# AI Assisted Development > Use when coordinating AI-assisted planning, implementation, review, modernisation, documentation, human approval, CI evidence, or bounded multi-agent software work. - Skill: `peterbamuhigire/ai-assisted-development` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add peterbamuhigire/ai-assisted-development` - Raw SKILL.md: https://api.skillmd.com/api/skills/peterbamuhigire/ai-assisted-development/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: peterbamuhigire (https://skillmd.com/u/peterbamuhigire) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/peterbamuhigire/ai-assisted-development --- ## 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. ## 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](references/orchestration-strategies.md)** - The 5 core strategies with detailed examples - 📖 **[ai-patterns.md](references/ai-patterns.md)** - AI-specific orchestration patterns - 📖 **[practical-examples.md](references/practical-examples.md)** - Real MADUUKA and BRIGHTSOMA projects --- ## Additional Guidance Extended guidance for `ai-assisted-development` was moved to [references/skill-deep-dive.md](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` ## Decision Rules | Condition | Action | |---|---| | Workstreams are independent and bounded | Delegate with explicit outputs | | Change is sensitive or destructive | Require approval and independent review | | Agent evidence conflicts | Reproduce before reconciliation | ## Capability Contract Read and search are required. Editing, execution, delegation, and external access require parent-task authorisation. ## Degraded Mode Fallback: without delegation, execute sequentially. Without execution, return patches and commands, never fabricated CI evidence. ## Domain Anti-Patterns - Delegating overlapping edits without ownership. - Accepting success claims without evidence. - Giving mutation permissions to review tasks. - Parallelising a dependency chain. - Hiding unresolved disagreement during synthesis. ## Inputs | Artefact | Required? | Purpose | |---|---|---| | Development task, repository context, AI-use policy, and review boundary | yes | Bound AI-assisted work | ## Outputs - Produce reviewed code or guidance with provenance, tests, human decisions, and unresolved risks. ## Book-derived additions For interaction modes, execution records, prompt/context discipline, and trust ramps, load [book-informed AI collaboration and execution](references/book-informed-ai-collaboration-and-execution.md).