task-execution-engine

davila7/claude-code-templates · updated Apr 8, 2026

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$npx skills add https://github.com/davila7/claude-code-templates --skill task-execution-engine
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summary

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

skill.md

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]
how to use task-execution-engine

How to use task-execution-engine on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add task-execution-engine
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/davila7/claude-code-templates --skill task-execution-engine

The skills CLI fetches task-execution-engine from GitHub repository davila7/claude-code-templates and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/task-execution-engine

Reload or restart Cursor to activate task-execution-engine. Access the skill through slash commands (e.g., /task-execution-engine) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.728 reviews
  • Min Ghosh· Dec 12, 2024

    task-execution-engine has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Dhruvi Jain· Dec 8, 2024

    Solid pick for teams standardizing on skills: task-execution-engine is focused, and the summary matches what you get after install.

  • Min Iyer· Dec 8, 2024

    Solid pick for teams standardizing on skills: task-execution-engine is focused, and the summary matches what you get after install.

  • Oshnikdeep· Nov 27, 2024

    We added task-execution-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Noor Brown· Nov 27, 2024

    We added task-execution-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Noah Kapoor· Nov 3, 2024

    task-execution-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Olivia Choi· Oct 22, 2024

    We added task-execution-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ganesh Mohane· Oct 18, 2024

    task-execution-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Mia Perez· Oct 18, 2024

    task-execution-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Omar Gonzalez· Sep 13, 2024

    task-execution-engine reduced setup friction for our internal harness; good balance of opinion and flexibility.

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