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 validating-in-realitygit 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/validating-in-reality)<a href="https://agentmods.dev/skills/cristhianzl/claude-skills-czl/validating-in-reality"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/validating-in-reality/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/validating-in-reality"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/validating-in-reality.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 | $0.00140 | $0.00949 |
| Opus 5.5 | $0.00056 | $0.00380 |
| Sonnet 5.5 | $0.00028 | $0.00190 |
| Haiku 4.5 | $0.00014 | $0.00095 |
Grade A, and why
validating-in-reality 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 1mo 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validating in reality
A change isn't done because the code looks right — it's done when the running system proves it. This skill turns a user-provided cURL (or endpoint, or repro) into the acceptance test, and replaces "should work" with pasted evidence.
Read first (always)
List learnings/ and read anything relevant — how this project starts its server, seeds data, connects to the DB, and authenticates test requests lives there and overrides the defaults here.
The principle: evidence over inference
Reading the diff tells you what the code should do. Only the running system tells you what it does. When the user supplies a cURL, an endpoint, or a repro, that artifact is the definition of done — the task is complete when it behaves correctly against the live system, and you have the output to show it.
Workflow
- Confirm the system is running. Find the server (the project's run command,
docker compose ps, the port in the cURL). If nothing is running and you can't start it with the project's documented command, say so explicitly and stop — never simulate a validation you didn't run. - Run the acceptance test BEFORE the change (bug: prove it reproduces — the live RED, mirroring
fixing-bugs; feature: prove the behavior is missing). Paste the actual response. - Implement (per
fixing-bugs/developing-features— tests included as usual). - Run the acceptance test AFTER. Paste the actual response and diff it against the BEFORE. Status code, body, and headers must show the expected change — actually compare them, don't eyeball.
- Check persisted state, not just the response. Query the database (the project's client/ORM shell) and confirm the row/document actually changed — created, updated, deleted — as claimed. An API can return 200 and persist nothing.
- Exercise the negative cases: wrong/missing auth, invalid payload, nonexistent ID. The fix must not have opened a hole the happy-path cURL can't see.
- If the flow has a UI, drive it end-to-end with
skills/playwright-cli: perform the user action in the browser and verify the visible result — the backend being right doesn't prove the frontend wired it. - Report with evidence. For each step: the command run and the real output (trimmed to the relevant part). Redact tokens/secrets/PII from anything you paste.
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.
- 1mo ago First seen · 47 lines · 140 tokens per session scan A 63fb1616ce66
validating-in-reality is a skill published in the GitHub repository Cristhianzl/claude-skills-czl (5 stars, last pushed 1mo ago), licensed MIT. It adds 140 tokens to every session and 949 once invoked, about $0.0006 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 2026-08-31.
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