subagent-dev

subagent-dev is a skill for Claude Code from yash-gadodia/claude-init. It costs 36 tokens per session (1,129 once invoked), scanned A, original, MIT.

A development workflow that assigns separate implementation tasks to fresh AI subagents and reviews each result in two stages.

In plain words
What is it for?
Use it for plans with at least three mostly independent tasks, such as implementing several unrelated features or fixes in one repository.
Why use it?
Fresh agents receive focused context, while specification and code-quality checks help catch incomplete or poorly implemented work.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents; names the TodoWrite tool.

Part of the claude-init plugin — 12 skills, 7 agents shipped together

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/yash-gadodia/claude-init/subagent-dev
Any agent
npx skills add yash-gadodia/claude-init --skill subagent-dev
Clone the repo
git clone --depth 1 https://github.com/yash-gadodia/claude-init

Made for: Claude Code.

Or install claude-init, the plugin that ships this one along with the rest of its 12 skills, 7 agents.

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-dev

README.md
[![agentmods](https://agentmods.dev/badge/skills/yash-gadodia/claude-init/subagent-dev.svg)](https://agentmods.dev/skills/yash-gadodia/claude-init/subagent-dev)
Your own site
<a href="https://agentmods.dev/skills/yash-gadodia/claude-init/subagent-dev"><img src="https://agentmods.dev/badge/skills/yash-gadodia/claude-init/subagent-dev.svg" alt="Measured on agentmods" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,129 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found 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 5.1 $0.00036 $0.01129
Opus 5 $0.00018 $0.00564
Sonnet 5 $0.00007 $0.00226
Haiku 4.5 $0.00004 $0.00113

Measured 6d ago against content hash 953831c2076d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

subagent-dev 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 6d ago.

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.

templates/skills/subagent-dev/SKILL.md · 113 lines

How it starts

The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Subagent-Driven Development

Execute a plan by dispatching a fresh subagent per task, with two-stage review (spec compliance → code quality) after each.

When to Use

Automatically when:

  • You have a plan with 3+ independent tasks
  • Tasks are mostly independent (don't tightly depend on each other's output)

Fall back to sequential implementation when tasks are tightly coupled or the plan has fewer than 3 tasks.

Why Fresh Subagents

  • No context pollution: Each agent gets exactly the context it needs, not your entire conversation history
  • Better focus: Agent sees only its task spec, relevant code, and project conventions
  • Preserved orchestrator context: You keep your context for coordination instead of burning it on implementation details

The Process

1. Setup

  • Read the plan file ONCE. Extract every task with its full text, file paths, and context upfront. When dispatching each subagent, provide the full task text directly — never make the subagent read the plan file itself.
  • Note any cross-task dependencies or shared context
  • Track progress by checking off each task's - [ ] in the plan file as it completes — don't rely on TodoWrite/task-tracking tools, which newer models no longer have by default

2. Per Task

For each task:
  a. Dispatch IMPLEMENTATION subagent with:
     - Full task text (exact file paths, expected behavior, test requirements)
     - Project context (test commands, conventions, patterns)
     - Instruction: follow TDD (RED-GREEN-REFACTOR)

  b. If implementer asks questions → answer, re-dispatch
     If implementer reports BLOCKED → assess: context problem? too complex? plan wrong?

  c. Dispatch SPEC REVIEW subagent with:
     - The task spec
     - The git diff of changes made
     - Question: "Does this implementation match the spec? Missing anything? Added anything not requested?"
     - WITHHOLD the implementer's summary and conclusions — spec + diff only.
       Handing the reviewer the claim ("done, all tests pass") biases it toward
       agreement. Fresh eyes on raw evidence.

  d. If spec review fails → implementer fixes → spec review again

  e. Dispatch CODE QUALITY review subagent with:
     - The changed files
     - Question: "Bugs? Security? Convention violations? Test quality?"

  f. If quality review fails → implementer fixes → quality review again

  g. Mark task complete in the plan file

Read the full file on GitHub · 113 lines

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. 6d ago First seen · 113 lines · 36 tokens per session scan A 953831c2076d

Subscribe to this mod's changes

subagent-dev is a skill published in the GitHub repository yash-gadodia/claude-init (5 stars, last pushed 5d ago), licensed MIT. It adds 36 tokens to every session and 1,129 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Related

Other skills, from other repositories

akf-trust-metadata

The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.

sickn33/agentic-awesome-skills · 60 tokens

agent-session-monitor

Real-time agent conversation monitoring - monitors Higress access logs, aggregates conversations by session, tracks token usage. Supports web interface for viewing complete conversation history and costs. Use when users ask about current session token consumption, conversation history, or cost statistics.

higress-group/higress · 53 tokens

higress-daily-report

生成 Higress 项目每日报告,追踪 issue/PR 动态,沉淀问题处理经验,驱动社区问题闭环。用于生成日报、跟进 issue、记录解决方案。.

higress-group/higress · 50 tokens

issue-spec-workflow

Use issue-spec to plan and implement a change through exact-head human review handoff.

higress-group/higress · 22 tokens

issue-spec-github

Use GitHub CLI for GitHub issues, pull requests, CI runs, and API queries that issue-spec does not wrap.

higress-group/higress · 30 tokens

open-ontologies

AI-native ontology engineering using 50+ MCP tools backed by an in-memory Oxigraph triple store. Build, validate, query, and govern RDF/OWL ontologies with a generate-validate-iterate loop. Use when building ontologies, knowledge graphs, RDF data, SPARQL queries, BORO/4D modeling, SHACL validation, clinical…

fabio-rovai/open-ontologies · 110 tokens