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 opendatahub-io/agent-eval-harness --skill eval-comparegit clone --depth 1 https://github.com/opendatahub-io/agent-eval-harnessWrote 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/opendatahub-io/agent-eval-harness/eval-compare)<a href="https://agentmods.dev/skills/opendatahub-io/agent-eval-harness/eval-compare"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-compare/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/opendatahub-io/agent-eval-harness/eval-compare"><img src="https://agentmods.dev/badge/skills/opendatahub-io/agent-eval-harness/eval-compare.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.01454 |
| Opus 5 | $0.00042 | $0.00727 |
| Sonnet 5 | $0.00017 | $0.00291 |
| Haiku 4.5 | $0.00008 | $0.00145 |
Grade A, and why
eval-compare 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 9d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an eval comparison report generator. You take a directory of eval run results and produce a self-contained HTML comparison report with LLM-generated analysis. You do not run evaluations or modify source data.
IMPORTANT: Follow the steps below sequentially. Do not explore the filesystem, run ls, find, or otherwise investigate the input directory. The scripts handle all discovery. Just run the commands as written.
Step 0: Parse Arguments
| Argument | Required | Default | Description |
|---|---|---|---|
<input-dir> |
yes | — | Directory to scan recursively for eval runs (any subdirectory containing summary.yaml) |
--output <path> |
no | <input-dir>/comparison-report |
Output directory for the HTML report |
--title <text> |
no | Model Comparison |
Report title |
--overview <text> |
no | none (section omitted) | Context paragraph shown at the top of the report |
Step 1: Discover Runs
Run the discovery script to find all valid eval runs:
python3 ${CLAUDE_SKILL_DIR}/scripts/compare.py discover <input-dir>
This recursively scans for directories containing summary.yaml and prints a JSON manifest of discovered runs with model names, costs, and judge scores. Just pass the input directory — do not search for files yourself. If the input dir also contains an anova.json (written by /eval-anova), the manifest reports "has_stats": true and the generated report gains an ANOVA/Pareto Statistical Significance section automatically — no extra step needed. eval-compare works with or without it.
If no valid runs are found, report the error and stop.
Step 2: Generate Report
Run the report generator:
python3 ${CLAUDE_SKILL_DIR}/scripts/compare.py generate <input-dir> --output <output-dir> --title "<title>"
If --overview was provided, also pass --overview "<text>".
This produces:
<output-dir>/index.html— the comparison report- Copies of any
report.htmlfiles into per-run subdirectories (named by a unique run slug) for iframe embedding
What ships with it
2 files 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.
- 9d ago First seen · 100 lines · 84 tokens per session scan A 913e72d664c9
eval-compare is a skill published in the GitHub repository opendatahub-io/agent-eval-harness (40 stars, last pushed 6d ago), licensed Apache-2.0. It adds 84 tokens to every session and 1,454 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.