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anth-local-dev-loop

'Configure a local development workflow for Anthropic Claude API projects.

Install / Use

npx skills add jeremylongshore/tons-of-skills-marketplace --skill anth-local-dev-loop

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

87/100

Category

Automation

Supported Platforms

Claude Code

Our assessment of anth-local-dev-loop

anth-local-dev-loop scores 87/100 on our quality scale, 1341st of 2,607 Automation skills we index.

Its SKILL.md is 5.2 KB long, well organised into 18 sections with 6 code examples: a solid amount of guidance for an agent.

With 2,785 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
20/20
Description
12/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 6 days ago, so anth-local-dev-loop is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

anth-local-dev-loop compared with similar skills

All 4 of these similar skills score higher than anth-local-dev-loop; compare them before choosing.

SkillScoreStarsUpdatedFormat
anth-local-dev-loop (this skill)by jeremylongshore872.8k6d agoSKILL.md
Agent-Reachby Panniantong10086.3k14d agoCLAUDE.md
headroomby headroomlabs-ai10074.1ktodayCLAUDE.md
rufloby ruvnet10073.6ktodayCLAUDE.md
Scraplingby D4Vinci10084.6ktodayMCP Server

Frequently asked questions

How do I install anth-local-dev-loop?
Run npx skills add jeremylongshore/tons-of-skills-marketplace --skill anth-local-dev-loop. The install tabs above show the steps for each supported agent.
Which AI agents does anth-local-dev-loop work with?
It is written for Claude Code, as a SKILL.md file. Other agents that read the same format can often use it too.
Is anth-local-dev-loop safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is anth-local-dev-loop still maintained?
The repository was last updated 6 days ago, so anth-local-dev-loop is actively maintained.

name: anth-local-dev-loop description: 'Configure a local development workflow for Anthropic Claude API projects.

Use when setting up dev environment, configuring hot reload,

or establishing a fast iteration cycle with the Messages API.

Trigger with phrases like "anthropic local dev", "claude dev setup",

"anthropic development workflow", "test claude locally".

' allowed-tools: Read, Write, Edit, Bash(npm:), Bash(pip:), Grep version: 1.7.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

  • saas
  • ai
  • anthropic compatibility: Designed for Claude Code

Anthropic Local Dev Loop

Overview

Set up a fast local development cycle for Claude API projects with environment management, request logging, cost tracking, and hot-reload.

Prerequisites

  • Completed anth-install-auth setup
  • Node.js 18+ or Python 3.8+
  • .env file with ANTHROPIC_API_KEY

Instructions

Step 1: Project Structure

my-claude-app/
├── .env                  # ANTHROPIC_API_KEY=sk-ant-...
├── .env.example          # ANTHROPIC_API_KEY=your-key-here
├── .gitignore            # Include .env
├── src/
│   ├── client.ts         # Singleton client
│   ├── prompts/          # System prompts as files
│   └── tools/            # Tool definitions
├── tests/
│   └── mock-responses/   # Saved API responses for testing
└── scripts/
    └── dev.ts            # Dev runner with logging

Step 2: Singleton Client with Request Logging

// src/client.ts
import Anthropic from '@anthropic-ai/sdk';

let client: Anthropic | null = null;

export function getClient(): Anthropic {
  if (!client) {
    client = new Anthropic({
      apiKey: process.env.ANTHROPIC_API_KEY,
      maxRetries: 2,
      timeout: 30_000,
    });
  }
  return client;
}

// Development logger — tracks cost per request
export function logUsage(messageId: string, usage: { input_tokens: number; output_tokens: number }, model: string) {
  const pricing: Record<string, { input: number; output: number }> = {
    'claude-sonnet-4-20250514': { input: 3.0, output: 15.0 },
    'claude-haiku-4-20250514': { input: 0.80, output: 4.0 },
    'claude-opus-4-20250514': { input: 15.0, output: 75.0 },
  };
  const rates = pricing[model] || pricing['claude-sonnet-4-20250514'];
  const cost = (usage.input_tokens * rates.input + usage.output_tokens * rates.output) / 1_000_000;
  console.log(`[${messageId}] ${model} | ${usage.input_tokens}+${usage.output_tokens} tokens | $${cost.toFixed(4)}`);
}

Step 3: Mock Responses for Tests

// tests/mock-client.ts
import { type Message } from '@anthropic-ai/sdk/resources/messages';

export function mockMessage(text: string): Message {
  return {
    id: 'msg_test_123',
    type: 'message',
    role: 'assistant',
    model: 'claude-sonnet-4-20250514',
    content: [{ type: 'text', text }],
    stop_reason: 'end_turn',
    stop_sequence: null,
    usage: { input_tokens: 10, output_tokens: 20 },
  };
}

Step 4: Hot-Reload Dev Script

# package.json scripts
"scripts": {
  "dev": "tsx watch src/index.ts",
  "dev:debug": "ANTHROPIC_LOG=debug tsx watch src/index.ts",
  "test": "vitest",
  "test:live": "LIVE_API=1 vitest --run"
}

Output

The development loop produces a repeatable local project layout, a single configured client, mock-backed tests, and a request log that records model and token usage. A change can then be exercised with no API spend in the normal test suite or with deliberately enabled live traffic when investigating a prompt or integration behavior.

Examples

While editing a prompt, run the default vitest command against a saved mock response and confirm the application handles the expected text and stop reason. When a real API check is needed, set LIVE_API=1 only for that invocation, choose the lower-cost development model, and inspect the logger for token use. If the live response changes the contract, first update the fixture and its assertions, then rerun the mock suite so later local iterations remain fast and deterministic.

Environment Management

# .env.example (commit this)
ANTHROPIC_API_KEY=your-key-here
ANTHROPIC_MODEL=claude-sonnet-4-20250514
ANTHROPIC_MAX_TOKENS=1024
ANTHROPIC_LOG=warn     # debug | info | warn | error

# Enable SDK debug logging
export ANTHROPIC_LOG=debug  # Logs all requests/responses

Cost Control During Development

import os

DEV_MODEL = "claude-haiku-4-20250514"      # $0.80/$4.00 per MTok
PROD_MODEL = "claude-sonnet-4-20250514"    # $3.00/$15.00 per MTok

model = DEV_MODEL if os.getenv("ENV") == "development" else PROD_MODEL

Error Handling

| Issue | Cause | Solution | |-------|-------|----------| | Hot reload triggers duplicate requests | File save causes restart | Add debounce or save-on-explicit-action | | .env not loading | Missing dotenv setup | Use dotenv package or tsx --env-file=.env | | Mock tests pass but live fails | Response shape changed | Update mocks from real API responses |

Resources

Next Steps

Apply patterns in anth-sdk-patterns for production-ready code.

Related Skills

View on GitHub
GitHub Stars2.8k
CategoryAutomation
Updated6d ago
Forks404

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions