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 growthxai/output --skill output-plan-workflowgit clone --depth 1 https://github.com/growthxai/outputWrote 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-plan-workflow)<a href="https://agentmods.dev/skills/growthxai/output/output-plan-workflow"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-plan-workflow/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-plan-workflow"><img src="https://agentmods.dev/badge/skills/growthxai/output/output-plan-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium MCP Rug Pull · line 246 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00050 | $0.02031 |
| Opus 5.5 | $0.00020 | $0.00812 |
| Sonnet 5.5 | $0.00010 | $0.00406 |
| Haiku 4.5 | $0.00005 | $0.00203 |
Grade A, and why
output-plan-workflow 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 — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your task is to generate a comprehensive Output.ai workflow implementation plan in markdown format.
The plan will be displayed to the user who can then decide what to do with it.
Please respond with only the final version of the plan.
Use the todo tool to track your progress through the plan creation process.
Plan Creation Rules
Overview
Generate detailed specifications for implementation of a new workflow.
Output Path
All plan outputs go to: .outputai/plans/YYYY_MM_DD_<workflow_name>_<task_name>/PLAN.md
<process_flow>
Step 0: Arguments Analysis
Analyze the arguments the user provided:
Ensure they have provided:
- workflow_description: The description of the workflow to be created
- additional_instructions: Additional instructions for the workflow
If not, ask the user for the missing information.
Step 1: Context Gathering
Take the time to gather all the context you need to create a comprehensive plan.
- Read any given files or links
- Find any related workflows in the project
- Read the projects documentation files
Step 2: Requirements Clarification
Clarify scope boundaries and technical considerations by asking numbered questions as needed to ensure clear requirements before proceeding.
<clarification_areas> - in_scope: what is included - out_of_scope: what is excluded (optional) - functionality specifics - UI/UX requirements - integration points <llm_provider> - Ask which LLM provider the user wants to use (anthropic, openai, google-vertex, or amazon-bedrock) - Default to anthropic if the user has no preference - All prompt files in the workflow must use the same provider unless the user explicitly requests otherwise - Record the chosen provider so it flows through to prompt engineering (step 6) and implementation </llm_provider> </clarification_areas>
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 · 297 lines · 50 tokens per session scan A 3ead1ada7d7d
output-plan-workflow is a skill published in the GitHub repository growthxai/output (443 stars, last pushed today), licensed Apache-2.0. It adds 50 tokens to every session and 2,031 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 2026-08-30.
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