Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add lingxling/awesome-skills-cn --skill advanced-evaluationgit clone --depth 1 https://github.com/lingxling/awesome-skills-cnWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lingxling/awesome-skills-cn/advanced-evaluation)<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/advanced-evaluation"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/advanced-evaluation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/advanced-evaluation"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/advanced-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00059 | $0.03592 |
| Opus 5 | $0.00030 | $0.01796 |
| Sonnet 5 | $0.00012 | $0.00718 |
| Haiku 4.5 | $0.00006 | $0.00359 |
Grade A, and why
advanced-evaluation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to advanced-evaluation — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 461 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Advanced Evaluation
This skill covers production-grade techniques for evaluating LLM outputs using LLMs as judges. It synthesizes research from academic papers, industry practices, and practical implementation experience into actionable patterns for building reliable evaluation systems.
Key insight: LLM-as-a-Judge is not a single technique but a family of approaches, each suited to different evaluation contexts. Choosing the right approach and mitigating known biases is the core competency this skill develops.
When to Use
Activate this skill when:
- Building automated evaluation pipelines for LLM outputs
- Comparing multiple model responses to select the best one
- Establishing consistent quality standards across evaluation teams
- Debugging evaluation systems that show inconsistent results
- Designing A/B tests for prompt or model changes
- Creating rubrics for human or automated evaluation
- Analyzing correlation between automated and human judgments
Core Concepts
The Evaluation Taxonomy
Evaluation approaches fall into two primary categories with distinct reliability profiles:
Direct Scoring: A single LLM rates one response on a defined scale.
- Best for: Objective criteria (factual accuracy, instruction following, toxicity)
- Reliability: Moderate to high for well-defined criteria
- Failure mode: Score calibration drift, inconsistent scale interpretation
Pairwise Comparison: An LLM compares two responses and selects the better one.
- Best for: Subjective preferences (tone, style, persuasiveness)
- Reliability: Higher than direct scoring for preferences
- Failure mode: Position bias, length bias
Research from the MT-Bench paper (Zheng et al., 2023) establishes that pairwise comparison achieves higher agreement with human judges than direct scoring for preference-based evaluation, while direct scoring remains appropriate for objective criteria with clear ground truth.
The Bias Landscape
LLM judges exhibit systematic biases that must be actively mitigated:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 461 lines · 59 tokens per session scan A 9874b2f2eb67
advanced-evaluation is a skill published in the GitHub repository lingxling/awesome-skills-cn (281 stars, last pushed 1mo ago), licensed MIT. It adds 59 tokens to every session and 3,592 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to advanced-evaluation, differing in 0 lines, and is treated as a copy.
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