How the speckit.implement Command Executes Tasks with Dependency Management

The /speckit.implement command reads the tasks.md file, builds a directed acyclic graph (DAG) from task dependencies, performs a topological sort to determine execution order, and invokes AI agents sequentially to generate implementation artefacts.

The github/spec-kit repository provides a Spec-Driven Development workflow where the speckit.implement command serves as the execution engine that transforms structured task lists into working code. This command automates the implementation phase by orchestrating AI agents while respecting complex dependency chains between tasks. Understanding its internal mechanics reveals how Spec-Kit ensures deterministic, failure-resistant code generation across multiple AI assistants.

Architecture of the speckit.implement Command

The command implementation resides in src/specify_cli/__init__.py, where the Typer command definition explicitly describes the process as handling an "ordered, dependency-aware" task list【1062‑1064】. When invoked, the command executes a five-phase pipeline that guarantees prerequisites complete before dependent tasks begin.

Loading and Parsing the Task File

Upon invocation, speckit implement first locates and reads tasks.md from the project root. This Markdown file contains the task list generated by the preceding speckit tasks command. Each entry in this file represents a discrete implementation task and may include a depends_on tag that references other task identifiers (e.g., depends_on: speckit.task.foo).

Building the Dependency DAG

The command parses each task into a graph node, translating every depends_on reference into a directed edge. This construction forms a directed acyclic graph (DAG) that models the complete dependency topology of the project. If the parser detects a circular dependency during this phase, the command immediately aborts with a descriptive error, preventing infinite execution loops.

Topological Sorting and Execution

Once the DAG is validated, the command applies a topological sort algorithm to produce a linear execution sequence. This sorted order guarantees that every task appears only after all its dependencies have completed successfully. The system then iterates through this sequence, invoking the configured AI agent for each task with a context-rich prompt containing the task description, project state, and artefacts from previously completed dependencies.

AI Agent Integration and File Generation

The command leverages the agent-registration machinery defined in src/specify_cli/extensions.py to handle output placement. After an AI agent generates implementation code, the system automatically routes files to agent-specific command directories such as .claude/commands/ or .cursor/commands/. This registration system ensures that subsequent Spec-Kit commands like speckit.analyze can locate and reference freshly created artefacts immediately.

A StepTracker instance monitors each task's lifecycle, recording statuses (started, succeeded, failed) throughout execution. Upon completion, the command renders a concise progress summary and updates the "Next Steps" panel to indicate implementation completion.

Usage Examples

Generate a dependency-aware task list and execute it with automatic ordering:

# Generate the task list with dependency metadata

speckit tasks

# Execute implementation respecting all declared dependencies

speckit implement

Example tasks.md structure showing dependency declarations:

- name: speckit.backend.setup-db
  description: Create database migration files
  depends_on: speckit.backend.define-models

- name: speckit.backend.define-models
  description: Write ORM model definitions

In this scenario, speckit implement executes speckit.backend.define-models first because setup-db depends on it, ensuring model definitions exist before migration generation begins.

Summary

• The speckit.implement command processes tasks.md to build a DAG representing task dependencies.
• Topological sorting guarantees prerequisites complete before dependent tasks execute.
• The implementation in src/specify_cli/__init__.py cycles detection prevents circular dependency deadlocks.
• Agent-specific output directories (.claude/commands/, .cursor/commands/) are managed via src/specify_cli/extensions.py.
• StepTracker provides real-time progress monitoring and final execution summaries.

Frequently Asked Questions

How does speckit.implement handle circular dependencies?

The command detects cycles during the DAG construction phase in src/specify_cli/__init__.py. If a circular reference is found in the depends_on chains, the command aborts immediately with a clear error message before any AI agent invocation occurs, preventing infinite execution loops.

What happens if a task fails during execution?

The StepTracker instance records each task's status throughout the run. If a task fails, the command logs the failure status and continues processing remaining independent tasks where possible. The final summary report indicates which specific tasks succeeded or failed, allowing targeted debugging without re-running successful dependencies.

Can I use speckit.implement with multiple AI agents simultaneously?

While the command executes tasks sequentially to respect dependencies, it supports switching between registered agents (Claude, Gemini, Cursor, etc.) via the configuration in src/specify_cli/extensions.py. Generated files automatically route to the correct agent-specific directories, enabling multi-agent workflows where different tasks use different AI assistants.

Where is the task dependency logic tested?

The test suite in tests/test_ai_skills.py contains unit tests that verify the topological sorting algorithm and ensure speckit implement respects the declared ordering of tasks. These tests validate both proper dependency resolution and error handling for malformed dependency declarations.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}