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
- 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):
- 📖 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
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
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.
1---2name: ai-assisted-development3description: Use when coordinating AI-assisted planning, implementation, review, modernisation, documentation, human approval, CI evidence, or bounded multi-agent software work.4---56## Platform Notes78- Optional helper plugins may help in some environments, but they must not be treated as required for this skill.910# AI-Assisted Development Orchestration11Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.1213<!-- dual-compat-start -->14## Use When1516- 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.1718## Evidence Produced1920| Category | Artifact | Format | Example |21|----------|----------|--------|---------|22| 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` |2324## References2526- Use the `references/` directory for deep detail after reading the core workflow below.27<!-- dual-compat-end -->28## Overview2930Learn to **orchestrate multiple AI agents** (like Codex, custom sub-agents, or specialized AI tools) to work together effectively in software development.3132This skill bridges **prompting patterns** + **orchestration** + **sub-agent coordination** for real-world AI-assisted development.3334## Operating Doctrine3536- Treat AI as a force multiplier inside a disciplined engineering system, not as a replacement for requirements, design, review, tests, security, or ownership.37- 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.38- Keep humans accountable for architecture, irreversible data changes, production release, security exceptions, licensing/IP decisions, and client commitments.39- Prefer small, reviewable AI work packets: one responsibility, one bounded write scope, one expected evidence artifact.40- Require codebase grounding before edits. The agent must inspect current patterns, interfaces, tests, and failure modes before proposing or changing implementation.4142## AI Development Workflow43441. **Frame**: State user value, business value, technical objective, constraints, and acceptance tests.452. **Ground**: Read the smallest set of files/docs needed to understand existing behavior.463. **Plan**: Split work by ownership boundaries. Identify what can be delegated and what must stay on the critical path.474. **Implement**: Make narrow changes that preserve local conventions. Avoid broad rewrites unless requested.485. **Verify**: Run focused tests, linters, type checks, migrations, or manual checks that match the blast radius.496. **Review**: Inspect diff for hallucinated APIs, over-broad abstractions, hidden state changes, secrets, data leaks, and licensing risks.507. **Record**: Capture changed files, commands run, residual risks, and follow-up work.5152## Agent Assignment Rules5354- Use explorers for bounded codebase questions with clear expected outputs.55- Use workers for bounded implementation with disjoint file ownership. Tell workers they are not alone in the codebase and must not revert others' edits.56- Do not delegate the immediate blocking task if the main workflow cannot proceed until it returns.57- Never let two agents write the same files unless one is explicitly reviewing the other's patch.58- For generated code, require the same quality bar as human code: tests, readable names, explicit error handling, and no invented dependencies.5960## AI Coding Risk Controls6162| Risk | Control |63|---|---|64| Hallucinated APIs | Compile/typecheck and inspect imports, method names, schemas, and SDK versions |65| Plausible but wrong logic | Add examples, regression tests, and domain-specific fixtures |66| Security regression | Run threat review for auth, tenancy, file IO, network calls, secrets, and prompt injection |67| IP/license exposure | Avoid copying unknown code; check dependency licenses before adding packages |68| Context leakage | Keep secrets, credentials, client PII, and proprietary data out of prompts unless explicitly approved |69| Over-automation | Require human approval for production deploys, destructive changes, payments, emails, and client-facing commitments |7071## Evidence Required7273- For code changes: diff summary, tests/checks run, and known gaps.74- For architecture or plans: decision record, alternatives considered, evaluation criteria, and economic rationale.75- For modernization: before/after behavior, migration steps, rollback plan, and compatibility notes.7677**What you'll learn:**78- The 5 orchestration strategies for AI development79- AI-specific coordination patterns (Agent Handoff, Fan-Out/Fan-In, Human-in-the-Loop)80- Real-world examples (MADUUKA, BRIGHTSOMA apps)8182**Documentation Structure (Tier 2 Deep Dives):**83- 📖 **[orchestration-strategies.md](references/orchestration-strategies.md)** - The 5 core strategies with detailed examples84- 📖 **[ai-patterns.md](references/ai-patterns.md)** - AI-specific orchestration patterns85- 📖 **[practical-examples.md](references/practical-examples.md)** - Real MADUUKA and BRIGHTSOMA projects8687---8889## Additional Guidance9091Extended 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.9293Use that deep dive for:94- `When to Use This Skill`95- `Core Concepts (Quick Reference)`96- `The 5 Orchestration Strategies (Summary)`97- `The 3 AI Orchestration Patterns (Summary)`98- `Quick Reference: When to Use Which`99- `Real-World Examples (Summary)`100- `Practical Workflow: How to Apply This Skill`101- `Best Practices`102- `Integration with Other Skills`103- `Summary`104105## Decision Rules106107| Condition | Action |108|---|---|109| Workstreams are independent and bounded | Delegate with explicit outputs |110| Change is sensitive or destructive | Require approval and independent review |111| Agent evidence conflicts | Reproduce before reconciliation |112113## Capability Contract114115Read and search are required. Editing, execution, delegation, and external access require parent-task authorisation.116117## Degraded Mode118119Fallback: without delegation, execute sequentially. Without execution, return patches and commands, never fabricated CI evidence.120121## Domain Anti-Patterns122123- Delegating overlapping edits without ownership.124- Accepting success claims without evidence.125- Giving mutation permissions to review tasks.126- Parallelising a dependency chain.127- Hiding unresolved disagreement during synthesis.128## Inputs129| Artefact | Required? | Purpose |130|---|---|---|131| Development task, repository context, AI-use policy, and review boundary | yes | Bound AI-assisted work |132## Outputs133- Produce reviewed code or guidance with provenance, tests, human decisions, and unresolved risks.134135## Book-derived additions136137For interaction modes, execution records, prompt/context discipline, and trust138ramps, load [book-informed AI collaboration and execution](references/book-informed-ai-collaboration-and-execution.md).