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Claude Code tip: keep Opus 5.5 as your main model, but stop paying Opus prices for your subagents move them to Sonnet 5.5 Opus 5.5 plans and decides Sonnet 5.5 subagents do the heavy reading, editing and testing at half the price ($2 / $10 vs $4 / $20... show more
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the core setup is good for substantial coding work. I would adjust it before making it your everyday default. • Keep the separate roles. Having agents investigate, implement, and review is sensible. Codex officially supports this arrangement. Subagent documentation • Question Sol high on every task. OpenAI recommends comparing settings on your actual work and keeping the lightest setting that meets your quality standard. Extra agents also consume additional tokens. Your Terra medium default remains consistent with your cost rules. Model guidance • Keep Astra available for difficult reviews. Give it a specific question and the actual changes, errors, and test results. Calling it automatically because a task was long can produce unnecessary review. Its agreement still does not prove the work functions. • Understand auto_review correctly. It reviews eligible permission requests. It does not check every action or certify finished work. Configuration documentation My recommendation: adopt the role separation and occasional independent review; retain your routine default and approval rules for stronger models.
gpt-6-luna is the right one as a subagent
opus for the plan sonnet for the loop is how you keep the bill from eating the win
Think of it as a team: Opus is the lead, Sonnet handles the work, and the advisor is the reviewer at key checkpoints.
Opus leads the strategy and Sonnet does the work it knows best which feels very efficient to watch in action
the real optimization is routing each task to the model that actually needs to handle it
Gemini 4 Argon (announced Sept 30): → 1M-token output (was 64K) → Leads on 12 of the 18 benchmarks Google shared (trails on 2 coding ones) → $2 in / $10 out per 1M tokens (intro price, $4/$20 later) → Built for coding, long multi-step work, cyber defense
别的不说，这个动效做的是相当棒！
![Anthropic just gave OpenClaw the green light Anthropic staff confirmed that CLI-based usage is sanctioned. We’ve successfully bridged the Claude Code CLI directly into the OpenClaw gateway, letting you run your entire agent fleet on your Pro/Max plan and no additional API tokens required. The Implementation: - Auth: Log in to Claude Code locally: claude auth login - Path: Grab your binary location: which claude - Bridge: Drop the path into your openclaw.json under agents.defaults: "cliBackends": { "claude-cli": { "args": [ "-p", "--output-format", "json" ], "command": " ", "input": "arg", "output": "json", "serialize": true, "sessionArg": "--session-id", "sessionMode": "existing" } }, "model": { "primary": "claude-cli/claude-opus-4-6" }, "models": { "claude-cli/claude-opus-4-6": {}, "claude-cli/claude-opus-4-7": {}, "claude-cli/claude-sonnet-4-6": {} }, The Result: OpenClaw reuses your local session IDs and subscription limits. You get the full power of Opus and Sonnet without the restricted OAuth endpoints or the API tax. They sanctioned the path. We built the bridge. Here is a video how I done it step by step:](https://image.24vids.com/tw-2046679352977580437/amplify_video_thumb/2046678940803117057/img/whpOwezqwjM1NOY5.jpg)
![i finally mastered how to maximise my opus 5.5 usage limits... the trick: let jev choose which subagent gets each task and how much effort it should use. [here’s how i’d wire it:] claude breaks the project into tasks. jev selects from predefined worker profiles. claude applies the selected settings and dispatches the work. → main session, medium: clarify the requirements, define what “done” looks like, and prepare the tasks → builder, low: small, clearly defined tasks with existing examples or patterns → builder, medium: tasks that connect multiple parts or need decisions within the approved plan → verifier, high: check requirements, probe edge cases, and report problems for the builder to fix jev gets the task’s scope, what’s uncertain, and the consequences of failure. it chooses from the profiles allowed for that task. your approval checkpoints stay in place. paste this into your next planning session: “use opus 5.5 with jev selecting the worker and effort profile for each task. first, check that a working jev integration is available and that this environment supports separate effort settings for subagents. check for configuration or environment overrides that could prevent those settings from taking effect. if anything is missing, explain what needs wiring before proceeding. break my request into tasks with clear ownership, dependencies, relevant context, and acceptance checks. keep small related tasks together when a separate subagent would add unnecessary overhead. keep the main session at medium effort. offer jev these worker profiles: builder at low effort for small, clearly defined tasks using existing patterns; builder at medium effort for tasks that connect multiple parts or require decisions within the approved plan; verifier at high effort for checking requirements and edge cases. give jev each task’s scope, uncertainties, dependencies, and consequences of failure. only offer profiles appropriate to the current stage. validate its selection before dispatching. use the actual jev integration; don’t simulate its decisions. if it abstains or returns an invalid choice, stop that handoff and ask me. show me the task plan and proposed assignments before starting. after approval, launch the selected workers with their assigned effort settings, relevant context, file ownership, and completion checks. let me review the result before verification. the verifier may add tests but must leave implementation code unchanged. have it report what passed, what failed, and what remains uncertain. send implementation fixes back to the builder, then recheck the affected parts. if a task repeatedly fails, examine the requirements and approach before increasing effort. report available total usage, including jev calls, worker calls, retries, and verification. don’t invent missing data. compare similar completed tasks before claiming savings.” steal this 👇](https://image.24vids.com/tw-2103852115341049897/amplify_video_thumb/2103851843214614528/img/QwYdKECdjfJZgMMK.jpg)
