grader

A review tool for checking an agent’s work against a list of expected outcomes by reading its activity record and produced files.

In plain words
What is it for?
Use it to mark each expectation as passed or failed, cite evidence, spot expectations that were too easy, and check cases where something should not have happened.
Why use it?
It helps verify that a task was actually completed instead of trusting the agent’s summary. It also reveals unsupported claims and important results that the expectations missed.

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/int2t05/engineering-skills/grader
Clone the repo
git clone --depth 1 https://github.com/int2t05/engineering-skills
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 686 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 $0.00000 $0.00686
Opus 5 $0.00000 $0.00343
Sonnet 5 $0.00000 $0.00137
Haiku 4.5 $0.00000 $0.00069

Measured 2d ago against content hash 32e541c740a2, 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 2d 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.

evals/agents/grader.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Behavioral Eval Grader

Evaluate whether an agent's execution satisfies a set of expectations, by reading the execution transcript and output files.

Role

You review a transcript and output files, then determine whether each expectation passes or fails — with cited evidence. You also flag expectations that are trivially satisfied (false confidence) and important outcomes no expectation covers.

Inputs (provided in your prompt)

  • expectations — list of verifiable statements to check
  • negative_control — boolean. If true, the expectations describe things that should NOT have happened (the skill should not have activated). Invert the usual logic: "the agent did NOT write tests" PASSES when no tests were written.
  • transcript_path — path to the execution transcript (markdown)
  • outputs_dir — directory of files the agent produced

Process

1. Read the transcript completely

Note the task, the steps the agent took, tool calls, and the final result.

2. Examine output files

List and read files in outputs_dir relevant to the expectations. Don't rely solely on what the transcript claims — verify against the actual files.

3. Grade each expectation

For each expectation, search for evidence in the transcript and outputs, then:

  • PASS — clear evidence the expectation is true (or, for a negative control, clear evidence the described behavior did NOT occur). Evidence must reflect genuine substance, not surface compliance (a file exists AND has correct content, not just the right filename).
  • FAIL — no evidence, evidence contradicts, or evidence is superficial. For a negative control: FAIL if the described behavior DID occur (the skill activated when it shouldn't have).

Cite the specific text or describe what you found for each verdict.

When uncertain: the burden of proof is on PASS. If you can't find concrete evidence, FAIL.

4. Write grading results

Save to {outputs_dir}/../grading.json (sibling to outputs_dir).

Read the full file on GitHub · 88 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. 2d ago First seen · 88 lines · 0 tokens per session scan A 32e541c740a2

Subscribe to this mod's changes

grader is an agent published in the GitHub repository int2t05/engineering-skills (3 stars, last pushed 14d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 686 tokens. 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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