Sp subagent driven development
Skill aAAaqwq/AGI-Super-Team/skills/sp-subagent-driven-development
Execute implementation plan by dispatching fresh subagent for each task, with code review between tasksFrom its SKILL.md
npx -y skills add aAAaqwq/AGI-Super-Team --skill sp-subagent-driven-developmentAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
4.8 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it
Subagent-Driven Development
Execute plan by dispatching fresh subagent per task, with code review after each.
Core principle: Fresh subagent per task + review between tasks = high quality, fast iteration
Overview
vs. Executing Plans (parallel session):
- Same session (no context switch)
- Fresh subagent per task (no context pollution)
- Code review after each task (catch issues early)
- Faster iteration (no human-in-loop between tasks)
When to use:
- Staying in this session
- Tasks are mostly independent
- Want continuous progress with quality gates
When NOT to use:
- Need to review plan first (use executing-plans)
- Tasks are tightly coupled (manual execution better)
- Plan needs revision (brainstorm first)
The Process
1. Load Plan
Read plan file, create TodoWrite with all tasks.
2. Execute Task with Subagent
For each task:
Dispatch fresh subagent:
Task tool (general-purpose):
description: "Implement Task N: [task name]"
prompt: |
You are implementing Task N from [plan-file].
Read that task carefully. Your job is to:
1. Implement exactly what the task specifies
2. Write tests (following TDD if task says to)
3. Verify implementation works
4. Commit your work
5. Report back
Work from: [directory]
Report: What you implemented, what you tested, test results, files changed, any issues
Subagent reports back with summary of work.
3. Review Subagent's Work
Dispatch code-reviewer subagent:
Task tool (code-reviewer):
Use template at skills/collaboration/requesting-code-review/code-reviewer.md
WHAT_WAS_IMPLEMENTED: [from subagent's report]
PLAN_OR_REQUIREMENTS: Task N from [plan-file]
BASE_SHA: [commit before task]
HEAD_SHA: [current commit]
DESCRIPTION: [task summary]
Code reviewer returns: Strengths, Issues (Critical/Important/Minor), Assessment
4. Apply Review Feedback
If issues found:
- Fix Critical issues immediately
- Fix Important issues before next task
- Note Minor issues
Dispatch follow-up subagent if needed:
"Fix issues from code review: [list issues]"
5. Mark Complete, Next Task
- Mark task as completed in TodoWrite
- Move to next task
- Repeat steps 2-5
6. Final Review
After all tasks complete, dispatch final code-reviewer:
- Reviews entire implementation
- Checks all plan requirements met
- Validates overall architecture
7. Complete Development
After final review passes:
- Announce: "I'm using the Finishing a Development Branch skill to complete this work."
- Switch to skills/collaboration/finishing-a-development-branch
- Follow that skill to verify tests, present options, execute choice
Example Workflow
You: I'm using Subagent-Driven Development to execute this plan.
[Load plan, create TodoWrite]
Task 1: Hook installation script
[Dispatch implementation subagent]
Subagent: Implemented install-hook with tests, 5/5 passing
[Get git SHAs, dispatch code-reviewer]
Reviewer: Strengths: Good test coverage. Issues: None. Ready.
[Mark Task 1 complete]
Task 2: Recovery modes
[Dispatch implementation subagent]
Subagent: Added verify/repair, 8/8 tests passing
[Dispatch code-reviewer]
Reviewer: Strengths: Solid. Issues (Important): Missing progress reporting
[Dispatch fix subagent]
Fix subagent: Added progress every 100 conversations
[Verify fix, mark Task 2 complete]
...
[After all tasks]
[Dispatch final code-reviewer]
Final reviewer: All requirements met, ready to merge
Done!
Advantages
vs. Manual execution:
- Subagents follow TDD naturally
- Fresh context per task (no confusion)
- Parallel-safe (subagents don't interfere)
vs. Executing Plans:
- Same session (no handoff)
- Continuous progress (no waiting)
- Review checkpoints automatic
Cost:
- More subagent invocations
- But catches issues early (cheaper than debugging later)
Red Flags
Never:
- Skip code review between tasks
- Proceed with unfixed Critical issues
- Dispatch multiple implementation subagents in parallel (conflicts)
- Implement without reading plan task
If subagent fails task:
- Dispatch fix subagent with specific instructions
- Don't try to fix manually (context pollution)
Integration
Pairs with:
- skills/collaboration/writing-plans (creates the plan)
- skills/collaboration/requesting-code-review (review template)
- skills/testing/test-driven-development (subagents follow this)
Alternative to:
- skills/collaboration/executing-plans (parallel session)
See code-reviewer template: skills/collaboration/requesting-code-review/code-reviewer.md
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.
Gives 4 of the 12 instructions most context ai engineering skills give in ~1.1k tokens
Counted across 1,328 of the 2,349 authors here whose files we hold, read 2026-09-06
- Dispatch a fresh subagent for each taskhere, and in 76 of 1328, across 59 files
- Perform spec compliance review before code quality reviewin 44 of 1328, across 34 files
- Dispatch a final code reviewer after all taskshere, and in 38 of 1328, across 26 files
- Answer subagent questions before allowing implementationin 36 of 1328, across 26 files
- Use the least powerful model capable of the taskin 33 of 1328, across 26 files
- Create a TodoWrite list for all taskshere, and in 32 of 1328, across 22 files
- Perform a task review after each implementationhere, and in 31 of 1328, across 24 files
- Extract all tasks and context from the planin 29 of 1328, across 20 files
- Provide full task text to subagentsin 28 of 1328, across 20 files
- Use git worktrees for isolated workspacesin 25 of 1328, across 20 files
- Specify the model explicitly when dispatching a subagentin 23 of 1328, across 18 files
- Execute all tasks from the plan without stoppingin 21 of 1328, across 16 files
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.