evidence-depth

Scale evidence depth to risk tier in AI output generation.

Updated May 11, 2026

Search Tags:#optimization #evidence #risk-management #harness #token-usage #cost-efficiency

Ready to run

npx skills add https://github.com/AesopScott/mojo --skill evidence-depth

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the issue of over-evidencing low-risk AI outputs, which leads to unnecessary token consumption, increased latency, and higher operational costs.

Core Features & Use Cases

Risk-Based Scaling: Dynamically adjusts the depth of citations, tests, and proof records based on the risk tier of the output.
Cost Efficiency: Reduces compute waste and token usage by applying rigorous evidence requirements only where they are strictly necessary.
Use Case: When generating routine internal summaries, use this skill to lower the evidence threshold, reserving deep validation for high-stakes customer-facing documentation.

Quick Start

Use the evidence-depth skill to analyze the current Evidence harness configuration and propose a cost-saving adjustment for low-risk output tiers.

Frequently Asked Questions about evidence-depth

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I reduce token usage and costs when generating routine AI outputs?▼

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.

What is risk-based evidence scaling in AI output generation?▼

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.

How do I configure the Evidence harness to stop over-evidencing low-risk outputs?▼

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.

When should I apply deep validation versus lower evidence thresholds in software workflows?▼

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.

Can I optimize AI output cost by adjusting evidence depth without removing required citations?▼

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.