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 skills add Cristhianzl/claude-skills-czl --skill evaluating-ai-outputgit clone --depth 1 https://github.com/Cristhianzl/claude-skills-czlWrote 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/cristhianzl/claude-skills-czl/evaluating-ai-output)<a href="https://agentmods.dev/skills/cristhianzl/claude-skills-czl/evaluating-ai-output"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/evaluating-ai-output/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.
<a href="https://agentmods.dev/skills/cristhianzl/claude-skills-czl/evaluating-ai-output"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/evaluating-ai-output.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00106 | $0.00879 |
| Opus 5 | $0.00053 | $0.00439 |
| Sonnet 5 | $0.00021 | $0.00176 |
| Haiku 4.5 | $0.00011 | $0.00088 |
Grade A, and why
evaluating-ai-output 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evaluating AI output
Code is deterministic; LLM output isn't. A feature that "worked once" can fail the next call on the same input. Evals are the unit tests of AI work — they measure how often and how well the output meets the bar, not just that it can.
Read first (always)
List learnings/ and read anything relevant — provider quirks, rubric calibration, and known-flaky cases for this project belong there.
Define expected behavior BEFORE you implement
Write the eval first: the inputs, what a good output looks like, and what must never happen. If you can't state how you'd grade it, you don't yet understand the feature.
Measure across repeated trials
Run each case k times (LLM output varies) and report:
- pass@k — at least one of k attempts succeeds. Measures capability ("can it do this at all?"). Typical target: pass@3 > 90%.
- pass^k — all k attempts succeed. Measures stability/reliability ("does it do this every time?"). Use for critical paths (auth, money, irreversible actions). pass^3 means 3 consecutive clean runs.
A feature can have high pass@k but low pass^k — impressive once, unreliable in production. Match the metric to the risk.
Three graders (use the cheapest that's trustworthy)
| Grader | How | Use for |
|---|---|---|
| Code-based | Deterministic check — regex/grep, schema/JSON validation, an assertion, a tool call that must appear |
Anything machine-verifiable (format, presence, exact values). Always prefer this. |
| Model-based | A model scores the output 1–5 against a written rubric | Quality/judgment that code can't check (relevance, tone, reasoning). Calibrate the rubric on a few human-labeled examples. |
| Human | A person reviews, tagged risk LOW / MED / HIGH | High-stakes or ambiguous cases. Never fully automate security review — keep a human in the loop there. |
Build the eval set
Cover representative cases, adversarial/edge cases (the failure modes from your threat model — see skills/threat-modeling), and regression cases (every bug becomes a permanent eval). Keep a baseline (committed scores) and gate changes on "no regression vs. baseline". Store the eval set, the baseline, and run logs alongside the feature.
What ships with it
1 file 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.
- 12d ago First seen · 55 lines · 106 tokens per session scan A 347be76034c7
evaluating-ai-output is a skill published in the GitHub repository Cristhianzl/claude-skills-czl (5 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 879 once invoked, about $0.0005 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 2026-08-31.
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