subagent-driven-development
Execute plans via subagents with a two-stage review (spec then quality).
Skill by Astroreen
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: agent-building
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md and code. Checked Nov 1, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/subagent-driven-development
What's inside
- Skill name
subagent-driven-development- Description
- Execute plans via subagents with a two-stage review (spec then quality).
- License
- MIT
- Instructions
- 341 lines in SKILL.md · view on GitHub
Files (3)
- SKILL.md
- context-budget-discipline.md · references/
- gates-taxonomy.md · references/
SKILL.md (first lines)
# Subagent-Driven Development
## Overview
Execute implementation plans by dispatching fresh subagents per task with systematic two-stage review.
**Core principle:** Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration.
## When to Use
Use this skill when:
- You have an implementation plan (from writing-plans skill or user requirements)
- Tasks are mostly independent
- Quality and spec compliance are important
- You want automated review between tasks
**vs. manual execution:**
- Fresh context per task (no confusion from accumulated state)
- Automated review process catches issues early
- Consistent quality checks across all tasks
- Subagents can ask questions before starting work
## The Process
### 1. Read and Parse Plan
Read the plan file. Extract ALL tasks with their full text and context upfront. Create a todo list:
```python
# Read the plan
read("docs/plans/feature-plan.md")
# Create todo list with all tasks
todowrite([
{"id": "task-1", "content": "Create User model with email field", "status": "pending"},
{"id": "task-2", "content": "Add password hashing utility", "status": "pending"},
{"id": "task-3", "content": "Create login endpoint", "status": "pending"},
])
```
**Key:** Read the plan ONCE. Extract everything. Don't make subagents read the plan file — provide the full task text directly in context.
### 2. Per-Task Workflow
For EACH task in the plan:
#### Step 1: Dispatch Implementer Subagent
Use the `task` tool with complete context:
```python
task(
subagent_type="Sisyphus-Junior",
description="Implement Task 1: User model",
prompt="""
TASK FROM PLAN:
- Create: src/models/user.py
- Add User class with email (str) and password_hash (str) fields
- Use bcrypt for password hashing
- Include __repr__ for debugging
FOLLOW TDD:
1. Write failing test in tests/models/test_user.py
2. Run: pytest tests/models/test_user.py -v (verify FAIL)
3. Write minimal implementation
4. Run: pytest tests/models/test_user.py -v (verify PASS)
5. Run: pytest tests/ -q (verify no regressions)
6. Commit: git add -A && git commit -m "feat: add User model with password hashing"
PROJECT CONTEXT:
- Python 3.11, Flask app in src/app.py
- Existing models in src/models/
- Tests use pytest, run from project root
- bcrypt already in requirements.txt
"""
)
```
#### Step 2: Dispatch Spec Compliance Reviewer
After the implementer completes, verify against the original spec:
```python
task(
subagent_type="Sisyphus-Junior",
description="Review spec compliance",
prompt="""
ORIGINAL TASK SPEC:
- Create src/models/user.py with User class
- Fields: email (str), password_hash (str)
- Use bcrypt for password hashing
- Include __repr__
CHECK:
- [ ] All requirements from spec implemented?
- [ ] File paths match spec?
- [ ] Function signatures match spec?
- [ ] Behavior matches expected?
- [ ] Nothing extra added (no scope creep)?
OUTPUT: PASS or list of specific spec gaps to fix.
"""
)
```
**If spec issues found:** Fix gaps, then re-run spec review. Continue only when spec-compliant.
#### Step 3: Dispatch Code Quality Reviewer
After spec compliance passes:
```python
task(
subagent_type="Sisyphus-Junior",
description="Review code quality for Task 1",
prompt="""
FILES TO REVIEW:
- src/models/user.py
- tests/models/test_user.pyFrom Astroreen/astrocode, MIT.
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Signals
- Stars
- 0
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Oct 10, 2026 (22 days ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Astroreen/astrocode
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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- 0.559score
docx
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Create, read, edit and verify Word documents (.docx) with local CLI tools only — LibreOffice, unzip/zip, no pandoc required.
SkillAFreshness: AClaude Code +9 - 0.558score
systematic-debugging
Astroreen
4-phase root cause debugging: understand bugs before fixing.
SkillAFreshness: AClaude Code +9 - 0.555score
spike
Astroreen
Throwaway experiments to validate an idea before build.
SkillAFreshness: AClaude Code +9 - 0.525score
test-driven-development
Astroreen
TDD: enforce RED-GREEN-REFACTOR, tests before code.
SkillAFreshness: AClaude Code +9 - 0.516score
commit-message
Astroreen
Generate a Conventional Commits message from the staged diff.
SkillBFreshness: BClaude Code +9 - 0.516score
verify-before-done
Astroreen
Evidence gate checklist before claiming a task is done.
SkillBFreshness: BClaude Code +9
Done for you
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Chalk Labs is a two-founder AI and growth studio. We build AI agents, CRMs and MVPs for Web3 and AI companies, and have shipped 50+ product and token launches.
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Press we've placed: Forbes · CoinDesk · Decrypt · Cointelegraph · The Block
Rahil Jain, Co-founder, Chalk Labs. Ex-Goldman Sachs engineer. You talk to the person who builds it.
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Cite this page
subagent-driven-development skill. Agent Mart. https://agentmart.dev/skills/astroreen-subagent-driven-development (last updated 2026-10-24)