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auto-coder

Synchronize development specs into implementation tasks and execute pending changes.

Updated Mar 5, 2026
Search Tags:#pytest#autonomous coding#spec-driven development#task orchestration#git commits#yaml config
One-click install
npx skills add https://github.com/ljk486/Rag-agent --skill auto-coder-ljk486
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: auto-coder
Source: https://github.com/ljk486/Rag-agent/tree/main/MODULAR-RAG-MCP-SERVER-clean-start/.claude/skills/auto-coder
Command: npx skills add https://github.com/ljk486/Rag-agent --skill auto-coder-ljk486

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill removes the friction of turning a written development spec into a working implementation, helping you move from planning to code with less manual coordination and fewer missed steps.

Core Features & Use Cases

  • Spec Synchronization: Reads the project spec, keeps chapter references aligned, and finds the next actionable task.
  • Autonomous Implementation: Writes code according to the architecture and task schedule, then updates progress so work stays traceable.
  • Testing and Recovery: Runs the relevant tests, applies limited auto-fix iterations when needed, and stops with a clear report if issues remain.
  • Progress Persistence: Updates task status and prepares the work for an atomic commit so development remains reproducible.
  • Use Case: A developer wants a new feature built from the spec without manually juggling task discovery, coding, testing, and status tracking.

Quick Start

Ask the skill to read the project spec, pick the next pending task, implement it, run the relevant tests, and update the progress record.

Frequently Asked Questions about auto-coder

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I turn development specs into code and commits automatically?▼

Spec-driven development automates turning development specs into code by syncing the specification, finding the next actionable task, writing the implementation, running tests, and preparing an atomic git commit.

How does autonomous coding handle test failures during implementation?▼

Autonomous coding handles test failures by running relevant tests with controlled retry loops, applying limited auto-fix iterations, and stopping with a clear report if issues remain unresolved.

Can I use pytest with spec-driven development for task orchestration?▼

Yes, pytest is used within the task orchestration workflow to run relevant tests, validate the autonomous implementation, and trigger controlled auto-fix recovery loops when failures occur.

What is the best way to persist development progress in a modular repository?▼

Persisting development progress atomically is best achieved by updating task status in project schedule files and preparing work for git commits, ensuring development remains reproducible and traceable.

Do I need YAML config files to orchestrate autonomous coding workflows?▼

YAML config files are required to define task orchestration rules, manage autonomous coding workflows, and align chapter references from the project spec with the implementation schedule.

When should I avoid autonomous implementation for spec-driven coding?▼

You should avoid autonomous implementation when tests require complex manual debugging beyond controlled retry loops, or when the modular repository lacks clear task schedule files and spec references.

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