subagent-driven-development

subagent-driven-development is a skill for Claude Code, Codex from StarryCod/cogitum. It costs 17 tokens per session (2,436 once invoked), scanned A, a copy of subagent-driven-development, MIT.

A workflow for carrying out an implementation plan by giving separate tasks to fresh coding subagents. Each task goes through a requirements review and a quality review before the next task continues.

In plain words
What is it for?
Executing plans made of mostly independent tasks, delegating implementation work, and reviewing each task for both requirement compliance and code quality.
Why use it?
It spreads complex work across focused agents and checks each result before moving on. Fresh context can reduce confusion caused by one agent accumulating too much unrelated detail.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/starrycod/cogitum/subagent-driven-development
Any agent
npx skills add StarryCod/cogitum --skill subagent-driven-development
Clone the repo
git clone --depth 1 https://github.com/StarryCod/cogitum

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for subagent-driven-development

README.md
[![agentmods](https://agentmods.dev/badge/skills/starrycod/cogitum/subagent-driven-development.svg)](https://agentmods.dev/skills/starrycod/cogitum/subagent-driven-development)
Your own site
<a href="https://agentmods.dev/skills/starrycod/cogitum/subagent-driven-development"><img src="https://agentmods.dev/badge/skills/starrycod/cogitum/subagent-driven-development.svg" alt="Measured on agentmods" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,436 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00017 $0.02436
Opus 5 $0.00009 $0.01218
Sonnet 5 $0.00003 $0.00487
Haiku 4.5 $0.00002 $0.00244

Measured yesterday against content hash 4c498d15b396, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

subagent-driven-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

95% identical to subagent-driven-development — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

cogitum/data/skills/software-development/subagent-driven-development/SKILL.md · 353 lines

How it starts

The opening of the file, as written. The whole thing — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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:

# Read the plan
read_file("docs/plans/feature-plan.md")

# Create todo list with all tasks
todo([
    {"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 delegate_task with complete context:

delegate_task(
    goal="Implement Task 1: Create User model with email and password_hash fields",
    context="""
    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
    """,
    toolsets=['terminal', 'file']
)

Read the full file on GitHub · 353 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 353 lines · 17 tokens per session scan A 4c498d15b396

Subscribe to this mod's changes

subagent-driven-development is a skill published in the GitHub repository StarryCod/cogitum (11 stars, last pushed 3mo ago), licensed MIT. It adds 17 tokens to every session and 2,436 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to subagent-driven-development, differing in 14 lines, and is treated as a copy.

Related

Other skills, from other repositories

文档协作

引导用户通过结构化的文档共同编写工作流程。当用户想撰写文档、提案、技术规范、决策文档或类似结构化内容时使用。该工作流程帮助用户高效传递上下文,通过迭代优化内容,并验证文档对读者有效。当用户提到写文档、创建提案、起草规范或类似文档任务时触发。.

Tencent/WeKnora · 95 tokens

openmaic-classroom

将 RAG 检索结果、文档块或知识图谱概念转换为 OpenMAIC 互动课程。当用户要求将知识库内容、检索到的文档片段、上传的文档、或知识图谱中的概念批量转换为教学课件/互动课堂时使用此技能。支持纯需求生成、基于 PDF 内容的课程生成、和基于概念图遍历的批量课堂生成。.

Tencent/WeKnora · 102 tokens

weknora-shared

Use when driving a WeKnora RAG server through the weknora CLI as an agent — authenticating, managing knowledge bases / documents / sessions / agents, running search or chat, or interpreting the CLI's JSON envelopes and exit codes. Read this before any other weknora- skill.

Tencent/WeKnora · 68 tokens

weknora-rag-search

Use when retrieving from or asking questions against a WeKnora knowledge base via the weknora CLI — and especially when unsure whether to use chat, session ask, or search chunks for a given goal.

Tencent/WeKnora · 54 tokens

数据处理器

数据处理与分析技能。当用户需要对知识库检索结果进行数据分析、统计计算、格式转换、数据提取或生成报告时使用此技能。支持 Python 脚本执行进行高级数据处理。.

Tencent/WeKnora · 51 tokens

文档分析器

深度分析文档结构和内容。当用户需要分析文档结构、提取关键信息、识别文档类型、进行内容质量评估、或理解文档组织方式时使用此技能。.

Tencent/WeKnora · 51 tokens