SuperAPI AI Directory
Partner with us中文AI newsFavorites
Skillsai-assisted-development

ai-assisted-development

Coding & development

View repository

Updated 10-16 08:03

About this skill

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

  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):

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