esri-smells-consumer

Productivity & Tasks
v1.0.0
Benign

Paid client skill for Esri Workflow Smell Detector via x402 (Base/USDC)

11.3K downloads1.3K installsby @danmaps

Setup & Installation

Install command

clawhub install danmaps/esri-smells-consumer

If the CLI is not installed:

Install command

npx clawhub@latest install danmaps/esri-smells-consumer

Or install with OpenClaw CLI:

Install command

openclaw skills install danmaps/esri-smells-consumer

or paste the repo link into your assistant's chat

Install command

https://github.com/openclaw/skills/tree/main/skills/danmaps/esri-smells-consumer

What This Skill Does

Calls a paid REST endpoint that analyzes an ArcGIS Pro project snapshot for automation risks. Uses x402 pay-per-request billing on Base/USDC at $0.001 per call. Designed to run as a preflight check before executing ArcPy, geoprocessing, or AGOL workflows.

Separates the stateless, deterministic risk analysis from your local tooling so the same input always returns the same result and results are safe to cache.

When to Use It

  • Checking a project snapshot for automation risks before deploying ArcPy scripts
  • Scoring risk level of an AGOL workflow before a scheduled server run
  • Validating geoprocessing inputs to catch issues before a long batch job
  • Caching risk reports by requestHash to avoid redundant paid calls
  • Integrating preflight risk scoring into a CI pipeline for GIS automation
View original SKILL.md file
# Esri Workflow Smell Detector (Consumer Skill)

This skill helps an agent **call the paid Smell Detector** endpoint (x402 pay-per-request) using Base/USDC.

It does **not** host the service.

## How this relates to arcgispro-cli

The expected input, `project_snapshot`, is the JSON artifact produced by the open-source ArcGIS Pro CLI (`arcgispro-cli`).

Recommended workflow:
1) Use `arcgispro-cli` to export a project snapshot/context artifact (safe-by-default, no raw data)
2) Send that JSON to this paid endpoint for a deterministic preflight risk report
3) Use the report to decide whether to proceed with automation (ArcPy/GP/AGOL) and what to fix first

This keeps a clean boundary:
- Open core (`arcgispro-cli`) answers: **what is in the project**
- Paid layer (this service) answers: **how risky is it to automate, and why**

## Endpoint
- `POST https://api.x402layer.cc/e/esri-smells`

## Input
Required JSON body:

```json
{
  "project_snapshot": { },
  "constraints": {
    "target": "arcpy" | "geoprocessing" | "agol",
    "deployment": "desktop" | "server",
    "max_runtime_sec": 300
  }
}
```

## Output (guaranteed fields)
- `summary`
- `risk_score` (0.0–1.0)
- `issues[]`
- `flags`
- `version`
- `requestHash`

## Determinism
- Stateless
- No external network calls (beyond the paid endpoint itself)
- Same input produces same output
- Safe to cache by `requestHash`

## Pricing
- x402 pay-per-request on Base
- Target price: **$0.001** per call

## How to call (Python helper)

1) Install deps:

```bash
pip install -r {baseDir}/requirements.txt
```

2) Set wallet env (consumer wallet):

```bash
export PRIVATE_KEY="0x..."
export WALLET_ADDRESS="0x..."
```

3) Call the endpoint:

```bash
python {baseDir}/scripts/call_smells.py path/to/project_snapshot.json
```

### Notes
- The script implements the x402 HTTP 402 challenge flow and retries with `X-Payment`.
- If the endpoint is unreachable or the network rejects the payment, surface the error as-is.

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Checking a project snapshot for automation risks before deploying ArcPy scripts

AGENT
  1. 1Checking a project snapshot for automation risks before deploying ArcPy scripts
  2. 2Scoring risk level of an AGOL workflow before a scheduled server run
  3. 3Validating geoprocessing inputs to catch issues before a long batch job
  4. 4Caching risk reports by requestHash to avoid redundant paid calls
  5. 5Integrating preflight risk scoring into a CI pipeline for GIS automation
OUTPUT
Paid client skill for Esri Workflow Smell Detector via x402 (Base/USDC)

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Last updatedFeb 28, 2026