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 wangwei1237/agent-skills --skill skill-testinggit clone --depth 1 https://github.com/wangwei1237/agent-skillsWrote 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/wangwei1237/agent-skills/skill-testing)<a href="https://agentmods.dev/skills/wangwei1237/agent-skills/skill-testing"><img src="https://agentmods.dev/badge/skills/wangwei1237/agent-skills/skill-testing/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/wangwei1237/agent-skills/skill-testing"><img src="https://agentmods.dev/badge/skills/wangwei1237/agent-skills/skill-testing.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 | $0.00061 | $0.02670 |
| Opus 5.5 | $0.00024 | $0.01068 |
| Sonnet 5.5 | $0.00012 | $0.00534 |
| Haiku 4.5 | $0.00006 | $0.00267 |
Grade A, and why
skill-testing 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 2mo 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Testing
Use this skill to evaluate another Skill from its tests/skill_cases.yaml file. The test target is Skill behavior, not real business execution. Keep every case in dry-run mode unless the YAML explicitly says otherwise.
Default behavior is end-to-end. A user request to test a Skill or run a tests/skill_cases.yaml file authorizes isolated sub-agent capture for the test cases: generate dry-run prompts, run each rewritten prompt in a fresh isolated sub-agent, capture the actual dry-run response, format it, score it, and return the final Skill Test Report in the same turn.
Inputs
Accept either:
- A Skill directory containing
SKILL.mdandtests/skill_cases.yaml. - A direct path to a
skill_cases.yamlfile. - A YAML case file plus Agent responses captured from a separate harness.
The default response directory is the target Skill's tests/tmp/response/ directory. For example, skills/my-skill/tests/skill_cases.yaml uses skills/my-skill/tests/tmp/response/.
If the user provides a response directory explicitly, consume those response files instead of generating new responses. Otherwise, create or replace response files in the default response directory with sub-agent outputs and continue to final scoring.
Workflow
- Load the YAML test file.
- Validate required top-level fields:
version,skill.name,defaults.mode,defaults.execution, andcases. - Validate every case has
id,type,title,query,inputs.files,inputs.context,expected.skill,expected.must_include,expected.must_not_include,expected.artifacts,expected.routing, andassertions. - Build one dry-run prompt per case from the original
query. - Unless an explicit response directory was provided, dispatch each rewritten prompt to a fresh sub-agent in dry-run mode and capture the sub-agent's final response text. The sub-agent must receive only the rewritten prompt plus the dry-run restriction, not the YAML expected fields, assertions, scoring rubric, or report from other cases.
- Save the raw response under
<target-skill>/tests/tmp/raw_response/<case_id>.md. - Format the raw response into the Skill Test response contract and save one scorer-facing response file per case in
<target-skill>/tests/tmp/response/<case_id>.md. - Run the scorer against the formatted response directory.
- Output a deterministic final report with case-level status and an overall result.
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.
- 2mo ago First seen · 202 lines · 61 tokens per session scan A c7d63aea20ab
skill-testing is a skill published in the GitHub repository wangwei1237/agent-skills (4 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 61 tokens to every session and 2,670 once invoked, about $0.0002 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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