AI Coding Task Ledger

Software projects often collapse under the weight of chat memory limits and vague prompts. This skill provides a durable, dependency-aware task ledger that lives inside your repository, allowing AI agents to plan and execute complex features without losing context. It bridges the gap between high-level PRDs and granular implementation by maintaining a machine-readable task graph that persists across chat sessions.

What it does

• PRD Parsing converts unstructured requirement documents into a structured graph of actionable tasks.
• Dependency Tracking manages relationships between tasks to ensure the agent works in the correct logical sequence.
• Task Expansion breaks down coarse requirements into granular subtasks with specific implementation steps.
• Status Management maintains a local ledger of todo, in-progress, and done states within the project folder.
• Research Integration fetches external technical data to inform task implementation and updates task notes automatically.

How it works

1. Initialize the project via CLI or MCP to create the .taskmaster directory and configuration files.
2. Parse your PRD or requirements file to generate the initial task board and dependency tree.
3. Navigate the workload by requesting the "next" task, which identifies the highest priority unblocked item.
4. Execute implementation work through your AI editor, updating task statuses and notes as you progress.

Frameworks & tools

This skill is designed for Node.js environments and integrates with AI editors like Cursor, Windsurf, VS Code, and Claude Code. It supports major LLM providers including Anthropic, OpenAI, Google, and Perplexity for both coding and research tasks.

Why this beats prompting it yourself

Standard prompting relies on volatile chat history that eventually truncates or hallucinates. This skill enforces a local state that persists, ensuring the agent always knows exactly what remains to be done and what is blocking it. It significantly reduces token overhead by only loading relevant task context instead of the entire project history.

Use cases

• Manage multi-day feature builds that exceed typical model context windows.
• Coordinate complex refactors where file dependencies must be handled in a specific order.
• Maintain a shared task state for teams where both humans and AI agents contribute to the same repo.
• Automate the transition from product requirements to a technical execution plan.

Known limitations

Requires Node.js and npm/npx environment. Users must provide their own API keys for LLM providers. Task state is stored locally, requiring manual synchronization if used across different machines without git commits.