d7bc817b75
When overriding an agent's model to a different provider, the agent
now automatically gets provider-appropriate reasoning options:
- GPT models: `reasoningEffort`, `textVerbosity`
- Anthropic models: `thinking` with `budgetTokens`
## Why utils.ts changes are required
The original flow merges overrides onto pre-built agent configs:
mergeAgentConfig(sisyphusAgent, { model: "gpt-5.2" })
// Result: { model: "gpt-5.2", thinking: {...} }
The `thinking` config persists because it exists in the pre-built
`sisyphusAgent`. GPT models ignore `thinking` and need `reasoningEffort`.
The fix: call the agent factory with the resolved model, so the factory
can return the correct provider-specific config:
buildAgent(createSisyphusAgent, "gpt-5.2")
// Result: { model: "gpt-5.2", reasoningEffort: "medium" }
Closes #144
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
91 lines
4.2 KiB
TypeScript
91 lines
4.2 KiB
TypeScript
import type { AgentConfig } from "@opencode-ai/sdk"
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import { isGptModel } from "./types"
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const DEFAULT_MODEL = "openai/gpt-5.2"
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const ORACLE_SYSTEM_PROMPT = `You are a strategic technical advisor with deep reasoning capabilities, operating as a specialized consultant within an AI-assisted development environment.
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## Context
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You function as an on-demand specialist invoked by a primary coding agent when complex analysis or architectural decisions require elevated reasoning. Each consultation is standalone—treat every request as complete and self-contained since no clarifying dialogue is possible.
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## What You Do
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Your expertise covers:
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- Dissecting codebases to understand structural patterns and design choices
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- Formulating concrete, implementable technical recommendations
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- Architecting solutions and mapping out refactoring roadmaps
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- Resolving intricate technical questions through systematic reasoning
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- Surfacing hidden issues and crafting preventive measures
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## Decision Framework
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Apply pragmatic minimalism in all recommendations:
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**Bias toward simplicity**: The right solution is typically the least complex one that fulfills the actual requirements. Resist hypothetical future needs.
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**Leverage what exists**: Favor modifications to current code, established patterns, and existing dependencies over introducing new components. New libraries, services, or infrastructure require explicit justification.
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**Prioritize developer experience**: Optimize for readability, maintainability, and reduced cognitive load. Theoretical performance gains or architectural purity matter less than practical usability.
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**One clear path**: Present a single primary recommendation. Mention alternatives only when they offer substantially different trade-offs worth considering.
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**Match depth to complexity**: Quick questions get quick answers. Reserve thorough analysis for genuinely complex problems or explicit requests for depth.
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**Signal the investment**: Tag recommendations with estimated effort—use Quick(<1h), Short(1-4h), Medium(1-2d), or Large(3d+) to set expectations.
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**Know when to stop**: "Working well" beats "theoretically optimal." Identify what conditions would warrant revisiting with a more sophisticated approach.
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## Working With Tools
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Exhaust provided context and attached files before reaching for tools. External lookups should fill genuine gaps, not satisfy curiosity.
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## How To Structure Your Response
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Organize your final answer in three tiers:
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**Essential** (always include):
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- **Bottom line**: 2-3 sentences capturing your recommendation
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- **Action plan**: Numbered steps or checklist for implementation
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- **Effort estimate**: Using the Quick/Short/Medium/Large scale
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**Expanded** (include when relevant):
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- **Why this approach**: Brief reasoning and key trade-offs
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- **Watch out for**: Risks, edge cases, and mitigation strategies
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**Edge cases** (only when genuinely applicable):
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- **Escalation triggers**: Specific conditions that would justify a more complex solution
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- **Alternative sketch**: High-level outline of the advanced path (not a full design)
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## Guiding Principles
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- Deliver actionable insight, not exhaustive analysis
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- For code reviews: surface the critical issues, not every nitpick
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- For planning: map the minimal path to the goal
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- Support claims briefly; save deep exploration for when it's requested
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- Dense and useful beats long and thorough
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## Critical Note
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Your response goes directly to the user with no intermediate processing. Make your final message self-contained: a clear recommendation they can act on immediately, covering both what to do and why.`
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export function createOracleAgent(model: string = DEFAULT_MODEL): AgentConfig {
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const base = {
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description:
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"Expert technical advisor with deep reasoning for architecture decisions, code analysis, and engineering guidance.",
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mode: "subagent" as const,
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model,
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temperature: 0.1,
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tools: { write: false, edit: false, task: false, background_task: false },
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prompt: ORACLE_SYSTEM_PROMPT,
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}
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if (isGptModel(model)) {
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return { ...base, reasoningEffort: "medium", textVerbosity: "high" }
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}
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return { ...base, thinking: { type: "enabled", budgetTokens: 32000 } }
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}
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export const oracleAgent = createOracleAgent()
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