Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/growthxai/outputnpx agentmods add skills/growthxai/output/output-dev-prompt-fileWrote 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/growthxai/output/output-dev-prompt-file)<a href="https://agentmods.dev/skills/growthxai/output/output-dev-prompt-file"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-dev-prompt-file/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/growthxai/output/output-dev-prompt-file"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-dev-prompt-file.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 484 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00036 | $0.05005 |
| Opus 5 | $0.00018 | $0.02502 |
| Sonnet 5 | $0.00007 | $0.01001 |
| Haiku 4.5 | $0.00004 | $0.00500 |
Grade A, and why
output-dev-prompt-file 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 6d 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 — 696 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creating .prompt Files
Overview
This skill documents how to create .prompt files for LLM operations in Output SDK workflows. Prompt files use YAML frontmatter for configuration and Liquid.js templating for dynamic content.
When to Use This Skill
- Creating prompts for LLM-powered workflow steps
- Configuring LLM provider settings (model, temperature, etc.)
- Using template variables in prompts
- Troubleshooting prompt formatting issues
Location Convention
Prompt files are stored INSIDE the workflow folder:
src/workflows/{workflow-name}/
├── workflow.ts
├── steps.ts
├── types.ts
└── prompts/
├── [email protected]
├── [email protected]
└── [email protected]
Important: Prompts are workflow-specific and live inside the workflow folder, NOT in a shared location.
File Naming Convention
{promptName}@v{version}.prompt
Examples:
The version suffix (@v1, @v2) allows for prompt versioning without breaking existing code.
Basic Structure
Picking a model? See
output-dev-model-selectionfor the current decision tree and AI Gateway lookup script. Examples below show concrete IDs as of 2026-05-04 — refresh them with that skill.
---
provider: anthropic
# current as of 2026-05-04 — run output-dev-model-selection for the latest
model: claude-sonnet-4-6
temperature: 0.7
maxOutputTokens: 4096
---
<system>
System instructions go here.
</system>
<user>
User message with {{ variable }} placeholders.
</user>
The body uses exactly one mode:
- Message mode starts with a role tag and produces
messages. Use it withgenerateText,generateTextWithStreaming,streamText, andAgent. - Instruction mode starts with plain text and produces
instructions. Use it withgenerateImageor when consumingloadPrompt()results directly:
---
provider: openai
model: gpt-image-1
---
Create a cinematic image of {{ subject }}.
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.
- 6d ago Changed · +2 lines 6dc82fd9c306
- 10d ago First seen · 694 lines · 36 tokens per session scan A 714fd34e877b
output-dev-prompt-file is a skill published in the GitHub repository growthxai/output (435 stars, last pushed today), licensed Apache-2.0. It adds 36 tokens to every session and 5,005 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.
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