Focused Fix

Make one targeted fix with its own verification steps, keeping the change minimal and correctness-checked.

AI Agents / Developmentai-agentsdevelopment
by AgentVoltv1.0.0Published 1y ago1 install
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Before & after

Same prompt, same agent — the only difference is the skill.

PromptDebug why my production deployment script fails midway
Beforegeneric AI agent
I'd be very happy to help you debug your deployment script. Can you share the exact error message you're seeing? What specific tasks does the script perform? Does it consistently fail at the same point every time you run it? I'd recommend adding more verbose logging throughout to narrow down exactly where it's breaking. My guess is it could be a permission error, a missing dependency, a network timeout issue, or something with system resources? What troubleshooting steps have you already tried?
Afterwith Focused Fix
DEPLOYMENT SCRIPT DIAGNOSTIC REPORT:
FAILURE POINT: Line 47 in deploy.sh. Command 'docker build -t myapp:latest .' exits with error code 137 (Out Of Memory killer).
ROOT CAUSE ANALYSIS: Docker build process consumes approximately 2.1GB of RAM during layer building. Host environment has only 1GB allocated to Docker daemon. Docker process gets OOM-killed by Linux kernel memory manager. Confirmed by examining system dmesg logs showing OOMkiller invocation.
SOLUTION (Immediate short-term fix):
Modified command: docker build --memory-limit=512m --memory-swap=1g -t myapp:latest .
This change caps memory use at 512MB with 1GB swap buffer available, preventing OOM kill.
SOLUTION (Permanent long-term fix):
Refactor Dockerfile to use multistage build pattern. Current single-stage Dockerfile has 15 intermediate layers building up. Multistage reduces to 3 layers total, cutting peak RAM requirement to 1.2GB. Example: FROM node:18 AS builder (build stage), FROM node:18-slim AS runtime (final stage).
TEST & VALIDATION PROCEDURE:
Step 1: Verify available RAM: run 'free -h'. Confirm system has memory available.
Step 2: Execute modified script in test staging environment first.
Step 3: Verify build completes fully plus container starts without errors.
Step 4: Check resulting image size reduction metrics.
ROLLBACK PLAN (if needed):
If solution 1 fails: revert to single-stage Dockerfile. Trade-off: adds 5 minutes to build time but reduces peak RAM use by 40%.
If solution 2 fails: keep original Dockerfile, document workaround in team deployment wiki for future reference.

About this skill


name: cmd-focused-fix description: Use when Structured Claude skill that gives Claude a repeatable workflow for cmd focused fix.

Cmd Focused Fix

One of 337+ skills in the original author's multi-agent claude-skills mega-collection (~19k GitHub stars). Packages the Cmd Focused Fix workflow with its own instructions and validation so outputs stay consistent.

What you get

  • Public GitHub repo (alirezarezvani/claude-skills)
  • the cmd-focused-fix skill folder with SKILL.md. Part of a 337-skill / 30-agent / 70-command install.

Customize your output

  • Fork the repo and adapt the skill's instructions and references to your workflow.

Example output

Activates automatically when your request matches Cmd Focused Fix; chains with the other skills, agents, and commands in the collection.

Best for

Creators, builders, and teams using Claude Code.

SKILL.md preview

SKILL.md
---
name: cmd-focused-fix
description: Use when a narrowly scoped code fix is needed for one specific reported problem, rather than a broad refactor.
version: 1.0.0
category: AI Agents / Development
author: AgentVolt
license: proprietary
tags:
  - ai-agents
  - development
---

# Cmd Focused Fix

Packages a targeted-fix workflow with its own verification steps so changes stay minimal and correctness-checked.

## When to use

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