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 cenconq25/claude-code-app-studio --skill skill-testgit clone --depth 1 https://github.com/cenconq25/claude-code-app-studioWrote 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/cenconq25/claude-code-app-studio/skill-test)<a href="https://agentmods.dev/skills/cenconq25/claude-code-app-studio/skill-test"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/skill-test/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/cenconq25/claude-code-app-studio/skill-test"><img src="https://agentmods.dev/badge/skills/cenconq25/claude-code-app-studio/skill-test.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 | $0.00040 | $0.02160 |
| Opus 5 | $0.00020 | $0.01080 |
| Sonnet 5 | $0.00008 | $0.00432 |
| Haiku 4.5 | $0.00004 | $0.00216 |
Grade A, and why
skill-test 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 6d 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Test
The canonical validator for the project's skill library. Owned by the
template, used by /skill-improve (which calls --mode static), by CI
sanity hooks, and by maintainers auditing drift after a refactor.
Three modes, escalating cost:
- static — pure structural lint. Fast, deterministic. The default.
- spec — runs the skill against a fixture prompt set in
tests/skills/<skill-id>/and checks the response shape. - audit — library-wide coverage report: which agents are referenced by which skills, dead refs, ghost entries, tier drift.
Output is always a markdown report appended to
production/skill-test-report.md.
Purpose / When to Run
Run before:
- Publishing a new skill (
--mode static). - Merging a PR that touches
.claude/skills/(--mode static --all). - A release-readiness gate (
--mode audit). - Investigating "why does this skill misbehave" (
--mode spec).
Skip when:
- Editing only prose inside a skill that already passes static (the lint is unchanged).
- The change is a one-line typo fix.
Inputs
- A skill id (resolves to
.claude/skills/<id>/SKILL.md), or --allto iterate every skill, or--mode <static|spec|audit>to pick the mode (defaults tostatic).
For spec mode, fixtures live at
tests/skills/<skill-id>/cases/<case-name>.md with two sections: ## Input
and ## Expected.
For audit mode, the agent roster lives under .claude/agents/.
Outputs
- A printed verdict per skill:
PASS,WARN,FAIL. - A structured report block appended to
production/skill-test-report.mdwith timestamp. - For
--mode audit, a coverage table mapping skills → agents and agents → skills, with ghost / dead entries flagged.
Mode A: Static Lint
The canonical structural rule set. This is what every skill MUST pass.
Frontmatter rules
- YAML frontmatter delimited by
---at file start. -
name:value matches the parent directory name exactly. -
description:is one sentence, ≤ 240 characters, begins with the action and ends with when to use. -
argument-hint:present using[arg | --flag value]shape. -
user-invocable:is literaltrueorfalse. -
allowed-tools:lists every tool referenced in the body. NoWriteif the body never writes; no missing tool either. -
model:matches the tier policy incoordination-rules.md:- Read-only/format-only →
haiku. - Multi-doc synthesis with high-stakes verdicts →
opus(only the handful listed incoordination-rules.md). - Otherwise →
sonnetor absent.
- Read-only/format-only →
-
agent:if present matches a real file under.claude/agents/.
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.
- 6d ago First seen · 272 lines · 40 tokens per session scan A 85652b549b89
skill-test is a skill published in the GitHub repository cenconq25/claude-code-app-studio (40 stars, last pushed 4mo ago), licensed MIT. It adds 40 tokens to every session and 2,160 once invoked, about $0.0002 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 2026-09-03.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.