Scale evidence depth to risk tier in AI output generation.
npx skills add https://github.com/AesopScott/mojo --skill evidence-depth
Please help me install this Agent Skill. Skill: evidence-depth Source: https://github.com/AesopScott/mojo/tree/main/harnesses/skills/evidence-depth Command: npx skills add https://github.com/AesopScott/mojo --skill evidence-depth
This skill addresses the issue of over-evidencing low-risk AI outputs, which leads to unnecessary token consumption, increased latency, and higher operational costs.
Use the evidence-depth skill to analyze the current Evidence harness configuration and propose a cost-saving adjustment for low-risk output tiers.
High-intent search queries and answers about installing and using this skill.
To reduce token usage, you can scale evidence depth based on risk tiers, applying rigorous validation only to high-stakes outputs while lowering evidence requirements for routine internal summaries to optimize cost efficiency.
Risk-based evidence scaling dynamically adjusts the depth of citations, tests, and proof records according to the risk tier of the AI output, ensuring deep validation is reserved strictly for high-stakes tasks to prevent compute waste.
Configuring the Evidence harness involves inspecting the current configuration, identifying bottleneck failure modes, and implementing targeted control levers to lower evidence thresholds for low-risk tiers and reserve deep validation for customer-facing documentation.
You should apply deep validation to high-stakes customer-facing documentation to ensure accuracy, while lowering evidence thresholds for routine internal summaries to reduce unnecessary token consumption, latency, and operational costs.
Yes, optimizing AI output cost involves scaling evidence depth to match the specific risk tier, preserving necessary citations for high-risk outputs while reducing redundant proof records for low-risk tasks to maintain cost efficiency.
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