evaluation
by muratcankoylanThe evaluation skill helps you design and run agent evaluations for non-deterministic systems. Use it for evaluation install planning, rubrics, regression checks, quality gates, and evaluation for Skill Testing. It fits LLM-as-judge workflows, multi-dimensional scoring, and practical evaluation usage when you need repeatable results.
This skill scores 78/100, which means it is a solid directory listing candidate with real workflow value for users building or measuring agent evaluations. The repository gives enough operational detail to help an agent trigger and use it with less guesswork than a generic prompt, though install decisions should account for some experimental signaling and a missing install command.
- Clear activation intent for evaluation, test frameworks, quality gates, and agent benchmarking, making triggerability straightforward.
- Substantial workflow content: the SKILL.md is long, structured, and supported by a references doc plus a Python evaluator script, which improves operational clarity and agent leverage.
- Multi-dimensional evaluation guidance and concrete metric definitions help agents execute a real evaluation workflow instead of improvising a rubric from scratch.
- The repository is marked with experimental/test signals, so users should treat it as a practical prototype rather than a fully polished production package.
- No install command is provided in SKILL.md, which makes adoption slightly less frictionless for directory users who want immediate setup guidance.
Overview of evaluation skill
What evaluation skill does
The evaluation skill helps you design and run evaluation for agent systems, especially when outputs are non-deterministic and a single “correct” answer does not exist. It is best for people who need to measure agent performance, compare configurations, or create quality gates for a pipeline rather than just write a one-off prompt.
Who should use it
Use this evaluation skill if you are testing context engineering changes, scoring agent behavior over time, or deciding whether an agent is ready for production. It is a strong fit for LLM-as-judge workflows, rubric-based scoring, regression checks, and agent testing where outcome quality matters more than exact step-by-step execution.
What makes it different
The repo emphasizes multi-dimensional evaluation instead of one overall score, which is the right shape for agents that can succeed in different ways. It also focuses on practical implementation support through references and a runnable evaluator script, so the evaluation install is useful for both planning and execution.
How to Use evaluation skill
Install and activate
Install with:
npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill evaluation
Then use it when your task involves evaluation install planning, scoring rubrics, or building an evaluation guide for agent systems. The skill works best when you explicitly describe the system being tested, the success criteria, and the failure modes you care about.
Give the skill the right inputs
A weak request like “evaluate this agent” leaves too much open. A stronger prompt gives the agent system, target outcome, constraints, and scoring needs: “Design an evaluation for a support agent that must answer from product docs only, avoid hallucinations, and be scored on factual accuracy, completeness, citation accuracy, and tool efficiency.” That level of detail lets the evaluation skill produce usable rubrics instead of generic advice.
Read these repo files first
Start with SKILL.md for the workflow and activation rules, then read references/metrics.md for score definitions and scripts/evaluator.py for implementation patterns. If you are adapting the skill for your own stack, inspect those three first before looking for anything else, because they show how the evaluation logic is meant to be applied.
Apply it in a real workflow
A practical evaluation usage flow is: define the task, choose dimensions, assign weights, build test cases, run the scorer, then review failures for pattern-level issues. Use the skill to create or refine your rubric, not just to score outputs after the fact. That makes it more useful for regression testing, model comparison, and evaluation for Skill Testing.
evaluation skill FAQ
Is evaluation skill only for benchmarks?
No. It is also useful for day-to-day quality gates, regression testing, and improving prompts or agent policies after a bad run. If you need repeatable judgment criteria for agent outputs, the evaluation skill is relevant even without a formal benchmark suite.
When should I not use it?
Skip it if you only need a simple subjective review or a quick prompt tweak. The evaluation skill is most valuable when output quality matters enough to justify rubrics, test sets, and repeatable scoring.
Is it beginner friendly?
Yes, if you already know what the agent is supposed to do. The main learning curve is not syntax; it is defining good evaluation dimensions and avoiding overreliance on a single score.
How is this different from a normal prompt?
A normal prompt asks for an opinion. The evaluation skill is a workflow for turning that opinion into a structured, repeatable assessment with dimensions, weights, and test cases. That difference matters when you need consistency across runs or reviewers.
How to Improve evaluation skill
Start with sharper success criteria
The best results come from explicit target behavior, not broad goals. Instead of “measure quality,” specify what quality means: correct facts, complete coverage, source fidelity, latency, refusal behavior, or tool use. The more concrete your criteria, the better the evaluation skill can separate real wins from accidental success.
Use dimensions that match your risk
The repo’s default emphasis on factual accuracy, completeness, citation accuracy, and source quality is a good starting point, but your evaluation should reflect the actual failure cost. For a customer-facing agent, hallucinations may matter more than style; for a research agent, source quality may outrank brevity. Adjust the rubric instead of accepting a generic score.
Iterate on failures, not just averages
After the first pass, review the low-scoring cases and look for repeated causes: missing context, weak retrieval, bad tool selection, or overconfident answers. Use those patterns to revise your test set and prompt inputs. That is the fastest way to improve evaluation usage and make the skill pay off over time.