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.4metadata:5 portable: true6 compatible_with:7 - claude-code8 - codex9---1011## Platform Notes1213- Optional helper plugins may help in some environments, but they must not be treated as required for this skill.1415# AI-Assisted Development Orchestration16Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.1718<!-- dual-compat-start -->19## Use When2021- 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.2223## Evidence Produced2425| Category | Artifact | Format | Example |26|----------|----------|--------|---------|27| 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` |2829## References3031- Use the `references/` directory for deep detail after reading the core workflow below.32<!-- dual-compat-end -->33## Overview3435Learn to **orchestrate multiple AI agents** (like Codex, custom sub-agents, or specialized AI tools) to work together effectively in software development.3637This skill bridges **prompting patterns** + **orchestration** + **sub-agent coordination** for real-world AI-assisted development.3839## Operating Doctrine4041- Treat AI as a force multiplier inside a disciplined engineering system, not as a replacement for requirements, design, review, tests, security, or ownership.42- 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.43- Keep humans accountable for architecture, irreversible data changes, production release, security exceptions, licensing/IP decisions, and client commitments.44- Prefer small, reviewable AI work packets: one responsibility, one bounded write scope, one expected evidence artifact.45- Require codebase grounding before edits. The agent must inspect current patterns, interfaces, tests, and failure modes before proposing or changing implementation.4647## AI Development Workflow48491. **Frame**: State user value, business value, technical objective, constraints, and acceptance tests.502. **Ground**: Read the smallest set of files/docs needed to understand existing behavior.513. **Plan**: Split work by ownership boundaries. Identify what can be delegated and what must stay on the critical path.524. **Implement**: Make narrow changes that preserve local conventions. Avoid broad rewrites unless requested.535. **Verify**: Run focused tests, linters, type checks, migrations, or manual checks that match the blast radius.546. **Review**: Inspect diff for hallucinated APIs, over-broad abstractions, hidden state changes, secrets, data leaks, and licensing risks.557. **Record**: Capture changed files, commands run, residual risks, and follow-up work.5657## Agent Assignment Rules5859- Use explorers for bounded codebase questions with clear expected outputs.60- Use workers for bounded implementation with disjoint file ownership. Tell workers they are not alone in the codebase and must not revert others' edits.61- Do not delegate the immediate blocking task if the main workflow cannot proceed until it returns.62- Never let two agents write the same files unless one is explicitly reviewing the other's patch.63- For generated code, require the same quality bar as human code: tests, readable names, explicit error handling, and no invented dependencies.6465## AI Coding Risk Controls6667| Risk | Control |68|---|---|69| Hallucinated APIs | Compile/typecheck and inspect imports, method names, schemas, and SDK versions |70| Plausible but wrong logic | Add examples, regression tests, and domain-specific fixtures |71| Security regression | Run threat review for auth, tenancy, file IO, network calls, secrets, and prompt injection |72| IP/license exposure | Avoid copying unknown code; check dependency licenses before adding packages |73| Context leakage | Keep secrets, credentials, client PII, and proprietary data out of prompts unless explicitly approved |74| Over-automation | Require human approval for production deploys, destructive changes, payments, emails, and client-facing commitments |7576## Evidence Required7778- For code changes: diff summary, tests/checks run, and known gaps.79- For architecture or plans: decision record, alternatives considered, evaluation criteria, and economic rationale.80- For modernization: before/after behavior, migration steps, rollback plan, and compatibility notes.8182**What you'll learn:**83- The 5 orchestration strategies for AI development84- AI-specific coordination patterns (Agent Handoff, Fan-Out/Fan-In, Human-in-the-Loop)85- Real-world examples (MADUUKA, BRIGHTSOMA apps)8687**Documentation Structure (Tier 2 Deep Dives):**88- 📖 **[orchestration-strategies.md](references/orchestration-strategies.md)** - The 5 core strategies with detailed examples89- 📖 **[ai-patterns.md](references/ai-patterns.md)** - AI-specific orchestration patterns90- 📖 **[practical-examples.md](references/practical-examples.md)** - Real MADUUKA and BRIGHTSOMA projects9192---9394## Additional Guidance9596Extended 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.9798Use that deep dive for:99- `When to Use This Skill`100- `Core Concepts (Quick Reference)`101- `The 5 Orchestration Strategies (Summary)`102- `The 3 AI Orchestration Patterns (Summary)`103- `Quick Reference: When to Use Which`104- `Real-World Examples (Summary)`105- `Practical Workflow: How to Apply This Skill`106- `Best Practices`107- `Integration with Other Skills`108- `Summary`109110## Decision Rules111112| Condition | Action |113|---|---|114| Workstreams are independent and bounded | Delegate with explicit outputs |115| Change is sensitive or destructive | Require approval and independent review |116| Agent evidence conflicts | Reproduce before reconciliation |117118## Capability Contract119120Read and search are required. Editing, execution, delegation, and external access require parent-task authorisation.121122## Degraded Mode123124Fallback: without delegation, execute sequentially. Without execution, return patches and commands, never fabricated CI evidence.125126## Domain Anti-Patterns127128- Delegating overlapping edits without ownership.129- Accepting success claims without evidence.130- Giving mutation permissions to review tasks.131- Parallelising a dependency chain.132- Hiding unresolved disagreement during synthesis.133## Inputs134| Artefact | Required? | Purpose |135|---|---|---|136| Development task, repository context, AI-use policy, and review boundary | yes | Bound AI-assisted work |137## Outputs138- Produce reviewed code or guidance with provenance, tests, human decisions, and unresolved risks.139140## Book-derived additions141142For interaction modes, execution records, prompt/context discipline, and trust143ramps, load [book-informed AI collaboration and execution](references/book-informed-ai-collaboration-and-execution.md).