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 agentmods add skills/bugrabilge/bilge-development-kit/llm-evaluationnpx skills add bugrabilge/bilge-development-kit --skill llm-evaluationgit clone --depth 1 https://github.com/bugrabilge/bilge-development-kitWrote 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/bugrabilge/bilge-development-kit/llm-evaluation)<a href="https://agentmods.dev/skills/bugrabilge/bilge-development-kit/llm-evaluation"><img src="https://agentmods.dev/badge/skills/bugrabilge/bilge-development-kit/llm-evaluation.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.03221 |
| Opus 5 | $0.00018 | $0.01611 |
| Sonnet 5 | $0.00007 | $0.00644 |
| Haiku 4.5 | $0.00004 | $0.00322 |
Grade A, and why
llm-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 2d 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
92% identical to llm-evaluation — 3 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 — 486 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Evaluation
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
Do not use this skill when
- The task is unrelated to llm evaluation
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Use this skill when
- Measuring LLM application performance systematically
- Comparing different models or prompts
- Detecting performance regressions before deployment
- Validating improvements from prompt changes
- Building confidence in production systems
- Establishing baselines and tracking progress over time
- Debugging unexpected model behavior
Core Evaluation Types
1. Automated Metrics
Fast, repeatable, scalable evaluation using computed scores.
Text Generation:
- BLEU: N-gram overlap (translation)
- ROUGE: Recall-oriented (summarization)
- METEOR: Semantic similarity
- BERTScore: Embedding-based similarity
- Perplexity: Language model confidence
Classification:
- Accuracy: Percentage correct
- Precision/Recall/F1: Class-specific performance
- Confusion Matrix: Error patterns
- AUC-ROC: Ranking quality
Retrieval (RAG):
- MRR: Mean Reciprocal Rank
- NDCG: Normalized Discounted Cumulative Gain
- Precision@K: Relevant in top K
- Recall@K: Coverage in top K
2. Human Evaluation
Manual assessment for quality aspects difficult to automate.
Dimensions:
- Accuracy: Factual correctness
- Coherence: Logical flow
- Relevance: Answers the question
- Fluency: Natural language quality
- Safety: No harmful content
- Helpfulness: Useful to the user
3. LLM-as-Judge
Use stronger LLMs to evaluate weaker model outputs.
Approaches:
- Pointwise: Score individual responses
- Pairwise: Compare two responses
- Reference-based: Compare to gold standard
- Reference-free: Judge without ground truth
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.
- 2d ago First seen · 486 lines · 37 tokens per session scan A 7f211fed410a
llm-evaluation is a skill published in the GitHub repository bugrabilge/bilge-development-kit (10 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 3,221 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to llm-evaluation, differing in 3 lines, and is treated as a copy.
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