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/appsvortex/arness/arn-code-swiftnpx skills add AppsVortex/arness --skill arn-code-swiftgit clone --depth 1 https://github.com/AppsVortex/arnessWhat 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 | $0.00137 | $0.08346 |
| Opus 5 | $0.00068 | $0.04173 |
| Sonnet 5 | $0.00027 | $0.01669 |
| Haiku 4.5 | $0.00014 | $0.00835 |
Grade A, and why
arn-code-swift 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 yesterday.
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 — 617 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Arness Swift
Implement small features and enhancements through a lightweight, pattern-aware workflow: quick architectural assessment, inline plan, direct execution, verification, and review -- all in a single session. Every swift implementation produces a plan (SWIFT_<name>.md) and a report (SWIFT_REPORT.json), giving the same auditability as the full pipeline without the overhead.
This skill follows the arn-code-bug-spec dual-path architecture: assess complexity, route simple tasks to direct execution, escalate moderate tasks to task-tracked execution, and redirect complex tasks to the full pipeline.
This is an execution skill. It runs in normal conversation (NOT plan mode).
Step 0: Ensure Configuration
Read ${CLAUDE_PLUGIN_ROOT}/skills/arn-code-ensure-config/references/step-0-fast-path.md and follow its instructions. This guarantees a user profile exists and ## Arness is configured with Arness Code fields before proceeding.
Pipeline Position
arn-code-swift (this skill)
========================
For changes touching 1-8 files
with no new architectural patterns
Entry: User describes a small change
|
v
Full: init --> [ feature-spec | bug-spec | SWIFT ] --> plan --> ...
|
+-------------------+-------------------+
| | |
SIMPLE MODERATE COMPLEX
(1-3 files) (4-8 files) (9+ files)
| | |
Plan --> Plan --> Redirect to
Execute TaskCreate --> /arn-code-feature-spec
in session Execute
| |
Review Review
| |
Ship Ship
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.
- yesterday First seen · 617 lines · 137 tokens per session scan A 21f7ad8e4d73
arn-code-swift is a skill published in the GitHub repository AppsVortex/arness (33 stars, last pushed 1mo ago), licensed MIT. It adds 137 tokens to every session and 8,346 once invoked, about $0.0007 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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