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/adambien/airails/characterization-testsnpx skills add AdamBien/airails --skill characterization-testsgit clone --depth 1 https://github.com/AdamBien/airailsWrote 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/adambien/airails/characterization-tests)<a href="https://agentmods.dev/skills/adambien/airails/characterization-tests"><img src="https://agentmods.dev/badge/skills/adambien/airails/characterization-tests.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 | $0.00151 | $0.01047 |
| Opus 5 | $0.00076 | $0.00524 |
| Sonnet 5 | $0.00030 | $0.00209 |
| Haiku 4.5 | $0.00015 | $0.00105 |
Grade A, and why
characterization-tests 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 4d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Characterization Tests
A regular test fails when behavior is wrong; a characterization test fails when behavior is different. Record what the system does — bugs included — and pin it as the expected value. Correctness is the information the legacy system lost; observed behavior is the only surviving specification, and downstream consumers may depend on the bugs.
Characterization tests are system tests: black-box, against the running system, from the outside — the system-tests skill owns that contract. The only difference from ordinary system tests is the origin of the expectations — recorded, not specified.
Surfaces
The observable surface is a property of the system, not of this skill. Inventory all of them:
| Surface | Stimulus → Observation | Mechanics |
|---|---|---|
| HTTP (REST, server-rendered pages) | request → response | references/http.md |
| Messaging (JMS, queues) | message in → message(s) out | same discipline; document per system |
| Batch / file exchange | input file → output file, report | same discipline; document per system |
| CLI | invocation → exit code, output | same discipline; document per system |
| Database side effects | any stimulus → state delta | record only when no outer surface shows the effect |
HTTP mechanics are bundled; for other surfaces apply the identical record/normalize/replay discipline and document the surface mechanics next to the recordings. Prefer the outermost surface that shows the behavior — assert database state only when nothing above it does.
Record
- Inventory the system's surfaces and their entry points (JAX-RS resources,
web.xmlmappings, queue names, batch jobs, and the external contracts inmigration/CONCEPTS.mdwhen present). - Ask the user: target coordinates (base URL, broker, job trigger — never guess), environment, credentials, and which business flows matter most. Confirm the target is not an unprotected production system.
- Observe-only by default: record stimuli without side effects. Record mutating stimuli (writes, consumed messages, batch runs) only after explicit confirmation, only against a disposable environment with resettable seed data.
- Execute each stimulus, normalize the observation per references/recording-format.md and the surface reference, store one file per interaction under
migration/characterization/recordings/. - Each recording must replay in isolation — note seed-data assumptions in its header.
What ships with it
3 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.
- 4d ago First seen · 57 lines · 151 tokens per session scan A f072ebd1b3c9
characterization-tests is a skill published in the GitHub repository AdamBien/airails (48 stars, last pushed 14d ago), licensed MIT. It adds 151 tokens to every session and 1,047 once invoked, about $0.0008 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 2026-08-30.
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