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devtu-optimize-skills

Optimize ToolUniverse research skills to produce evidence-graded narrative reports with source attribution.

Updated Apr 18, 2026
Search Tags:#testing#disambiguation#fallbacks#tooluniverse#skill-optimization#evidence-grading#report-synthesis
One-click install
npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill devtu-optimize-skills-centaurioun
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: devtu-optimize-skills
Source: https://github.com/Centaurioun/osteogenesis_imperfecta/tree/main/.agents/skills/devtu-optimize-skills
Command: npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill devtu-optimize-skills-centaurioun

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured, test-driven approach to fixing and improving ToolUniverse research skills so they produce reliable, evidence-graded, and reproducible reports instead of noisy or non-functional outputs.

Core Features & Use Cases

  • Tool Verification: Check and correct tool parameter contracts before any calls to avoid runtime failures.
  • Disambiguation-First Workflow: Resolve identifiers and detect naming collisions before broad searches to reduce noise.
  • Evidence Grading & Completeness: Apply T1-T4 evidence tiers, quantified minimums, aggregated data gaps, and a mandatory completeness checklist.
  • Testing & Fallbacks: Require comprehensive real-data tests, fallback chains, and documented failure handling to ensure production readiness.
  • Use Case: Review an existing ToolUniverse research skill that returns inconsistent literature results and transform it into a validated, evidence-layered report with a JSON bibliography and a methods appendix.

Quick Start

Run a tool verification pass, disambiguate identifiers, and synthesize an evidence-graded narrative report with sources and a completeness checklist.

Frequently Asked Questions about devtu-optimize-skills

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

FAQPage Schema
How do I optimize ToolUniverse research skills to produce evidence-graded reports?▼

To optimize ToolUniverse research skills for evidence-graded reports, apply a test-driven approach that verifies tool parameter contracts, executes disambiguation-before-search workflows, and applies T1-T4 evidence grading with quantified completeness checks.

What is the disambiguation-first workflow for research skill identifier resolution?▼

The disambiguation-first workflow resolves identifiers and detects naming collisions before executing broad aggregator-first queries, reducing noisy results and ensuring accurate source attribution in narrative reports.

How do I apply T1-T4 evidence grading and completeness checks to research outputs?▼

Apply T1-T4 evidence grading by categorizing aggregated data into tiered evidence levels, calculating quantified minimums, identifying data gaps, and validating the final output against a mandatory completeness checklist.

Why do my research skills return inconsistent literature results and fail at runtime?▼

Inconsistent literature results and runtime failures occur when tool parameter contracts are unverified or fallback handling is missing, requiring comprehensive real-data testing and documented failure chains for production readiness.

What is the best way to structure fallback handling for aggregator-first queries?▼

The best way to structure fallback handling for aggregator-first queries is to establish comprehensive real-data tests, define explicit fallback chains for tool failures, and document failure handling to ensure reproducible report synthesis.

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