ai-assisted-development

name	ai-assisted-development
description	Orchestrate AI coding agents, human reviewers, CI, and delivery workflows for professional software work. Use when coordinating AI-assisted planning, implementation, code review, modernization, documentation, or multi-agent development.
metadata	{"portable":true,"compatible_with":["Codex","codex"]}

Platform Notes

• Optional helper plugins may help in some environments, but they must not be treated as required for this skill.

AI-Assisted Development Orchestration

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, [phone].

Use When

• Orchestrate AI coding agents, human reviewers, CI, and delivery workflows for professional software work. Use when coordinating AI-assisted planning, implementation, code review, modernization, documentation, or multi-agent development.
• The task needs reusable judgment, domain constraints, or a proven workflow rather than ad hoc advice.

Do Not Use When

• The task is unrelated to ai-assisted-development or would be better handled by a more specific companion skill.
• The request only needs a trivial answer and none of this skill's constraints or references materially help.

Required Inputs

• Gather relevant project context, constraints, and the concrete problem to solve; load references only as needed.
• Confirm the desired deliverable: design, code, review, migration plan, audit, or documentation.

Workflow

• Read this SKILL.md first, then load only the referenced deep-dive files that are necessary for the task.
• Apply the ordered guidance, checklists, and decision rules in this skill instead of cherry-picking isolated snippets.
• Produce the deliverable with assumptions, risks, and follow-up work made explicit when they matter.

Quality Standards

• Keep outputs execution-oriented, concise, and aligned with the repository's baseline engineering standards.
• Preserve compatibility with existing project conventions unless the skill explicitly requires a stronger standard.
• Prefer deterministic, reviewable steps over vague advice or tool-specific magic.

Anti-Patterns

• Treating examples as copy-paste truth without checking fit, constraints, or failure modes.
• Loading every reference file by default instead of using progressive disclosure.

Outputs

• A concrete result that fits the task: implementation guidance, review findings, architecture decisions, templates, or generated artifacts.
• Clear assumptions, tradeoffs, or unresolved gaps when the task cannot be completed from available context alone.
• References used, companion skills, or follow-up actions when they materially improve execution.

Evidence Produced

Category	Artifact	Format	Example
Release evidence	AI agent orchestration record	Markdown doc capturing agent assignments, hand-offs, and review checkpoints across the project	docs/ai/agent-orchestration-2026-04-16.md

References

• Use the references/ directory for deep detail after reading the core workflow below.

Overview

Learn to orchestrate multiple AI agents (like Codex, custom sub-agents, or specialized AI tools) to work together effectively in software development.

This skill bridges prompting patterns + orchestration + sub-agent coordination for real-world AI-assisted development.

Operating Doctrine

• Treat AI as a force multiplier inside a disciplined engineering system, not as a replacement for requirements, design, review, tests, security, or ownership.
• Start every AI-assisted task with a concrete outcome, repo constraints, acceptance criteria, and verification command. Do not ask an agent to "improve" broad surfaces without a definition of done.
• Keep humans accountable for architecture, irreversible data changes, production release, security exceptions, licensing/IP decisions, and client commitments.
• Prefer small, reviewable AI work packets: one responsibility, one bounded write scope, one expected evidence artifact.
• Require codebase grounding before edits. The agent must inspect current patterns, interfaces, tests, and failure modes before proposing or changing implementation.

AI Development Workflow

1. Frame: State user value, business value, technical objective, constraints, and acceptance tests.
2. Ground: Read the smallest set of files/docs needed to understand existing behavior.
3. Plan: Split work by ownership boundaries. Identify what can be delegated and what must stay on the critical path.
4. Implement: Make narrow changes that preserve local conventions. Avoid broad rewrites unless requested.
5. Verify: Run focused tests, linters, type checks, migrations, or manual checks that match the blast radius.
6. Review: Inspect diff for hallucinated APIs, over-broad abstractions, hidden state changes, secrets, data leaks, and licensing risks.
7. Record: Capture changed files, commands run, residual risks, and follow-up work.

Agent Assignment Rules

• Use explorers for bounded codebase questions with clear expected outputs.
• Use workers for bounded implementation with disjoint file ownership. Tell workers they are not alone in the codebase and must not revert others' edits.
• Do not delegate the immediate blocking task if the main workflow cannot proceed until it returns.
• Never let two agents write the same files unless one is explicitly reviewing the other's patch.
• For generated code, require the same quality bar as human code: tests, readable names, explicit error handling, and no invented dependencies.

AI Coding Risk Controls

Risk	Control
Hallucinated APIs	Compile/typecheck and inspect imports, method names, schemas, and SDK versions
Plausible but wrong logic	Add examples, regression tests, and domain-specific fixtures
Security regression	Run threat review for auth, tenancy, file IO, network calls, secrets, and prompt injection
IP/license exposure	Avoid copying unknown code; check dependency licenses before adding packages
Context leakage	Keep secrets, credentials, client PII, and proprietary data out of prompts unless explicitly approved
Over-automation	Require human approval for production deploys, destructive changes, payments, emails, and client-facing commitments

Evidence Required

• For code changes: diff summary, tests/checks run, and known gaps.
• For architecture or plans: decision record, alternatives considered, evaluation criteria, and economic rationale.
• For modernization: before/after behavior, migration steps, rollback plan, and compatibility notes.

What you'll learn:

• The 5 orchestration strategies for AI development
• AI-specific coordination patterns (Agent Handoff, Fan-Out/Fan-In, Human-in-the-Loop)
• Real-world examples (MADUUKA, BRIGHTSOMA apps)

Documentation Structure (Tier 2 Deep Dives):

• 📖 orchestration-strategies.md - The 5 core strategies with detailed examples
• 📖 ai-patterns.md - AI-specific orchestration patterns
• 📖 practical-examples.md - Real MADUUKA and BRIGHTSOMA projects

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Additional Guidance

Extended guidance for ai-assisted-development was moved to references/skill-deep-dive.md to keep this entrypoint compact and fast to load.

Use that deep dive for:

• When to Use This Skill
• Core Concepts (Quick Reference)
• The 5 Orchestration Strategies (Summary)
• The 3 AI Orchestration Patterns (Summary)
• Quick Reference: When to Use Which
• Real-World Examples (Summary)
• Practical Workflow: How to Apply This Skill
• Best Practices
• Integration with Other Skills
• Summary