model-grader

model-grader is a skill for Claude Code from Abhillashjadhav/AI-PM-essential-skills. It costs 174 tokens per session (2,130 once invoked), scanned A, original, MIT.

An interview guide for defining what a correct answer from an AI feature must look like before building an automated grader. It covers the output shape, rules that prevent misleading success, and the final judgement.

In plain words
What is it for?
Use it to turn an AI feature's expected output and acceptance rules into a specification that can support a grader.
Why use it?
Vague requirements make graders impossible to build or easy for a model to satisfy while still doing the wrong thing.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the model-grader plugin — 1 skill shipped together

Good fit Use it to turn an AI feature's expected output and acceptance rules into a specification that can support a grader.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/abhillashjadhav/ai-pm-essential-skills/model-grader
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.

Any agent
npx skills add Abhillashjadhav/AI-PM-essential-skills --skill model-grader
Clone the repo
git clone --depth 1 https://github.com/Abhillashjadhav/AI-PM-essential-skills

Made for: Claude Code.

Or install model-grader, the plugin that ships this one along with the rest of its 1 skill.

Wrote 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.

agentmods badge for model-grader

README.md
[![agentmods](https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/model-grader/github.svg)](https://agentmods.dev/skills/abhillashjadhav/ai-pm-essential-skills/model-grader)
Your own site
<a href="https://agentmods.dev/skills/abhillashjadhav/ai-pm-essential-skills/model-grader"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/model-grader/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.

agentmods 80×15 button for model-grader

Your own site · 80×15
<a href="https://agentmods.dev/skills/abhillashjadhav/ai-pm-essential-skills/model-grader"><img src="https://agentmods.dev/badge/skills/abhillashjadhav/ai-pm-essential-skills/model-grader.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 174 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,130 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00174 $0.02130
Opus 5.5 $0.00070 $0.00852
Sonnet 5.5 $0.00035 $0.00426
Haiku 4.5 $0.00017 $0.00213

Measured 18d ago against content hash 6ae0d5777b19, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

model-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 18d 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.

model-grader/skills/model-grader/SKILL.md · 86 lines

How it starts

The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Model Grader

Asks the questions that make a grader buildable, and that close the ways one has been cheated before. Nothing else.

A grader needs two things a spec usually lacks. Shape: the fields, types, statuses and codes it must produce — without these it cannot be written at all. Integrity: the decisions whose absence lets a model satisfy every rule while doing the wrong thing — without these it can be written, and cheated.

This skill asks for both, then tests whether the answers are sufficient. It does not write the grader.

Hard rules

  1. Never answer a question on the owner's behalf. Propose options, name the tradeoff, recommend one, then stop until they choose. A declined question goes to OPEN_DECISIONS.md in their words. Never fill a default, never soften a refusal into a guess — a guessed answer looks decided and nobody revisits it.
  2. Never ask a question the spec already answers. Quote the line that answers it and move on. Re-asking settled decisions is how an interview becomes a form nobody finishes.
  3. Never mark a rule verified because an example was written for it. Four gates are tracked separately and never collapsed: the decision, the examples, the verification, the buildability. Writing an example is not running it. Running it is not having it independently adjudicated. Read references/status-model.md and apply all four to every rule.
  4. Never reopen a settled decision to accommodate existing code. If an approved rule is implemented wrongly, that is an implementation defect and is reported as one. Unclear expected behaviour is a policy gap and goes to the owner. Read references/policy-vs-defect.md before classifying anything as either.
  5. Never claim a judgment is deterministic when it is not. A rule whose only test is a view about quality gets a named human reviewer, the evidence they need, and their decision criteria — or it is dropped. Read references/human-judgment.md. Do not invent a proxy metric to make a subjective rule look checkable.
  6. Never expand into building. No grader code, no eval suite, no fixtures, no scoring. This skill ends at a contract and a list of open decisions.
  7. Ask at most three questions per turn, in plain language, with the reason each one matters. This is an interview, not a form.
  8. Never report a rule complete on three of four gates. Decision, Examples, Verification and Buildability are independent and none substitutes for another. The usual failure is treating a written example as verification. Read references/process-discipline.md.
  9. Never freeze without the trail. A contract is trustworthy because of how it was arrived at, not because it reads well. All 26 questions accounted for, four gates shown per rule, no PROPOSED decision left in the body, the dry-run run and counted, and at least one review by someone who is not the interviewer. Report which condition fails; never freeze past a failure.

Read the full file on GitHub · 86 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. 18d ago Changed 6ae0d5777b19
  2. 28d ago First seen · 86 lines · 174 tokens per session scan A 49eb0d72b225

Subscribe to this mod's changes

model-grader is a skill published in the GitHub repository Abhillashjadhav/AI-PM-essential-skills (9 stars, last pushed 10d ago), licensed MIT. It adds 174 tokens to every session and 2,130 once invoked, about $0.0007 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 2026-09-19.

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