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inference-improve

Analyze historical logs and reports to refine routing inference patterns.

Updated Sep 9, 2025
Search Tags:#routing#agent workflow#log analysis#inference#keyword mapping#confidence calibration#autonomous improvement
One-click install
npx skills add https://github.com/htafolla/chrono-warp-drive --skill inference-improve-htafolla
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: inference-improve
Source: https://github.com/htafolla/chrono-warp-drive/tree/main/.opencode/skills/inference-improve
Command: npx skills add https://github.com/htafolla/chrono-warp-drive --skill inference-improve-htafolla

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill reduces routing misfires by systematically analyzing prior inference outcomes and using evidence from logs and reports to refine routing decisions.

Core Features & Use Cases

  • Collaborative Inference Review: Coordinates a multi-role workflow to gather, analyze, and refine routing-related signals (logs, reflections, and reports).
  • Pattern-Driven Routing Updates: Identifies failure patterns such as weak keyword matches and confidence distribution issues, then designs concrete mapping and confidence adjustments.
  • Validated, Safe Application: Applies proposed changes only after review and safety checks to avoid regressions.

Quick Start

Ask an AI agent to run the inference-improve workflow to update routing mappings based on your most recent logs and reports.

Frequently Asked Questions about inference-improve

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

FAQPage Schema
How do I fix routing misfires using historical inference logs?▼

Autonomous routing improvement works by executing a five-phase workflow that gathers inference data, analyzes failure patterns like weak keyword matches, designs mapping changes, reviews them, and applies validated updates to routing-mappings.json.

When should I calibrate confidence thresholds in an agent workflow?▼

Calibrate confidence thresholds when log analysis reveals systematic routing failures or uneven confidence distributions across your inference pipeline, indicating that current keyword mappings are causing misdirected agent workflows.

What is the step-by-step process to update keyword mappings from log analysis?▼

Updating keyword mappings requires a five-phase process: gather historical logs and reports, analyze routing patterns, architect mapping and confidence adjustments, review proposed changes for safety, and apply validated updates to routing-mappings.json.

Does inference-improve require any specific dependencies to run?▼

No dependencies are required to run the inference-improve workflow, but you must supply historical inference logs, reflections, and reports as input data for the multi-role analysis and routing-mapping maintenance process.

How does validated application prevent regressions during routing updates?▼

Validated application prevents regressions by applying proposed routing changes only after a dedicated review phase and safety checks, ensuring that new keyword mappings and confidence threshold adjustments perform as expected before deployment.

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