skill-optimizer

Measure skill activation with and without skills across models and tasks.

7|1|Updated Mar 15, 2026

Search Tags:#evaluation #regression #activation #benchmark #guardrails #prompt-design #skill-optimizer

Ready to run

npx skills add https://github.com/Harmeet10000/skills --skill skill-optimizer-harmeet10000

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: skill-optimizer
Source: https://github.com/Harmeet10000/skills/tree/main/skills/productivity-tools/skill-optimizer
Command: npx skills add https://github.com/Harmeet10000/skills --skill skill-optimizer-harmeet10000

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Helps teams optimize AI skills to improve activation, reduce coverage gaps, and prevent regressions across model upgrades.

Core Features & Use Cases

Establish a repeatable optimization loop that measures baseline vs skill-on behavior across models and scenarios.
Add explicit triggers, integrated examples, and clear guardrails to boost instruction salience and retrieval.
Run benchmark and release-gate workflows to quantify deltas and guide safe deployments.

Quick Start

Run a baseline assessment of your current skill packs and apply the optimization loop to improve activation and reduce regressions.

Frequently Asked Questions about skill-optimizer

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

FAQPage Schema

How do I optimize AI skill activation and prevent regressions across model upgrades?▼

To optimize AI skill activation, measure baseline vs skill-on behavior across multiple models, add explicit triggers, and run benchmark workflows to quantify deltas and prevent regressions. This structured loop ensures reliable deployment and improved coverage.

What is the best way to measure performance deltas for prompt design and instruction salience?▼

The best way to measure performance deltas is by running a benchmark and release-gate workflow that compares behavior with and without the skill. This quantifies instruction salience improvements and ensures safe deployments across tasks.

How do I establish a repeatable optimization loop for AI skill packs in product management workflows?▼

You establish a repeatable optimization loop by assessing baseline skill packs, applying explicit triggers and integrated examples, and measuring outcomes across product management and engineering scenarios to boost context budget and retrieval.

When do I need to add guardrails to improve AI skill retrieval and context budget?▼

You need to add guardrails when optimizing skill packs to boost instruction salience, ensure safe activations, and manage context budget. Guardrails provide measurable boundaries that prevent unintended behavior during model upgrades.

Does this approach work for evaluating skill activation gaps across multiple models and tasks?▼

Yes, this approach works by identifying and optimizing skill activation gaps through measuring performance with and without the skill across multiple models and tasks, providing explicit triggers and measurable deltas for reliable activation.

Why does my AI skill activation fail during model upgrades and how can I fix it?▼

AI skill activation fails during model upgrades due to shifting context budgets and salience loss. Fix it by applying an optimization loop with integrated examples, explicit triggers, and guardrails to measure deltas and handle regressions.