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Subagent Driven Development

Community
HezaoHezao
subagent-driven-development

Execute plans task-by-task with two-stage review.

Overview

PublisherHezaoHezao
Repositorypoirot
Skill namesubagent-driven-development
Stars
252
Forks
26
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HezaoHezao on GitHub. Read the source before you install it.

Installation

Install the Subagent Driven Development AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/HezaoHezao/poirot/tree/master/poirot/backend/agents/skill/builtin_skills/software-development/subagent-driven-development
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HezaoHezao/poirot.git /tmp/poirot
mkdir -p .claude/skills
cp -r /tmp/poirot/poirot/backend/agents/skill/builtin_skills/software-development/subagent-driven-development .claude/skills/subagent-driven-development
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Subagent Driven Development in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Subagent Driven Development on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Subagent Driven Development is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Subagent-Driven Development

Overview

Execute implementation plans task-by-task with systematic two-stage review between tasks.

Core principle: Fresh focus per task + two-stage review (spec then quality) = high quality, fast iteration.

Poirot note: The original skill dispatches a fresh subagent per task via delegate_task. Poirot has no subagents, so this version executes tasks sequentially in the same context with a deliberate context-reset between tasks. The two-stage review methodology is preserved.

When to Use

  • You have an implementation plan (from the plan skill or user requirements)
  • Tasks are mostly independent
  • Quality and spec compliance are important
  • You want automated review between tasks

The Process

1. Read and Parse Plan

Read the plan file. Extract ALL tasks with full text and context upfront:

read_file(".poirot/plans/feature-plan.md")

Create a todo list with all tasks. Read the plan ONCE — don't re-read per task.

2. Per-Task Workflow

For EACH task in the plan:

Step 1: Context Reset

Before starting each task, deliberately reset your focus:

  • Re-read only the current task's description
  • Forget previous tasks' implementation details (they're committed)
  • Treat each task as if a fresh agent is picking it up
Step 2: Implement

Follow the task's steps exactly:

  1. Write the failing test (TDD — see test-driven-development skill)
  2. Run test to verify failure
  3. Write minimal implementation
  4. Run test to verify pass
  5. Run full test suite to check for regressions
  6. Commit
bash("pytest tests/test_feature.py::test_name -v")
# ... implement ...
bash("pytest tests/ -q")
bash("git add -A && git commit -m 'feat: task N description'")
Step 3: Stage 1 Review — Spec Compliance

After implementation, review against the plan:

  • Does the implementation match the task's stated objective?
  • Are all files listed in the task created/modified?
  • Are all tests listed in the task written?
  • Does the implementation do what the task says, not more, not less?

If spec compliance fails: Fix before proceeding to Stage 2.

Step 4: Stage 2 Review — Code Quality

Review the committed diff for quality:

  • No hardcoded secrets, credentials
  • Input validation on user-provided data
  • Error handling for I/O/network/DB
  • No debug print/console.log left behind
  • No commented-out code
  • Tests cover happy path + edge cases
  • Code follows project conventions

Use the requesting-code-review skill's checklist for thoroughness.

If quality review fails: Fix, re-commit, re-review.

Step 5: Proceed to Next Task

Only after both reviews pass:

  1. Mark current task complete in the todo list
  2. Move to next task
  3. Repeat from Step 1 (Context Reset)

3. Handling Failures

Implementation fails (tests don't pass):

  1. Use systematic-debugging skill to find root cause
  2. Fix the root cause
  3. Re-run tests
  4. If 3+ fixes fail → question the approach, discuss with user

Review fails (spec or quality):

  1. Fix the specific issues found
  2. Re-commit
  3. Re-review only the fixed parts

Task is blocked (depends on unfinished work):

  1. Note the blocker
  2. Skip to next independent task
  3. Return to blocked task when dependency is resolved

Two-Stage Review Detail

Stage 1: Spec Compliance

Re-read the task from the plan. Compare against what you actually built:

Task says: "Create User model with email and password_hash fields"
Check:
- [ ] User model exists
- [ ] Has email field
- [ ] Has password_hash field
- [ ] No extra fields not in the task
- [ ] Test exists for the model

Common spec violations:

  • Implementing more than the task asks ("while I'm here" scope creep)
  • Missing a file the task lists
  • Different API shape than the task specifies
  • No test for the feature

Stage 2: Code Quality

Review the git diff for the current task:

bash("git diff HEAD~1 HEAD")

Check for security, correctness, and conventions. Use the requesting-code-review skill's security scan + self-review checklist.

Pitfalls

  • Context accumulation: without subagents, context grows across tasks. If you feel confused about which task you're on, re-read the plan + current task.
  • Scope creep: "while I'm here" edits to previous tasks' code. Don't. Each task is atomic.
  • Skipping review: the two-stage review IS the value of this skill. Without it, you're just implementing sequentially.
  • Re-reviewing everything: after a fix, only re-review the fixed parts, not the entire task.
  • Forgetting to commit: commit after each task. Don't accumulate uncommitted changes across tasks — if a later task breaks, you can't cleanly revert.

Frequently asked questions

What does the Subagent Driven Development AI skill do?

Execute plans task-by-task with two-stage review.

Why use Subagent Driven Development on TypingMind?

Because you install it once and use it with any model. Subagent Driven Development is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Subagent Driven Development in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HezaoHezao/poirot/tree/master/poirot/backend/agents/skill/builtin_skills/software-development/subagent-driven-development. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Subagent Driven Development?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Subagent Driven Development?

As many as you like. As long as a model supports skills, you can use Subagent Driven Development with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Subagent Driven Development AI skill free?

Yes. It is published on GitHub by HezaoHezao under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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