agent-eval

agent-eval is a skill for Claude Code from timurgaleev/vibestack. It costs 78 tokens per session (6,824 once invoked), scanned A, original, MIT.

An evaluation toolkit for an AI agent, prompt, or tool-using workflow. It creates repeatable test tasks, scores results with fixed rules or an AI judge, and tracks results by label.

In plain words
What is it for?
Use it to build evaluation sets, compare prompt versions, measure agent quality, and block regressions before release.
Why use it?
It replaces judging AI changes by eye with repeatable measurements. This helps detect when a prompt or model update makes behavior worse.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the AskUserQuestion tool; positional $N argument.

Good fit Use it to build evaluation sets, compare prompt versions, measure agent quality, and block regressions before release.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/timurgaleev/vibestack/agent-eval
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 timurgaleev/vibestack --skill agent-eval
Clone the repo
git clone --depth 1 https://github.com/timurgaleev/vibestack

Made for: Claude Code.

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 agent-eval

README.md
[![agentmods](https://agentmods.dev/badge/skills/timurgaleev/vibestack/agent-eval/github.svg)](https://agentmods.dev/skills/timurgaleev/vibestack/agent-eval)
Your own site
<a href="https://agentmods.dev/skills/timurgaleev/vibestack/agent-eval"><img src="https://agentmods.dev/badge/skills/timurgaleev/vibestack/agent-eval/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 agent-eval

Your own site · 80×15
<a href="https://agentmods.dev/skills/timurgaleev/vibestack/agent-eval"><img src="https://agentmods.dev/badge/skills/timurgaleev/vibestack/agent-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,824 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 16
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
How audits are shown
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 $0.00078 $0.06824
Opus 5 $0.00039 $0.03412
Sonnet 5 $0.00016 $0.01365
Haiku 4.5 $0.00008 $0.00682

Measured 6d ago against content hash 1287c3e3adbf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

agent-eval 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 6d 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.

skills/agent-eval/SKILL.md · 452 lines

How it starts

The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.

When to invoke

Use when: "evaluate the agent", "build an eval set", "LLM as judge", "prompt regression test", "did the prompt change make it better", "measure prompt quality", "eval harness".

Do not use for web page performance (/benchmark) or for choosing between vendors on a single prompt (/benchmark-models). This skill builds a repeatable test suite for one unit of LLM behavior in the user's own project.

Preamble

eval "$(~/.vibestack/bin/vibe-slug 2>/dev/null)" 2>/dev/null || SLUG="unknown"
_LEARN_FILE="${VIBESTACK_HOME:-$HOME/.vibestack}/projects/${SLUG:-unknown}/learnings.jsonl"
if [ -f "$_LEARN_FILE" ]; then
  _LEARN_COUNT=$(wc -l < "$_LEARN_FILE" 2>/dev/null | tr -d ' ')
  echo "LEARNINGS: $_LEARN_COUNT entries loaded"
  if [ "$_LEARN_COUNT" -gt 5 ] 2>/dev/null; then
    ~/.vibestack/bin/vibe-learnings-search --limit 5 2>/dev/null || true
  fi
else
  echo "LEARNINGS: none yet"
fi

{{include lib/snippets/session-host.md}}

{{include lib/snippets/decision-brief.md}}

{{include lib/snippets/working-protocols.md}}

{{include lib/snippets/state-protocols.md}}

User-invocable

When the user types /agent-eval, run this skill. An optional argument names the unit under test (a file path, a function, or a prompt name); without one, find it in Step 1.


Step 1: Identify the unit under test

Pin down exactly one thing to evaluate. An eval that covers "the whole agent" measures nothing.

Find candidates with Grep and Glob:

  • Prompt templates: **/*prompt*, **/prompts/**, string literals containing You are or system:
  • Agent loops: files that call a chat completion API in a loop and dispatch on tool calls
  • Tool routers: a function that maps a model's tool-call name to a handler
  • Existing evals or fixtures: **/eval*, **/golden*, **/fixtures/**, *.jsonl, transcripts under logs/

For the chosen unit, write down its interface before writing any case:

Field What to record
Inputs user message, conversation history, retrieved context, tool results — and which are fixed vs varied
Outputs free text, structured JSON, a tool-call sequence, a final answer plus a trace
Side effects files written, HTTP calls, database writes — each must be stubbed or sandboxed in the harness
Non-determinism temperature, sampling, tool results that change over time
Cost model, typical tokens in and out, price in USD per million tokens (the unit config.json stores)

Read the full file on GitHub · 452 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. 6d ago First seen · 452 lines · 78 tokens per session scan A 1287c3e3adbf

Subscribe to this mod's changes

agent-eval is a skill published in the GitHub repository timurgaleev/vibestack (6 stars, last pushed 7d ago), licensed MIT. It adds 78 tokens to every session and 6,824 once invoked, about $0.0004 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 2026-09-03.

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