Agent Orchestration

Provide world-building context (WHERE, WHAT, WHY). Define success criteria. Trust agent expertise for implementation (HOW).

📚 References

Reference	Purpose
`references/principles.md`	Core scientific method & delegation principles
`references/is-it-done-checklist.md`	Completion criteria checklist
`references/validation-protocol.md`	Post-completion validation
`references/clear-framework.md`	CLEAR delegation framework
`references/hallucination-triggers.md`	Avoiding common triggers

🛠️ Delegation Template

Start every Task prompt with:

Your ROLE_TYPE is sub-agent.

Full template structure:

text

Your ROLE_TYPE is sub-agent.

[Task identification]

OBSERVATIONS:
- [Factual observations from your work or other agents]
- [Verbatim error messages if applicable]
- [Observed locations: file:line references if already known]
- [Environment or system state if relevant]

DEFINITION OF SUCCESS:
- [Specific measurable outcome]
- [Acceptance criteria]
- [Verification method]
- Solution follows existing patterns found in [reference locations]
- Solution maintains or reduces complexity

CONTEXT:
- Location: [Where to look]
- Scope: [Boundaries of the task]
- Constraints: [User requirements only]

YOUR TASK:
1. Run SlashCommand /is-it-done to understand completion criteria for this task type
2. Use the /is-it-done checklists as your working guide throughout this task
3. Perform comprehensive context gathering using:
   - Available functions and MCP tools from the <functions> list
   - Relevant skills from the <available_skills> list
   - Project file exploration and structure analysis
   - External resources (CI/CD logs, API responses, configurations)
4. Form hypothesis based on gathered evidence
5. Design and execute experiments to test hypothesis
6. Verify findings against authoritative sources
7. Implement solution following discovered best practices
8. Verify each /is-it-done checklist item as you complete it
9. Only report completion after all /is-it-done criteria satisfied with evidence

INVESTIGATION REQUIREMENTS:
- Trace the issue through the complete stack before proposing fixes
- Document discoveries at each layer (e.g., UI → Logic → System → Hardware)
- Identify both symptom AND root cause
- Explain why addressing [root] instead of patching [symptom]
- If proposing workaround, document why root cause cannot be fixed

VERIFICATION REQUIREMENTS:
- /is-it-done is step 1 of YOUR TASK - run it before starting work
- Use /is-it-done checklists as working guide, not post-mortem report
- Provide evidence for each checklist item as you complete it
- If checklist reveals missing work, complete that work before proceeding
- Your work will be reviewed by a rigorous engineer who expects verified functionality

AVAILABLE RESOURCES:
[See below for examples]

Writing Effective AVAILABLE RESOURCES

The AVAILABLE RESOURCES section provides world-building context about the environment, not a restrictive tool list.

Correct pattern (world-building, empowering):

text

AVAILABLE RESOURCES:
- The `gh` CLI is pre-authenticated for GitHub operations (issues, PRs, API queries)
- Excellent MCP servers are installed for specialized tasks - check your <functions> list and prefer MCP tools (like `Ref`, `context7`, `exa`) over built-in alternatives since they're specialists at their domain
- This Python project uses `uv` for all operations - activate the `uv` skill and use `uv run python` instead of `python3`, `uv pip` instead of `pip`
- Project uses `hatchling` as build backend - activate the `hatchling` skill for build/publish guidance
- This repository uses GitLab CI - use `gitlab-ci-local` to validate pipeline changes locally before pushing
- Recent linting fixes are documented in `.claude/reports/` showing common issues and resolutions
- Package validation scripts live in `./scripts/` - check its README.md for available validators to run after changes
- Full project context available including tests, configs, and documentation

Specialized Agent Assignments

For Context Gathering:

•Use context-gathering sub-agent to gather context without polluting orchestrator's context window

For Python Development:

•Use python-cli-architect sub-agent to write Python code
•Use python-pytest-architect sub-agent to plan and write Python tests
•Use python-code-reviewer sub-agent for post-write code review

For Bash Development:

•Use bash-script-developer sub-agent to write bash scripts
•Use bash-script-auditor sub-agent for post-write script review

For Documentation:

•Use documentation-expert sub-agent to write user-facing documentation

For Architecture:

•Use system-architect sub-agent for system architecture documentation
•For Python architecture, use python-cli-architect instead

For Linting Issues:

•Use linting-root-cause-resolver sub-agent for pre-commit, ruff, mypy, pytest issues

Critical Rule: The orchestrator must task sub-agents with ALL code changes, including the smallest edits, and any context gathering or research.