Claude Opus 4.8: Max-Effort Prompt for High-Stakes Tasks

One-sentence takeaway

This community prompt breaks high-stakes tasks into multiple approaches, risks, confidence levels, and key assumptions. It is suited to drafting decisions or important deliverables with Opus 4.8's Max effort setting.

Use cases

• Suitable tasks: Pricing or strategic decisions, important client deliverables, and evaluating proposals where uncertainty needs to be made explicit.

• Unsuitable tasks: Simple formatting, quick factual questions, and low-risk tasks that do not require analysis of multiple approaches; for these tasks, effort can be lowered or this template omitted.

• Applicable model version: The post discusses Claude Opus 4.8; other models have not been verified.

• Applicable client, agent, or API: The author describes the effort control in the Claude client/Cowork; the API parameter mapping is not publicly disclosed and should be checked against the actual client.

• Recommended reasoning level and parameters: Select Max in the interface; API parameters, temperature, and token limits are not publicly disclosed / unverifiable.

Ready-to-use content

The code block below is the original prompt publicly shared in the post; its original meaning and structure are preserved:

This is a high-stakes task and I want your
maximum effort. Do not rush it.

The task: [describe the decision or deliverable
in full - include the context, constraints,
and what's riding on it]

Before you answer:
- Reason through multiple approaches, not just
  the first one
- Consider what could go wrong with each
- Tell me where you're confident and where
  you're uncertain
- Flag any assumption you're making that, if
  wrong, would change your answer

Then give me your most considered output.

Testing/workflow steps

1. Replace [describe ...] with the full background, constraints, decision at hand, and cost of failure.

2. Set effort to Max in the Claude client; if the client does not offer this level, do not assume that the client setting maps to an API parameter.

3. Ask the model to list multiple approaches first, then list the risks, confidence level, and assumptions that would change the conclusion for each.

4. Manually verify high-stakes conclusions; treat the template as an analytical framework, not the model's “confidence” as proof of fact.

5. Compare runs on the same task at Low/High effort, and record the actual differences in output quality, elapsed time, and token consumption.

Original evidence and data

• The post body explicitly recommends using low effort for formatting, quick factual questions, and proofreading, and reserving Max for a small number of genuinely important tasks.

• The post provides a complete, copyable template rather than only a title or summary.

• The author associates “uncertainty prompts” with fewer unsupported assertions, but does not disclose an original test set, call logs, sample size, or statistical method.

Scope and limitations

• This is a personal experience and prompt template, not an official Anthropic evaluation; the specific error-rate figures mentioned in the post cannot be verified from the post alone.

• Max increases reasoning cost and latency and does not guarantee factual accuracy; important decisions still require verification by domain experts against sources and data.

• The template emphasizes multiple approaches, which may lead to over-analysis for simple tasks; choose the effort level according to task risk.

• The post does not disclose whether the API supports exactly the same client level, parameter names, and limits.

Source excerpts or observations (for compliance-short-quote purposes only)

The post summarizes its approach with “match effort to stakes” and builds the core template around four checkpoints: multiple approaches, risks, uncertainty, and key assumptions. Its community provenance and lack of original logs mean that this material is best treated as a reusable starting point, not a performance guarantee.