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 navendubrajesh/context-management-for-agents --skill advanced-evaluationgit clone --depth 1 https://github.com/navendubrajesh/context-management-for-agentsWrote 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/navendubrajesh/context-management-for-agents/advanced-evaluation)<a href="https://agentmods.dev/skills/navendubrajesh/context-management-for-agents/advanced-evaluation"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/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/navendubrajesh/context-management-for-agents/advanced-evaluation"><img src="https://agentmods.dev/badge/skills/navendubrajesh/context-management-for-agents/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 | $0.00062 | $0.01342 |
| Opus 5 | $0.00031 | $0.00671 |
| Sonnet 5 | $0.00012 | $0.00268 |
| Haiku 4.5 | $0.00006 | $0.00134 |
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.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Advanced Evaluation: LLM-as-a-Judge Techniques
Master the use of language models as evaluators for subjective quality dimensions that deterministic checks cannot capture. LLM-as-judge evaluation complements assertion-based testing by assessing coherence, helpfulness, safety, instruction-following, and other qualities that require semantic understanding. The core challenge is that evaluators are themselves non-deterministic — they exhibit systematic biases that must be detected and mitigated.
When to Activate
Activate this skill when:
- Implementing LLM-based scoring for agent outputs
- Designing pairwise comparison evaluations
- Building rubrics for subjective quality assessment
- Mitigating evaluator biases (position, verbosity, self-preference)
- Calibrating evaluator models against human judgments
- Measuring inter-evaluator reliability
Do not activate this skill for adjacent work owned by other skills:
- Deterministic checks, regression testing, or metric design:
evaluation. - Designing autonomous agent loops with evaluation gates:
harness-engineering. - Choosing evaluation strategy at the project level:
project-development.
Core Concepts
LLM-as-judge evaluation uses a language model to score or compare outputs against quality criteria. Three evaluation modes serve different purposes:
- Direct scoring — A single evaluator rates an output on a scale (1-5, 1-10). Simple but susceptible to calibration drift.
- Pairwise comparison — An evaluator chooses between two outputs. More reliable than direct scoring because relative judgments are easier than absolute ones.
- Reference-based grading — An evaluator compares output against a gold-standard reference. Most reliable but requires reference answers.
All three modes are subject to systematic biases: position bias (preferring the first or last option), verbosity bias (preferring longer responses), self-preference bias (preferring outputs from the same model family), and anchoring bias (being influenced by the scoring scale presentation).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 137 lines · 62 tokens per session scan A d39859fee125
advanced-evaluation is a skill published in the GitHub repository navendubrajesh/context-management-for-agents (2 stars, last pushed 2mo ago), licensed MIT. It adds 62 tokens to every session and 1,342 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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