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 agentmods add skills/agentailor/fullstack-langgraph-nextjs-agent/agent-eval-casesnpx skills add agentailor/fullstack-langgraph-nextjs-agent --skill agent-eval-casesgit clone --depth 1 https://github.com/agentailor/fullstack-langgraph-nextjs-agentWrote 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/agentailor/fullstack-langgraph-nextjs-agent/agent-eval-cases)<a href="https://agentmods.dev/skills/agentailor/fullstack-langgraph-nextjs-agent/agent-eval-cases"><img src="https://agentmods.dev/badge/skills/agentailor/fullstack-langgraph-nextjs-agent/agent-eval-cases.svg" alt="Measured on agentmods" 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 | $0.00174 | $0.04724 |
| Opus 5 | $0.00087 | $0.02362 |
| Sonnet 5 | $0.00035 | $0.00945 |
| Haiku 4.5 | $0.00017 | $0.00472 |
Grade A, and why
agent-eval-cases 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.
How it starts
The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Eval Cases
Overview
A case is one task you give the agent, plus the graders that decide whether what came back was acceptable.
That is the whole shape — and note what it is not. It is not an input paired with an expected output. There is no single correct answer string to compare against, which is why the right-hand side is a list of graders rather than a value. Many frameworks offer an expected / expected_output field; reaching for it by default is the most common way to write a suite that measures phrasing instead of behavior.
Three consequences shape everything below:
- A grader can assert on what the agent said or on what the agent did — which tools it called, what is in the store afterwards, whether a gate fired. The second kind is usually the stronger one.
- Graders see only what the run recorded. That record — the final answer, the tool calls, what each returned, what a guardrail stopped — is called the capture here; your harness may have no name for it at all. It bounds every assertion you can write, so find out what is in it before designing a case around one.
- A case is not passed or failed by a person reading it, so whatever you want to be true has to be expressible as code (or as a rubric a model can apply, or — occasionally — as a human's read).
The hard part is not the format. It is knowing which handful of tasks are worth paying a model to run, repeatedly, forever.
The workflow
Steps 1–3 are the ones that decide whether a suite is worth having. Do not skip to step 5.
Step 0 — Confirm where these will run
A case needs something to run it on. Before writing any, find out what exists.
Look for an existing suite first. Search for an eval/evals/evaluation directory, a case or dataset type, *.eval.* files, or a framework dependency. If one exists, write cases in its idiom — its case type, its grader catalog, its repeat convention — and stop looking. See references/vocabulary.md.
What ships with it
4 files 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.
- yesterday First seen · 196 lines · 174 tokens per session scan A 754adf9bf98c
agent-eval-cases is a skill published in the GitHub repository agentailor/fullstack-langgraph-nextjs-agent (131 stars, last pushed yesterday), licensed MIT. It adds 174 tokens to every session and 4,724 once invoked, about $0.0009 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 2026-09-05.
Other skills, from other repositories
using-superpowers
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions.
code-review
This skill should be used when the user asks for a code review of a change — a pull request, merge request, branch, commit range, working-tree change, or pasted diff — covering correctness, security, performance, structure, and repository-specific rules. Trigger on phrases like "review this PR", "review this MR"…
plan
This skill should be used when the user asks to explore a codebase and design implementation plans without making any changes. Trigger when users say 'plan how to implement X', 'design an approach for Y', 'explore the codebase before changing Z', 'create an implementation strategy', 'analyze how to refactor X', 'map…
security-audit
This skill should be used when the user asks to review code for security vulnerabilities, audit a pull request or merge request for risks, check if code is safe, find injection flaws or hardcoded secrets, or assess the security posture of a feature or codebase area. Use this skill even when the user doesn't say…
init
This skill should be used when a user asks to initialize a repository for AI agents, create or update an AGENTS.md file, scaffold agent instructions for a codebase, or improve existing agent guidance. Triggers include phrases like 'create an AGENTS.md', 'set up agent instructions', 'initialize this repo for coding…
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.