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subagent-driven-development
Use when executing implementation plans with independent tasks in the current session
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Use when executing implementation plans with independent tasks in the current session
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | subagent-driven-development |
| description | Use when executing implementation plans with independent tasks in the current session |
Execute plan by dispatching fresh subagent per task, with two-stage review after each: spec compliance review first, then code quality review.
Why subagents: You delegate tasks to specialized agents with isolated context. By precisely crafting their instructions and context, you ensure they stay focused and succeed at their task. They should never inherit your session's context or history — you construct exactly what they need. This also preserves your own context for coordination work.
Core principle: Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration
Continuous execution: Do not pause to check in with your human partner between tasks. Execute all tasks from the plan without stopping. The only reasons to stop are: BLOCKED status you cannot resolve, ambiguity that genuinely prevents progress, or all tasks complete. "Should I continue?" prompts and progress summaries waste their time — they asked you to execute the plan, so execute it.
digraph when_to_use {
"Have implementation plan?" [shape=diamond];
"Tasks mostly independent?" [shape=diamond];
"Stay in this session?" [shape=diamond];
"subagent-driven-development" [shape=box];
"executing-plans" [shape=box];
"Manual execution or brainstorm first" [shape=box];
"Have implementation plan?" -> "Tasks mostly independent?" [label="yes"];
"Have implementation plan?" -> "Manual execution or brainstorm first" [label="no"];
"Tasks mostly independent?" -> "Stay in this session?" [label="yes"];
"Tasks mostly independent?" -> "Manual execution or brainstorm first" [label="no - tightly coupled"];
"Stay in this session?" -> "subagent-driven-development" [label="yes"];
"Stay in this session?" -> "executing-plans" [label="no - parallel session"];
}
vs. Executing Plans (parallel session):
digraph process {
rankdir=TB;
subgraph cluster_per_task {
label="Per Task";
"Dispatch implementer subagent (./implementer-prompt.md)" [shape=box];
"Implementer subagent asks questions?" [shape=diamond];
"Answer questions, provide context" [shape=box];
"Implementer subagent implements, tests, commits, self-reviews" [shape=box];
"Dispatch spec reviewer subagent (./spec-reviewer-prompt.md)" [shape=box];
"Spec reviewer subagent confirms code matches spec?" [shape=diamond];
"Implementer subagent fixes spec gaps" [shape=box];
"Dispatch code quality reviewer subagent (./code-quality-reviewer-prompt.md)" [shape=box];
"Code quality reviewer subagent approves?" [shape=diamond];
"Implementer subagent fixes quality issues" [shape=box];
"Mark task complete in TodoWrite" [shape=box];
}
"Read plan, extract all tasks with full text, note context, create TodoWrite" [shape=box];
"More tasks remain?" [shape=diamond];
"Dispatch final code reviewer subagent for entire implementation" [shape=box];
"Use superpowers:finishing-a-development-branch" [shape=box style=filled fillcolor=lightgreen];
"Read plan, extract all tasks with full text, note context, create TodoWrite" -> "Dispatch implementer subagent (./implementer-prompt.md)";
"Dispatch implementer subagent (./implementer-prompt.md)" -> "Implementer subagent asks questions?";
"Implementer subagent asks questions?" -> "Answer questions, provide context" [label="yes"];
"Answer questions, provide context" -> "Dispatch implementer subagent (./implementer-prompt.md)";
"Implementer subagent asks questions?" -> "Implementer subagent implements, tests, commits, self-reviews" [label="no"];
"Implementer subagent implements, tests, commits, self-reviews" -> "Dispatch spec reviewer subagent (./spec-reviewer-prompt.md)";
"Dispatch spec reviewer subagent (./spec-reviewer-prompt.md)" -> "Spec reviewer subagent confirms code matches spec?";
"Spec reviewer subagent confirms code matches spec?" -> "Implementer subagent fixes spec gaps" [label="no"];
"Implementer subagent fixes spec gaps" -> "Dispatch spec reviewer subagent (./spec-reviewer-prompt.md)" [label="re-review"];
"Spec reviewer subagent confirms code matches spec?" -> "Dispatch code quality reviewer subagent (./code-quality-reviewer-prompt.md)" [label="yes"];
"Dispatch code quality reviewer subagent (./code-quality-reviewer-prompt.md)" -> "Code quality reviewer subagent approves?";
"Code quality reviewer subagent approves?" -> "Implementer subagent fixes quality issues" [label="no"];
"Implementer subagent fixes quality issues" -> "Dispatch code quality reviewer subagent (./code-quality-reviewer-prompt.md)" [label="re-review"];
"Code quality reviewer subagent approves?" -> "Mark task complete in TodoWrite" [label="yes"];
"Mark task complete in TodoWrite" -> "More tasks remain?";
"More tasks remain?" -> "Dispatch implementer subagent (./implementer-prompt.md)" [label="yes"];
"More tasks remain?" -> "Dispatch final code reviewer subagent for entire implementation" [label="no"];
"Dispatch final code reviewer subagent for entire implementation" -> "Use superpowers:finishing-a-development-branch";
}
Use the least powerful model that can handle each role to conserve cost and increase speed.
Mechanical implementation tasks (isolated functions, clear specs, 1-2 files): use a fast, cheap model. Most implementation tasks are mechanical when the plan is well-specified.
Integration and judgment tasks (multi-file coordination, pattern matching, debugging): use a standard model.
Architecture, design, and review tasks: use the most capable available model.
Task complexity signals:
Implementer subagents report one of four statuses. Handle each appropriately:
DONE: Proceed to spec compliance review.
DONE_WITH_CONCERNS: The implementer completed the work but flagged doubts. Read the concerns before proceeding. If the concerns are about correctness or scope, address them before review. If they're observations (e.g., "this file is getting large"), note them and proceed to review.
NEEDS_CONTEXT: The implementer needs information that wasn't provided. Provide the missing context and re-dispatch.
BLOCKED: The implementer cannot complete the task. Assess the blocker:
Never ignore an escalation or force the same model to retry without changes. If the implementer said it's stuck, something needs to change.
./implementer-prompt.md - Dispatch implementer subagent./spec-reviewer-prompt.md - Dispatch spec compliance reviewer subagent./code-quality-reviewer-prompt.md - Dispatch code quality reviewer subagentYou: I'm using Subagent-Driven Development to execute this plan.
[Read plan file once: docs/superpowers/plans/feature-plan.md]
[Extract all 5 tasks with full text and context]
[Create TodoWrite with all tasks]
Task 1: Hook installation script
[Get Task 1 text and context (already extracted)]
[Dispatch implementation subagent with full task text + context]
Implementer: "Before I begin - should the hook be installed at user or system level?"
You: "User level (~/.config/superpowers/hooks/)"
Implementer: "Got it. Implementing now..."
[Later] Implementer:
- Implemented install-hook command
- Added tests, 5/5 passing
- Self-review: Found I missed --force flag, added it
- Committed
[Dispatch spec compliance reviewer]
Spec reviewer: ✅ Spec compliant - all requirements met, nothing extra
[Get git SHAs, dispatch code quality reviewer]
Code reviewer: Strengths: Good test coverage, clean. Issues: None. Approved.
[Mark Task 1 complete]
Task 2: Recovery modes
[Get Task 2 text and context (already extracted)]
[Dispatch implementation subagent with full task text + context]
Implementer: [No questions, proceeds]
Implementer:
- Added verify/repair modes
- 8/8 tests passing
- Self-review: All good
- Committed
[Dispatch spec compliance reviewer]
Spec reviewer: ❌ Issues:
- Missing: Progress reporting (spec says "report every 100 items")
- Extra: Added --json flag (not requested)
[Implementer fixes issues]
Implementer: Removed --json flag, added progress reporting
[Spec reviewer reviews again]
Spec reviewer: ✅ Spec compliant now
[Dispatch code quality reviewer]
Code reviewer: Strengths: Solid. Issues (Important): Magic number (100)
[Implementer fixes]
Implementer: Extracted PROGRESS_INTERVAL constant
[Code reviewer reviews again]
Code reviewer: ✅ Approved
[Mark Task 2 complete]
...
[After all tasks]
[Dispatch final code-reviewer]
Final reviewer: All requirements met, ready to merge
Done!
vs. Manual execution:
vs. Executing Plans:
Efficiency gains:
Quality gates:
Cost:
Never:
If subagent asks questions:
If reviewer finds issues:
If subagent fails task:
Required workflow skills:
Subagents should use:
Alternative workflow:
Reference and apply design system specifications from the VoltAgent awesome-design-md collection (Stripe, Linear, Vercel, Notion, etc.). Use this skill to initialize DESIGN.md in the project root or audit/format UI styling decisions.
Use when the user needs human-in-the-loop workflows in Airflow (approval/reject, form input, or human-driven branching). Covers ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator. Requires Airflow 3.1+. Does not cover AI/LLM calls (see airflow-ai).
Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead.
Queries data warehouse and answers business questions about data. Handles questions requiring database/warehouse queries including "who uses X", "how many Y", "show me Z", "find customers", "what is the count", data lookups, metrics, trends, or SQL analysis.
Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input/output datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.
Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.