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 commands/carolanelfbv/ios-development-agents/documentgit clone --depth 1 https://github.com/CarolaneLFBV/ios-development-agentsWhat 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 | $0.00014 | $0.00524 |
| Opus 5 | $0.00007 | $0.00262 |
| Sonnet 5 | $0.00003 | $0.00105 |
| Haiku 4.5 | $0.00001 | $0.00052 |
Grade A, and why
document 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 2d 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
/ios:document - Documentation Generation
Generate documentation for $ARGUMENTS.
Delegation
Spawn the specialist owning the documented code via the Task tool for accurate DocC/API docs: architecture-specialist for ViewModels/services/models, swiftui-specialist for views.
Arguments
| Flag | Values | Default | Description |
|---|---|---|---|
--type |
readme|api|docc|inline|changelog | readme | Documentation type |
--format |
markdown|docc|html | markdown | Output format |
--export |
<path> |
- | Export documentation |
--update |
- | - | Update existing documentation |
--coverage |
- | - | Show documentation coverage |
Documentation Types
| Type | Output | Description |
|---|---|---|
readme |
README.md | Project documentation |
api |
Inline comments | Swift API documentation |
docc |
Documentation.docc | DocC catalog |
inline |
MARK comments | Code organization |
changelog |
CHANGELOG.md | Version history |
API Documentation Format
/// A view model managing user profile data.
///
/// Use `UserProfileViewModel` to load and update user information.
///
/// ## Topics
/// ### Loading Data
/// - ``loadUser()``
@Observable
class UserProfileViewModel {
/// The currently loaded user.
var user: User?
/// Loads the current user's profile.
/// - Throws: `NetworkError` if the request fails.
func loadUser() async throws { }
}
Examples
/ios:document . --type readme --export README.md
/ios:document Services/ --type api --update
/ios:document . --type docc --format docc
/ios:document UserViewModel.swift --type inline
/ios:document . --coverage
Output
# Documentation: [Target]
## Type: readme | api | docc
## Files: Generated | Updated
## Coverage: X% documented
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.
- 2d ago First seen · 74 lines · 14 tokens per session scan A 01191c853740
document is a command published in the GitHub repository CarolaneLFBV/ios-development-agents (7 stars, last pushed 1mo ago), licensed MIT. It adds 14 tokens to every session and 524 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.