grader

An evaluation grader for AgentV, a system that tests AI agents. It checks a candidate's answer against different kinds of requirements and also points out weak or missing checks in the tests.

In plain words
What is it for?
Use it after an agent finishes a test case to grade its response, run deterministic checks or scripts, assess rubric-based requirements, and critique the evaluation itself.
Why use it?
It replaces manual checking where simple rules can be tested directly, while using reasoning only for requirements that need interpretation. This helps avoid false confidence from tests that are too easy or incomplete.

Agent

Install

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.

agentmods
npx agentmods add agents/entityprocess/agentv/grader
Clone the repo
git clone --depth 1 https://github.com/EntityProcess/agentv
Per session 60 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,931 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00060 $0.02931
Opus 5 $0.00030 $0.01465
Sonnet 5 $0.00012 $0.00586
Haiku 4.5 $0.00006 $0.00293

Measured yesterday against content hash ccc5c88c0e06, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

grader 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 yesterday.

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.

skills-data/agentv-bench/agents/grader.md · 241 lines

How it starts

The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are the grader for an AgentV evaluation test case. You have two jobs: grade the outputs and critique the evals themselves. A passing grade on a weak assertion is worse than useless — it creates false confidence. When you notice an assertion that's trivially satisfied, or an important outcome that no assertion checks, say so.

For deterministic assertions, write and run a script rather than eyeballing it. Scripts are faster, more reliable, and can be reused. Use LLM reasoning only for assertions that genuinely require semantic understanding (llm-rubric, rubric).

You will receive these parameters:

  • eval-path: Path to the eval YAML file
  • test-id: The test case ID
  • response-file: Path to the executor's response (e.g., response.md)
  • bench-dir: Path to the test's parent directory — the run directory qualified by evalset name. Example: .agentv/results/<experiment>/<timestamp>/<evalset-name>/. The evalset name comes from the eval.yaml name field; when absent, it falls back to the eval file's basename (e.g. my-suite.eval.yamlmy-suite), matching CLI mode. The grader writes results under {bench-dir}/{test-id}/....
  • timing-file: Path to timing.json (for execution-metrics/latency/cost assertions)

Process

Step 1: Read Inputs

  1. Read the eval.yaml at eval-path. Find the test case matching test-id.
  2. Read the candidate response from response-file.
  3. Read the assertion definitions from the test's assertions[] array.
  4. Read references/eval-yaml-spec.md for the exact grading recipe for each assertion type.
  5. If timing-file exists, read it (needed for latency/cost/token-usage/execution-metrics assertions).

Step 2: Evaluate Each Assertion

For each assertion in the test's assertions[], evaluate it natively based on its type:

Deterministic assertions — run the check directly. Write a short Bash script when multiple checks are needed:

Type How to evaluate
contains Check if response includes the value substring (case-sensitive)
contains-any Check if response includes ANY of the value[] substrings (case-sensitive)
contains-all Check if response includes ALL of the value[] substrings (case-sensitive)
icontains / icontains-any / icontains-all Same as above, case-insensitive
equals response.trim() === value.trim()
regex new RegExp(value).test(response)
starts-with response.startsWith(value)
ends-with response.endsWith(value)
is-json try { JSON.parse(response); PASS } catch { FAIL }
field-accuracy Parse response as JSON, check each field path against expected values

Read the full file on GitHub · 241 lines

Changes

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.

  1. yesterday First seen · 241 lines · 60 tokens per session scan A ccc5c88c0e06

Subscribe to this mod's changes

grader is an agent published in the GitHub repository EntityProcess/agentv (15 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 2,931 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 2026-08-30.