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 kurtosis-tech/kurtosis --skill lintgit clone --depth 1 https://github.com/kurtosis-tech/kurtosisWrote 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/kurtosis-tech/kurtosis/lint)<a href="https://agentmods.dev/skills/kurtosis-tech/kurtosis/lint"><img src="https://agentmods.dev/badge/skills/kurtosis-tech/kurtosis/lint/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/kurtosis-tech/kurtosis/lint"><img src="https://agentmods.dev/badge/skills/kurtosis-tech/kurtosis/lint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00338 |
| Opus 5 | $0.00020 | $0.00169 |
| Sonnet 5 | $0.00008 | $0.00068 |
| Haiku 4.5 | $0.00004 | $0.00034 |
Grade A, and why
lint 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 9d 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.
What it actually says
Lint
Lint and format Kurtosis Starlark (.star) files.
Check formatting
# Check a single file
kurtosis lint main.star
# Check a directory
kurtosis lint ./my-package/
# Check multiple files
kurtosis lint main.star lib.star helpers.star
Returns non-zero exit code if formatting issues are found.
Auto-format
Fix formatting in place:
kurtosis lint -f main.star
# Format all files in a package
kurtosis lint -f ./my-package/
Check docstrings
Validate that the main function has a proper docstring:
kurtosis lint -c ./my-package/main.star
# Or point to the package directory
kurtosis lint -c ./my-package/
This checks that the run function has a valid docstring describing its parameters.
CI integration
# Check formatting (fails if not formatted)
kurtosis lint ./my-package/
# Check docstrings
kurtosis lint -c ./my-package/
# Fix-and-retry pattern: auto-fix, then re-lint to verify
kurtosis lint -f ./my-package/
kurtosis lint ./my-package/
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.
- 9d ago First seen · 66 lines · 40 tokens per session scan A 8a59ba7869b7
lint is a skill published in the GitHub repository kurtosis-tech/kurtosis (551 stars, last pushed 6d ago), licensed Apache-2.0. It adds 40 tokens to every session and 338 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 2026-08-30.
Other skills, from other repositories
github-code-review
Comprehensive GitHub code review with AI-powered swarm coordination.
receiving-code-review
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation.
requesting-code-review
Use when completing tasks, implementing major features, or before merging to verify work meets requirements.
harbor-daytona
Use Harbor's Daytona sandbox platform for computer use — creating sandboxes, taking screenshots, sending mouse/keyboard input, and building agent loops. Use when the user wants to interact with a GUI, automate a desktop, do computer use, control a browser visually, or run Claude computer use against a Daytona sandbox.
boost-modules
Skill "boost-modules" from av/harbor, covering harbor boost custom modules, module structure, quick reference, output methods and stream text to client.
address-review
Read PR review comments from GitHub, evaluate, address, and reply.