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Task Execution Engine

  • 387 installs
  • 30.1k repo stars
  • Updated August 4, 2026
  • davila7/claude-code-templates

task-execution-engine is a Claude Code skill that designs reliable agent task runners with decomposition, step tracking, retries, and failure recovery so developers who automate coding or ops workflows can execute multi-

About

task-execution-engine is a Claude Code skill from davila7/claude-code-templates focused on building dependable execution layers for autonomous agents. It guides developers through decomposing complex coding or operations work into tracked steps, persisting state between steps, retrying transient failures, and surfacing blockers instead of silently halving a plan. The skill targets engineers who outgrew one-shot prompts and need a runner that can resume after errors, parallelize safe subtasks, and report progress across long refactors or deployment sequences. Teams reach for task-execution-engine when agents drop mid-migration, lose context between shell commands, or cannot explain which step failed in a ten-part workflow. It complements individual domain skills by supplying orchestration patterns—queues, checkpoints, and retry policy—rather than domain-specific commands alone.

  • Models multi-step task graphs and dependencies
  • Supports retries, status tracking, and recovery
  • Fits autonomous coding and workflow agents
  • Bridges planning output to concrete tool calls
  • Raises reliability beyond one-shot prompts

Task Execution Engine by the numbers

  • 387 all-time installs (skills.sh)
  • Ranked #2,033 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/davila7/claude-code-templates --skill task-execution-engine

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Listed on Skillselion
Installs387
repo stars30.1k
Last updatedAugust 4, 2026
Repositorydavila7/claude-code-templates

How do you build a reliable agent task runner?

Design and implement a reliable task runner for agents that decomposes work, tracks steps, retries failures, and executes multi-step coding or ops workflows autonomously.

Who is it for?

Developers building autonomous coding agents or ops automations that must survive partial failures across long multi-step workflows.

Skip if: Single-command scripts or chats where a one-shot prompt completes the entire job without state, retries, or step visibility.

When should I use this skill?

A developer asks to design or implement an agent task runner with decomposition, step tracking, retries, or autonomous multi-step workflow execution.

What you get

Task decomposition plan, step tracker schema, retry policy, checkpoint/resume logic, and orchestration code for multi-step agent workflows.

  • Task runner design
  • Retry and checkpoint logic
  • Step state schema

Files

SKILL.mdMarkdownGitHub ↗

Feature Pipeline

Execute implementation tasks directly from design documents. Tasks are managed as markdown checkboxes - no separate session files needed.

Quick Reference

# Get next task
python3 scripts/task_manager.py next --file <design.md>

# Mark task completed
python3 scripts/task_manager.py done --file <design.md> --task "Task Title"

# Mark task failed
python3 scripts/task_manager.py fail --file <design.md> --task "Task Title" --reason "..."

# Show status
python3 scripts/task_manager.py status --file <design.md>

Task Format

Tasks are written as markdown checkboxes in the design document:

## Implementation Tasks

- [ ] **Create User model** `priority:1` `phase:model`
  - files: src/models/user.py, tests/models/test_user.py
  - [ ] User model has email and password_hash fields
  - [ ] Email validation implemented
  - [ ] Password hashing uses bcrypt

- [ ] **Implement JWT utils** `priority:2` `phase:model`
  - files: src/utils/jwt.py
  - [ ] generate_token() creates valid JWT
  - [ ] verify_token() validates JWT

- [ ] **Create auth API** `priority:3` `phase:api` `deps:Create User model,Implement JWT utils`
  - files: src/api/auth.py
  - [ ] POST /register endpoint
  - [ ] POST /login endpoint

See references/task-format.md for full format specification.

Execution Loop

LOOP until no tasks remain:
  1. GET next task (task_manager.py next)
  2. READ task details (files, criteria)
  3. IMPLEMENT the task
  4. VERIFY acceptance criteria
  5. UPDATE status (task_manager.py done/fail)
  6. CONTINUE

Unattended Mode Rules

  • NO stopping for questions
  • NO asking for clarification
  • Make autonomous decisions based on codebase patterns
  • If blocked, mark as failed and continue

Status Updates

Completed task:

- [x] **Create User model** `priority:1` `phase:model` ✅
  - files: src/models/user.py
  - [x] User model has email field
  - [x] Password hashing implemented

Failed task:

- [x] **Create User model** `priority:1` `phase:model` ❌
  - files: src/models/user.py
  - [ ] User model has email field
  - reason: Missing database configuration

Resume / Recovery

To resume interrupted work, simply run again with the same design file:

/feature-pipeline docs/designs/xxx.md

The task manager will find the first uncompleted task and continue from there.

Integration

This skill is typically triggered after /feature-analyzer completes:

User: /feature-analyzer implement user auth

Claude: [designs feature, generates task list]
        Design saved to docs/designs/2026-01-02-user-auth.md
        Ready to start implementation?

User: Yes / 开始实现

Claude: [executes tasks via task-execution-engine]

Related skills

How it compares

Use task-execution-engine for orchestration infrastructure; invoke domain skills for individual migration, test, or deploy steps inside the runner.

FAQ

What problem does task-execution-engine solve?

task-execution-engine addresses agents that lose context or stop mid-workflow during long coding or ops jobs. The skill designs decomposition, step tracking, retries, and checkpoints so multi-step runs can recover and report progress.

Does task-execution-engine replace domain skills?

task-execution-engine supplies orchestration patterns—queues, checkpoints, retry policy—while domain skills still perform specific coding or ops steps. Teams combine both when autonomous runs must span many commands.

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