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-eval-reviewgit 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-eval-review)<a href="https://agentmods.dev/skills/entityprocess/agentv/agentv-eval-review"><img src="https://agentmods.dev/badge/skills/entityprocess/agentv/agentv-eval-review/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-eval-review"><img src="https://agentmods.dev/badge/skills/entityprocess/agentv/agentv-eval-review.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 | $0.00081 | $0.00683 |
| Opus 5.5 | $0.00032 | $0.00273 |
| Sonnet 5.5 | $0.00016 | $0.00137 |
| Haiku 4.5 | $0.00008 | $0.00068 |
Grade A, and why
agentv-eval-review 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval Review
Overview
Lint and review AgentV eval YAML files for structural issues, schema compliance, and quality problems. Apply this checklist deterministically first, then layer LLM judgment for semantic issues a checklist cannot catch.
Process
Step 1: Structural checklist
Walk every target eval file and report violations grouped by severity (error > warning > info). For each finding, include the file path and a concrete fix.
- File extension is
.eval.yaml(error if not). descriptionfield is present at the top level (error if missing).- Each entry under
testshasid,input, and at least one ofcriteria/expected_output/assert(error if missing). - File-typed inputs (
type: file) use a leading/in theirpath(error if relative). - Tests have an
assertblock — flag tests that rely solely onexpected_output(warning). - Flag
criteriathat duplicates assertion strings whenassertionsalready express the grading contract (warning — remove the duplicatecriteria). - Prefer plain assertion strings over multiple named
type: llm-rubricblocks when the default LLM rubric grader can evaluate the checks (info unless custom prompts or grader targets are present). - Detect
expected_outputprose patterns like "The agent should..." or "The output is..." (warning —expected_outputshould be a golden/reference answer; scoring rules belong inassertionsor, for implicit-grader cases,criteria). - For historical or repo-state evals, verify the relevant repo is pinned under
workspace.repos[].commit; a SHA mentioned only in prompt prose or metadata is not an operational checkout (warning). - Identical file inputs repeated across multiple tests in the same eval should be hoisted to a top-level
input(info). - Eval files in the same directory should share a common
idprefix (info — flag drift).
Step 2: Semantic review (LLM judgment)
The structural checklist catches mechanical issues but cannot assess:
- Factual accuracy — Do tool/command names in expected_output match what the skill documents?
- Coverage gaps — Are important edge cases missing?
- Assertion discriminability — Would assertions pass for both good and bad output?
- Cross-file consistency — Do output filenames match across evals and skills?
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.
- 1mo ago First seen · 57 lines · 81 tokens per session scan A 9175ed132b0e
agentv-eval-review is a skill published in the GitHub repository EntityProcess/agentv (15 stars, last pushed 6d ago), licensed MIT. It adds 81 tokens to every session and 683 once invoked, about $0.0003 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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