Execute implementation tasks directly from design documents. Tasks are managed as markdown checkboxes - no separate session files needed.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontask-execution-engineExecute the skills CLI command in your project's root directory to begin installation:
Fetches task-execution-engine from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate task-execution-engine. Access via /task-execution-engine in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Execute implementation tasks directly from design documents. Tasks are managed as markdown checkboxes - no separate session files needed.
# 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>
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.
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
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
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.
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]
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
task-execution-engine has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: task-execution-engine is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: task-execution-engine is focused, and the summary matches what you get after install.
We added task-execution-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added task-execution-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
task-execution-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added task-execution-engine from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
task-execution-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
task-execution-engine fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
task-execution-engine reduced setup friction for our internal harness; good balance of opinion and flexibility.
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