| 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, +256 784 464178.
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
- Frame: State user value, business value, technical objective, constraints, and acceptance tests.
- Ground: Read the smallest set of files/docs needed to understand existing behavior.
- Plan: Split work by ownership boundaries. Identify what can be delegated and what must stay on the critical path.
- Implement: Make narrow changes that preserve local conventions. Avoid broad rewrites unless requested.
- Verify: Run focused tests, linters, type checks, migrations, or manual checks that match the blast radius.
- Review: Inspect diff for hallucinated APIs, over-broad abstractions, hidden state changes, secrets, data leaks, and licensing risks.
- 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):
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 SkillCore Concepts (Quick Reference)The 5 Orchestration Strategies (Summary)The 3 AI Orchestration Patterns (Summary)Quick Reference: When to Use WhichReal-World Examples (Summary)Practical Workflow: How to Apply This SkillBest PracticesIntegration with Other SkillsSummary
