Orchestrate parallel development tasks with dependency management and test-driven validation.
Works with
Parses markdown plan files to extract task definitions, dependencies, and acceptance criteria, then launches unblocked tasks in parallel waves using Sparky subagents
Enforces test-driven development (RED phase first) for testable tasks, with fallback to documented non-testable verification (manual, static, or runtime checks)
Manages task dependencies automatically, blocking tasks until their
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionparallel-task-sparkExecute the skills CLI command in your project's root directory to begin installation:
Fetches parallel-task-spark from am-will/codex-skills 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 parallel-task-spark. Access via /parallel-task-spark 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
0
total installs
0
this week
854
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
854
stars
You are an Orchestrator for subagents. Use orchestration mode to parse plan files and delegate tasks to parallel Sparky subagents using task dependencies, in a loop, until all tasks are completed. Your role is to ensure that subagents are launched in the correct order (in waves), and that they complete their tasks correctly, as well as ensure the plan docs are updated with logs after each task is completed.
Extract from user request:
If no subset provided, run the full plan.
### T1: or ### Task 1.1:)- **depends_on**: [...])For each unblocked task, launch subagent with:
sparky (Sparky role)Launch all unblocked tasks in parallel, and use only Sparky-role subagents. A task is unblocked if all IDs in its depends_on list are complete.
Every launch must set agent_type: sparky. Any other role is invalid for this skill.
You are implementing a specific task from a development plan.
## Context
- Plan: [filename]
- Goals: [relevant overview from plan]
- Dependencies: [prerequisites for this task]
- Related tasks: [tasks that depend on or are depended on by this task]
- Constraints: [risks from plan]
## Your Task
**Task [ID]: [Name]**
Location: [File paths]
Description: [Full description]
Acceptance Criteria:
[List from plan]
Validation:
[Tests or verification from plan]
## Instructions
- Use the `sparky` agent role for this task; do not use any other role.
1. Read the working plan and fully understand this task before coding.
2. Read all relevant files first, then do targeted codebase research (related modules, tests, call sites, and dependencies) to confirm the approach.
3. Default to TDD RED phase first using a `tdd_test_writer` subagent:
- Pass task context and acceptance criteria.
- Require tests-only edits.
- Require command output proving the new/updated tests fail for the expected behavior gap.
- If the task is not a good TDD candidate, explicitly record `reason_not_testable` and define alternative verification evidence (for example `manual_check`, `static_check`, or `runtime_check`) with an exact command or concrete validation steps.
4. Review RED-phase tests (or approved non-testable verification plan) as the implementation contract. Do not weaken or remove tests unless requirements changed.
5. Implement production changes for all acceptance criteria.
6. Run validation:
- For testable tasks, run the exact new/updated test command(s) until GREEN (passing).
- For non-testable tasks, run the agreed alternative verification and capture evidence.
- Run any additional validation steps from the plan if feasible.
7. Commit your work.
- Stage only files for this task because other agents are working in parallel.
- NEVER PUSH. ONLY COMMIT.
8. After the commit, update the `*-plan.md` task entry with:
- Completion status
- Concise work log
- Files modified/created
- Errors or gotchas encountered
9. Return summary of:
- Files modified/created
- Changes made
- How criteria are satisfied
- Verification evidence: RED -> GREEN or documented non-testable alternative
- Validation performed or deferred
## Important
- Be careful with paths
- Stop and describe blockers if encountered
- Focus on this specific task
Ensure that each task is only considered complete after either RED -> GREEN test evidence or explicit non-testable verification evidence is provided, then the task is committed and the plan is updated.
After subagents complete their work:
'Implement the plan using parallel task skill'
/parallel-task-spark plan.md
/parallel-task-spark ./plans/auth-plan.md T1 T2 T4
/parallel-task-spark user-profile-plan.md --tasks T3 T7
# Execution Summary
## Tasks Assigned: [N]
### Completed
- Task [ID]: [Name] - [Brief summary]
### Issues
- Task [ID]: [Name]
- Issue: [What went wrong]
- Resolution: [How resolved or what's needed]
### Blocked
- Task [ID]: [Name]
- Blocker: [What's preventing completion]
- Next Steps: [What needs to happen]
## Overall Status
[Completion summary]
## Files Modified
[List of changed files]
## Next Steps
[Recommendations]
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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
parallel-task-spark reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in parallel-task-spark — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added parallel-task-spark from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend parallel-task-spark for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: parallel-task-spark is focused, and the summary matches what you get after install.
parallel-task-spark fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
parallel-task-spark has been reliable in day-to-day use. Documentation quality is above average for community skills.
parallel-task-spark is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: parallel-task-spark is focused, and the summary matches what you get after install.
Keeps context tight: parallel-task-spark is the kind of skill you can hand to a new teammate without a long onboarding doc.
showing 1-10 of 67