Author or refine agent instructions: $ARGUMENTS
Before Starting
- Search OpenAI docs for prompt engineering guidance:
- Use
mcp__openai-docs__search_openai_docswith query "system instructions best practices" for latest recommendations - Use
mcp__openai-docs__search_openai_docswith query "agent instructions" for agents-specific patterns
- Use
- Read the existing instruction architecture to understand the composable pattern:
/Users/joshuashepherd/Desktop/dev/repos/ai-lab-agent/src/agents/ai-lab/instructions/core.ts— identity + voice markers + failure modes/Users/joshuashepherd/Desktop/dev/repos/ai-lab-agent/src/agents/ai-lab/instructions/themes.ts— theme-specific layers (metanoia, movemental, mdna)/Users/joshuashepherd/Desktop/dev/repos/ai-lab-agent/src/agents/ai-lab/instructions/modes.ts— pedagogical modes (teacher, coach, reflection, mentor, companion)/Users/joshuashepherd/Desktop/dev/repos/ai-lab-agent/src/agents/ai-lab/instructions/styles.ts— interaction styles (conversation, challenge, socratic, evaluative, explainer)/Users/joshuashepherd/Desktop/dev/repos/ai-lab-agent/src/agents/ai-lab/instructions/context.ts— dynamic user context per run
- Read the writing assistant instruction pattern:
/Users/joshuashepherd/Desktop/dev/repos/movemental-dashboard/src/agents/writing-assistant/instructions.ts—buildWritingAssistantPrompt()with pluggable content sources/Users/joshuashepherd/Desktop/dev/repos/movemental-dashboard/src/agents/writing-assistant/instructions/identity.ts— voice identity template/Users/joshuashepherd/Desktop/dev/repos/movemental-dashboard/src/agents/writing-assistant/instructions/content-forms.ts— content form templates/Users/joshuashepherd/Desktop/dev/repos/movemental-dashboard/src/agents/writing-assistant/instructions/examples.ts— voice calibration examples
Instruction Architecture
Instructions are composed from layers that are concatenated at runtime:
┌─────────────────────────────────┐
│ STATIC (cached by dimension key)│
│ ┌───────────────────────────┐ │
│ │ Core Identity │ │ — who the agent IS, voice markers, failure modes
│ │ + Theme Layer │ │ — theological/framework lens
│ │ + Mode Layer │ │ — pedagogical approach
│ │ + Style Layer │ │ — interaction pattern
│ └───────────────────────────┘ │
├─────────────────────────────────┤
│ DYNAMIC (fresh per run) │
│ ┌───────────────────────────┐ │
│ │ User Context │ │ — name, role, language, kairos, engagement
│ │ + Conversation History │ │ — continuity from prior turns
│ │ + Page/Content Context │ │ — what the user is currently viewing
│ └───────────────────────────┘ │
└─────────────────────────────────┘
Static sections are cached by key {theme}-{mode}-{style} (max 100 entries) for performance.
Dynamic sections are NEVER cached — they change per run.
Writing Instructions
Core Identity Section
Define who the agent is. Include:
- Role statement: "You are [name], a [role] who [purpose]."
- Voice markers: 3-5 dimensions that define the voice (e.g., Christocentric Anchoring, Pastoral Warmth, Narrative Imagery, Theological Depth, Prophetic Intensity)
- Signature elements: Recurring phrases, metaphors, or framing devices the voice uses
- Failure modes: What the agent must NOT do (e.g., "Never simplify theological terms", "Never invent citations")
Theme/Mode/Style Layers
Each adds a focused paragraph or two. Keep them modular — they should make sense in any combination.
Dynamic Context Section
Built from RunContext at runtime. Use the DynamicInstructionsGenerator pattern:
export const myDynamicInstructions: DynamicInstructionsGenerator = async (
baseInstructions: string,
context: Record<string, unknown>
) => {
const sections: string[] = [baseInstructions];
if (context.userName) {
sections.push(`## User Context\nYou are speaking with ${context.userName}.`);
}
return sections.join('\n\n');
};
For Writing Assistant Instructions
Use the pluggable content source pattern from movemental-dashboard:
export function buildWritingAssistantPrompt(content: AgentPromptContent): string {
return [
content.headerTemplate, // from voice_identities table
content.coreIdentity, // from voice_identities table
content.platformContext, // from voice_identities table
buildContentFormSection(content.contentForms), // from content_form_templates table
buildExamplesSection(content.examples), // from writing_examples table
].filter(Boolean).join('\n\n');
}
This allows org-specific voice swapping without code changes.
Rules
- Never hardcode tenant-specific names, content, or theological positions — use
tenantConfigor DB-backed content - Keep total instruction length under 10,000 tokens (static + dynamic combined)
- Voice markers should be descriptive enough for the LLM to calibrate tone
- Failure modes are as important as positive instructions — always include them
- Test instruction changes by running the agent and checking voice fidelity
- For book-related agents, include mandatory retrieval instructions ("Always use file_search before answering questions about specific books")
- Use markdown formatting in instructions — LLMs parse it well
- Check OpenAI docs MCP for any changes to instruction handling or token limits