| name | subagent-driven-development |
| description | Use when executing implementation plans with independent tasks in the current session. Dispatches fresh subagent per task with two-stage review (spec compliance, then code quality). |
| argument-hint | Provide the path to the implementation plan to execute. |
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Level 3 - Referenced Supporting Files
Subagent-Driven Development
Execute plan by dispatching fresh subagent per task, with two-stage review after each: spec compliance review first, then code quality review.
Core principle: Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration
When to Use
- You have an implementation plan (from writing-plans skill)
- Tasks are mostly independent
- You want to stay in this session
The Process
- Read plan once — extract all tasks with full text and context
- Create todo list with all tasks
- Per task:
- Dispatch implementer subagent with full task text + scene-setting context
- Answer any questions before letting them proceed
- Implementer implements, tests, commits, self-reviews
- Dispatch spec compliance reviewer subagent
- If spec issues found: implementer fixes → re-review
- Dispatch code quality reviewer subagent
- If quality issues found: implementer fixes → re-review
- Mark task complete
- After all tasks: dispatch final code reviewer for entire implementation
- Run
make check to confirm everything is green
Scene-Setting Context
Every implementer subagent dispatch MUST include:
Repository: DevCD (local-first Developer Context Daemon)
Language: Python 3.11+, Pydantic v2, FastAPI
Validation: `make check` (lint + typecheck + pytest)
Architecture: Vertical Slice — each feature lives in devcd/slices/<slice>/{models,service,api}.py
Tests: tests/test_<slice>.py, use pytest-asyncio for async
Privacy: mutations denied by default, observation allowed
Model Selection
- Mechanical tasks (isolated functions, clear specs, 1-2 files): cheap/fast model
- Integration tasks (multi-file, pattern matching, debugging): standard model
- Architecture/design/review: most capable model
Handling Implementer Status
- DONE: Proceed to spec compliance review
- DONE_WITH_CONCERNS: Read concerns before proceeding. Address correctness/scope concerns first.
- NEEDS_CONTEXT: Provide missing context and re-dispatch
- BLOCKED: Assess blocker — more context, more capable model, smaller task, or escalate to human
Never ignore an escalation or force the same model to retry without changes.
Spec Compliance Review Prompt
Review this implementation against the spec.
Spec requirement: [paste relevant section]
Implementation: [paste code diff or relevant files]
Check:
1. Does the implementation satisfy every requirement in the spec?
2. Are there missing cases or edge conditions?
3. Do types, method signatures, and property names match the spec exactly?
Output: PASS or FAIL with specific issues.
Code Quality Review Prompt
Review this implementation for code quality.
Implementation: [paste code diff]
Context: DevCD slice architecture, Pydantic v2, FastAPI, mypy strict mode
Check:
1. Is the logic minimal and clear? No over-engineering?
2. Are there security issues (input validation, injection, etc.)?
3. Is the code idiomatic Python 3.11+ with full type annotations?
4. Does it follow the slice structure (models / service / api)?
5. Does `make check` pass?
Output: PASS or FAIL with specific issues.
Red Flags
Never:
- Skip reviews (spec compliance OR code quality)
- Proceed with unfixed issues
- Dispatch multiple implementation subagents in parallel (conflicts)
- Make subagent read plan file (provide full text instead)
- Skip scene-setting context
- Accept "close enough" on spec compliance
- Start code quality review before spec compliance is PASS
- Move to next task while either review has open issues
Integration
Required workflow skills:
writing-plans — Creates the plan this skill executes
test-driven-development — Subagents follow TDD for each task
verification-before-completion — Final gate before claiming work is done