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 EntityProcess/agentv --skill agentv-benchgit clone --depth 1 https://github.com/EntityProcess/agentvWrote 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/entityprocess/agentv/agentv-bench)<a href="https://agentmods.dev/skills/entityprocess/agentv/agentv-bench"><img src="https://agentmods.dev/badge/skills/entityprocess/agentv/agentv-bench/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/entityprocess/agentv/agentv-bench"><img src="https://agentmods.dev/badge/skills/entityprocess/agentv/agentv-bench.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 | $0.00101 | $0.05806 |
| Opus 5.5 | $0.00040 | $0.02322 |
| Sonnet 5.5 | $0.00020 | $0.01161 |
| Haiku 4.5 | $0.00010 | $0.00581 |
Grade A, and why
agentv-bench 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 1mo 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 — 465 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentV Bench
A skill for evaluating agents and iteratively improving them through data-driven optimization.
At a high level, the process goes like this:
- Understand what the agent does and what "good" looks like
- Write evaluation test cases (EVAL.yaml or evals.json)
- Run the agent on those test cases, grade the outputs
- Analyze the results — what's working, what's failing, and why
- Improve the agent's prompts/skills/config based on the analysis
- Repeat until you're satisfied
Your job when using this skill is to figure out where the user is in this process and then jump in and help them progress. Maybe they want to start from scratch — help them write evals, run them, and iterate. Maybe they already have results — jump straight to analysis and improvement.
Be flexible. If the user says "I don't need a full benchmark, just help me debug this failure", do that instead.
After the agent is working well, you can also run description optimization to improve skill triggering accuracy (see references/description-optimization.md).
Communicating with the user
This skill is used by people across a wide range of familiarity with evaluation tooling. Pay attention to context cues:
- "evaluation" and "benchmark" are borderline but OK in most cases
- For "YAML", "grader", "assertion", "deterministic judge" — see serious cues from the user that they know what those mean before using them without explanation
- Briefly explain terms if in doubt
When presenting results, default to summary tables. Offer detail on request. In CI/headless mode, skip interactive prompts and exit with status codes.
Step 1: Understand the Agent
Before running or optimizing, understand what you're working with.
-
Read the agent's artifacts — prompts, skills, configs, recent changes. Understand the full picture: what tools are available, what the expected input/output looks like, what constraints exist.
-
Identify success criteria — what does "good" look like for this agent? What are the edge cases? What would a failure look like? Talk to the user if this isn't clear from the artifacts alone.
What ships with it
14 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.
- agents/analyzer.md 10.0 KB
- agents/comparator.md 10 KB
- agents/executor.md 1.5 KB
- agents/grader.md 12 KB
- agents/mutator.md 10 KB
- LICENSE.txt 11 KB
- references/autoresearch.md 15 KB
- references/description-optimization.md 2.8 KB
- references/environment-adaptation.md 3.4 KB
- references/eval-yaml-spec.md 13 KB
- references/migrating-from-skill-creator.md 4.9 KB
- references/schemas.md 11 KB
- references/subagent-pipeline.md 7.0 KB
- scripts/trajectory.html 16 KB
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.
- 1mo ago First seen · 465 lines · 101 tokens per session scan A 7527ae09a209
agentv-bench is a skill published in the GitHub repository EntityProcess/agentv (15 stars, last pushed 6d ago), licensed MIT. It adds 101 tokens to every session and 5,806 once invoked, about $0.0004 per session on Opus 5.5. 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.
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