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 launch52-ai/flutter-template --skill presentationgit clone --depth 1 https://github.com/launch52-ai/flutter-templateWrote 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/launch52-ai/flutter-template/presentation)<a href="https://agentmods.dev/skills/launch52-ai/flutter-template/presentation"><img src="https://agentmods.dev/badge/skills/launch52-ai/flutter-template/presentation/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/launch52-ai/flutter-template/presentation"><img src="https://agentmods.dev/badge/skills/launch52-ai/flutter-template/presentation.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 | $0.00044 | $0.01641 |
| Opus 5.5 | $0.00018 | $0.00656 |
| Sonnet 5.5 | $0.00009 | $0.00328 |
| Haiku 4.5 | $0.00004 | $0.00164 |
Grade A, and why
presentation 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Presentation - Presentation Layer Generator
Generate presentation layer components that handle UI state and render screens using domain entities.
Philosophy
The presentation layer is domain-aware, data-blind:
- Imports domain entities and repository interfaces
- Never imports data layer (models, repository implementations)
- Uses Freezed sealed states for exhaustive pattern matching
- Uses Riverpod AsyncNotifier with disposal safety
This keeps the UI decoupled from data sources and serialization details.
When to Use This Skill
- After
/datahas implemented repositories - When creating screens from spec requirements
- When adding new screens or actions to existing features
- User asks to "create screen", "implement UI", "add presentation layer"
What This Skill Creates
lib/features/{feature}/presentation/
├── providers/
│ ├── {feature}_state.dart # Freezed sealed state
│ └── {feature}_notifier.dart # AsyncNotifier with safe state
├── screens/
│ └── {feature}_screen.dart # ConsumerWidget with pattern matching
└── widgets/
└── {feature}_item.dart # Extracted reusable widgets
Workflow
Step 1: Locate Requirements
Check for:
lib/features/{feature}/.spec.md # Feature specification
lib/features/{feature}/domain/ # Domain entities
lib/features/{feature}/data/ # Repository implementation
If domain/data layers don't exist, ask user to run /domain and /data first.
Step 2: Analyze UI Requirements
From the spec, identify:
- Screens - Section 4.1 (list, detail, form, etc.)
- State Fields - What data each screen needs
- Actions - User interactions (refresh, create, delete, submit)
- Navigation - How screens connect
Step 3: Generate Code
Use reference files in reference/ directory as templates:
| Component | Reference File |
|---|---|
| List state | reference/providers/list_state.dart |
| Detail state | reference/providers/detail_state.dart |
| Form state | reference/providers/form_state.dart |
| Basic notifier | reference/providers/basic_notifier.dart |
| CRUD notifier | reference/providers/crud_notifier.dart |
| Form notifier | reference/providers/form_notifier.dart |
| List screen | reference/screens/list_screen.dart |
| Detail screen | reference/screens/detail_screen.dart |
| Form screen | reference/screens/form_screen.dart |
| List item widget | reference/widgets/list_item.dart |
| Form field widget | reference/widgets/form_field.dart |
What ships with it
14 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.
- examples.md 17 KB
- patterns-guide.md 3.8 KB
- reference/providers/basic_notifier.dart 1.7 KB
- reference/providers/crud_notifier.dart 2.5 KB
- reference/providers/detail_notifier.dart 1.6 KB
- reference/providers/detail_state.dart 1.2 KB
- reference/providers/form_notifier.dart 3.0 KB
- reference/providers/form_state.dart 1.3 KB
- reference/providers/list_state.dart 1.1 KB
- reference/screens/detail_screen.dart 3.5 KB
- reference/screens/form_screen.dart 3.4 KB
- reference/screens/list_screen.dart 2.6 KB
- reference/widgets/form_field.dart 3.2 KB
- reference/widgets/list_item.dart 1.6 KB
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 · 197 lines · 44 tokens per session scan A c0a4824c5579
presentation is a skill published in the GitHub repository launch52-ai/flutter-template (2 stars, last pushed 7mo ago), licensed MIT. It adds 44 tokens to every session and 1,641 once invoked, about $0.0002 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 2026-08-31.
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