 25%    | <50% of expected lifespan used         | >50% of expected lifespan used      |
| **Condition**       | 15%    | Good condition otherwise               | Multiple issues, poor co

README.md

# Repair or Replace

> Don't guess. Score it.

A [Hermes Agent](https://hermes-agent.nousresearch.com/docs) / OpenClaw skill
that helps you decide whether to **fix, replace, or recycle** a broken item
using a weighted decision matrix across cost, lifespan, condition, sentiment,
and environmental impact.

## Why

When something breaks, the repair-vs-replace decision is usually made on gut
feel — or worse, on whatever the repair shop quotes before you've thought it
through. This skill structures the decision: it takes the numbers you have
(repair cost, replacement cost, age), combines them with factors you might not
have considered (remaining lifespan, environmental cost, sentimental value),
and produces a clear, score-backed recommendation with reasoning.

## What's Included

- **`SKILL.md`** — core skill: decision matrix, quick-reference, when-to-use.
- **`references/`**
  - `decision-matrix.md` — full scoring algorithm, weights, and rationale.
  - `item-lifespans.md` — expected lifespan data for 40+ common items.
  - `environmental-impact.md` — e-waste, embodied carbon, and sustainability.
- **`scripts/repair_or_replace.py`** — the main decision engine (stdlib only).
- **`scripts/sample_run.sh`** — example invocations for different item types.

## Quick Start

```bash
# Basic decision
python3 scripts/repair_or_replace.py \
  --item "washing machine" \
  --age 8 \
  --repair-cost 250 \
  --replacement-cost 800 \
  --expected-lifespan 12

# With symptoms and condition
python3 scripts/repair_or_replace.py \
  --item "refrigerator" \
  --age 10 \
  --repair-cost 200 \
  --replacement-cost 900 \
  --expected-lifespan 14 \
  --symptoms "not cooling properly" \
  --condition 4

# Factor in strong sentimental value (grandfather's watch)
python3 scripts/repair_or_replace.py \
  --item "vintage watch" \
  --age 20 \
  --repair-cost 150 \
  --replacement-cost 500 \
  --expected-lifespan 40 \
  --sentimental 9

# JSON output
python3 scripts/repair_or_replace.py --item "laptop" --age 5 \
  --repair-cost 300 --replacement-cost 1000 --format json

# Interactive mode (prompts for each value)
python3 scripts/repair_or_replace.py --interactive
```

Example output:

```
Repair or Replace — Decision Report
====================================
Item                : washing machine
Age                 : 8 years
Expected lifespan   : 12 years

Repair cost         : $250
Replacement cost    : $800
Cost ratio          : 31% (repair is 31% of replacement)

Decision Matrix (weighted):
  Cost Ratio        : 25.0/30  → Repair favored (cost ratio < 50%)
  Remaining Life    : 12.5/25  → Only 33% lifespan remaining
  Condition         : 10.5/15  → Decent overall condition
  Sentimental       :  2.0/10  → Low sentimental value
  Environmental     : 16.0/20  → Repair avoids e-waste

Total Score         : 66.0/100

Recommendation      : REPAIR
Confidence          : Moderate (66%)

Reasoning:
  • Repair cost is well below the 50% threshold (31%)
  • Item still has some remaining l

_meta.json

{
  "ownerId": "kn75wwn4x6djaf28jbykeamazd81gtdp",
  "slug": "repair-or-replace",
  "version": "0.1.2",
  "publishedAt": 1786688354590
}

references/decision-matrix.md

# Decision Matrix — Scoring Algorithm

This document details the scoring algorithm used by `repair_or_replace.py`.

## Overview

The decision engine scores the item across five factors, each weighted to
reflect its importance in the repair-vs-replace decision. Factor scores are
normalized to 0-100, multiplied by their weight, and summed. The total
determines the recommendation.

## Factors and Weights

| Factor           | Weight | Range   | Higher = Repair |
| ---------------- | ------ | ------- | --------------- |
| Cost Ratio       | 30%    | 0-100   | Yes             |
| Remaining Life   | 25%    | 0-100   | Yes             |
| Condition        | 15%    | 0-100   | Yes             |
| Sentimental      | 10%    | 0-100   | Yes             |
| Environmental    | 20%    | 0-100   | Mixed           |

## Scoring Details

### 1. Cost Ratio (30%)

The ratio of repair cost to replacement cost:

```
cost_ratio = repair_cost / replacement_cost
```

| Cost Ratio | Score | Interpretation                       |
| ---------- | ----- | ------------------------------------ |
| 0-20%      | 100   | Repair is very cheap — definitely fix |
| 20-30%     | 90    | Repair is cost-effective              |
| 30-40%     | 75    | Repair is reasonable                  |
| 40-50%     | 60    | Borderline — consider other factors   |
| 50-60%     | 40    | Replace starts looking better         |
| 60-80%     | 20    | Replace is strongly favored           |
| 80-100%+   | 0     | Repair makes no financial sense       |

**Formula:** `score = max(0, min(100, 100 * (1 - cost_ratio / 0.5)))`

This creates a linear scale where a 50% ratio scores 50, and the score
decreases as the ratio increases.

### 2. Remaining Life (25%)

How much of the expected lifespan is left:

```
lifespan_used = age / expected_lifespan
remaining_pct = 1 - lifespan_used
```

| Lifespan Used | Score | Interpretation                           |
| ------------- | ----- | ---------------------------------------- |
| 0-25%         | 100   | Nearly new — lots of life left           |
| 25-50%        | 85    | Still in the prime of life               |
| 50-60%        | 65    | Past midpoint but functional             |
| 60-75%        | 40    | Entering failure-prone years             |
| 75-90%        | 20    | Near end of life                         |
| 90-100%+      | 5     | At or beyond expected lifespan           |

**Depreciation curve:** The score isn't linear because failure rates accelerate
in the last 25% of lifespan. A 50% penalty applies after 75% lifespan used.

### 3. Condition (15%)

Overall condition of the item beyond the current fault (user-supplied, 1-10):

```
normalized = condition_score / 10 * 100
```

| Condition (1-10) | Score | Meaning                          |
| ---------------- | ----- | -------------------------------- |
| 8-10             | 90-100| Excellent — like new             |
| 6-7              | 65-80 | Good — minor wear               |
| 4-5              | 40-55

references/environmental-impact.md

# Environmental Impact — E-Waste and Sustainability

The environmental factor in the Repair or Replace decision matrix accounts for
two considerations: avoiding e-waste through repair, and the energy efficiency
gains of newer models.

## The E-Waste Problem

### Scale

- The world generates **~57 million tonnes** of e-waste annually (UN Global
  E-waste Monitor, 2021).
- Only **~17%** is formally collected and recycled.
- E-waste contains toxic materials (lead, mercury, cadmium) that leach into
  soil and water when landfilled.

### Embodied Carbon

Every manufactured product carries "embodied" carbon — the emissions from
extraction, manufacturing, transport, and packaging:

| Item                | Embodied CO2 (kg)  | Equivalent            |
| ------------------- | ------------------ | --------------------- |
| Smartphone          | ~70                | 300 km of driving     |
| Laptop              | ~200               | 1,000 km of driving   |
| Washing machine     | ~400               | 2,000 km of driving   |
| Refrigerator        | ~500               | 2,500 km of driving   |
| Television (55")    | ~300               | 1,500 km of driving   |

Repairing extends the useful life of these embodied emissions. Keeping a
laptop for 5 years instead of 3 reduces its annual carbon footprint by ~40%.

### Right to Repair

The Right to Repair movement advocates for:
- Access to repair manuals and schematics
- Availability of spare parts
- No software locks preventing third-party repair
- Modular designs that are easy to disassemble

Supporting repair — even when slightly more expensive — signals market demand
for repairable products.

## When Replacement Is More Environmental

### Energy Efficiency Gains

For energy-hungry appliances, a new model may be significantly more efficient:

| Appliance          | Efficiency Gain (10-year-old → new) |
| ------------------ | ------------------------------------ |
| Refrigerator       | 20-40% more efficient                |
| Washing machine    | 25-50% more efficient                |
| Dishwasher         | 20-30% more efficient                |
| Air conditioner    | 30-50% more efficient                |
| Water heater       | 15-30% more efficient                |

### The Break-Even Calculation

The environmental benefit of replacement depends on whether the energy savings
offset the embodied carbon of the new item:

```
years_to_break_even = new_item_embodied_carbon / annual_energy_savings_carbon
```

**Example:** A new refrigerator saves ~100 kg CO2/year in energy. The new
fridge has ~500 kg embodied CO2. Break-even: 5 years. If the old fridge has
no remaining life, replace. If it has 5+ years left, repair is better.

### Rule of Thumb

- **Electronics (phones, laptops):** Almost always repair. Embodied carbon is
  high relative to energy savings.
- **Major appliances:** Compare. If the old unit is 10+ years old, replacement
  may save more carbon through efficiency.
- **Small appliances:** Usually repai

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