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 cloudshipai/station --skill station-configgit clone --depth 1 https://github.com/cloudshipai/stationWrote 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/cloudshipai/station/station-config)<a href="https://agentmods.dev/skills/cloudshipai/station/station-config"><img src="https://agentmods.dev/badge/skills/cloudshipai/station/station-config/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/cloudshipai/station/station-config"><img src="https://agentmods.dev/badge/skills/cloudshipai/station/station-config.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 | $0.00037 | $0.01280 |
| Opus 5.5 | $0.00015 | $0.00512 |
| Sonnet 5.5 | $0.00007 | $0.00256 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
station-config 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 1mo 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Station Configuration
Configure Station settings including AI provider, coding backend, CloudShip integration, and more.
Browser-Based Configuration (Recommended)
The easiest way to configure Station is via the browser UI:
# Opens browser-based config editor
# Automatically starts server if not running
stn config --browser
This opens a visual editor with:
- All configuration sections organized
- Conditional fields (e.g., backend-specific settings only show for selected backend)
- Secret fields with show/hide toggle
- Validation and defaults
CLI Configuration Commands
View Configuration
# Show all config (secrets redacted)
stn config show
# Show specific section
stn config show ai
stn config show coding
stn config show cloudship
# Show config file path
stn config path
# Show all available config keys
stn config schema
Set Configuration Values
# Set a value
stn config set <key> <value>
# Examples
stn config set ai_provider anthropic
stn config set ai_model claude-sonnet-4-20250514
stn config set coding.backend opencode
stn config set cloudship.enabled true
# Set nested values
stn config set coding.nats.url nats://localhost:4222
stn config set cloudship.api_key cst_xxx
# Reset to default
stn config reset <key>
Edit Config File Directly
# Open in $EDITOR
stn config edit
Configuration Sections
AI Provider (ai_*)
| Key | Description | Default |
|---|---|---|
ai_provider |
Provider: openai, anthropic, ollama, gemini | openai |
ai_model |
Model name | gpt-4o |
ai_api_key |
API key (secret) | - |
ai_base_url |
Custom base URL for OpenAI-compatible | - |
Coding Backend (coding.*)
| Key | Description | Default |
|---|---|---|
coding.backend |
Backend: opencode, opencode-nats, opencode-cli, claudecode | opencode-cli |
coding.workspace_base_path |
Base path for workspaces | /tmp/station-coding |
coding.max_attempts |
Max retry attempts | 3 |
coding.task_timeout_min |
Task timeout in minutes | 30 |
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.
- 1mo ago First seen · 183 lines · 37 tokens per session scan A bf49bd8a5118
station-config is a skill published in the GitHub repository cloudshipai/station (429 stars, last pushed 9mo ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,280 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
symbolpeek
Token-efficient code, JSON, and Markdown navigation; use before whole-file reads.
ai-agent-evaluation-benchmarking
Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.
create-tech-design
Only use when the user explicitly invokes this skill by name.
iterate-tech-design
Only use when the user explicitly invokes this skill by name.
otto-pr-review-loop
Run Polly and OCR on a PR until all findings settle.
implement-outline
Only use when the user explicitly invokes this skill by name.