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.
git clone --depth 1 https://github.com/Ingramradical235/anty-frameworkWrote 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/commands/ingramradical235/anty-framework/quickstart)<a href="https://agentmods.dev/commands/ingramradical235/anty-framework/quickstart"><img src="https://agentmods.dev/badge/commands/ingramradical235/anty-framework/quickstart/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/commands/ingramradical235/anty-framework/quickstart"><img src="https://agentmods.dev/badge/commands/ingramradical235/anty-framework/quickstart.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 | $0.00017 | $0.01425 |
| Opus 5.5 | $0.00007 | $0.00570 |
| Sonnet 5.5 | $0.00003 | $0.00285 |
| Haiku 4.5 | $0.00002 | $0.00143 |
Grade A, and why
quickstart 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.
This is a copy
100% identical to quickstart — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/anty:quickstart — 5-Minute Quick Onboarding to First Action
Purpose
Get the founder from zero to their first actionable marketing/sales action in under 5 minutes. Collects only the minimum information needed to generate useful actions, then deepens context over time through progressive profiling.
This is NOT a replacement for /anty:onboard. It is a fast-track entry point:
/anty:quickstart→ first action in 5 minutes, shallow context/anty:onboard→ full 14-topic business context, deep strategic planning
Before Starting
-
Check if
.anty/business-context.yamlalready exists and is complete.- If complete: "Your full business context already exists. Running quickstart to generate your first action based on existing context." → Skip to Step 3 (Action Generation).
- If partial: Read existing data, skip questions already answered, fill gaps with Steps 1-2.
- If does not exist: Create
.anty/directory if needed, start from Step 1.
-
Ensure
.anty/config.yamlexists. Create with defaults if not.
Step 1: Core Question (1 question, covers Product + Target)
Ask ONE combined question:
"What does your product do, and who is it for? One or two sentences is enough."
Example answer: "AI tool that automates sales and marketing for startup founders who just launched their product."
From this single answer, extract:
product.descriptionproduct.problem_solved(infer from the product description)target_market.ideal_customer
Echo back: "So [product] for [customer]. Got it."
Step 2: Channel Question (1 question)
"Where do your target customers spend time online? List the top 2-3 places."
Example answer: "HackerNews, Reddit, LinkedIn"
From this, extract:
target_market.customer_channels
Echo back: "Targeting [channels]. Clear."
Step 3: Action Generation (automatic, no question)
Based on the collected (or pre-existing) context, generate 3 concrete first actions. Each action must be:
- Immediately executable (can be done today)
- Specific (not "do marketing" but "post X on Y with Z angle")
- Low-cost (time only, no paid spend)
- Measurable (define what success looks like)
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 · 175 lines · 17 tokens per session scan A 61cb7d93bea5
quickstart is a command published in the GitHub repository Ingramradical235/anty-framework (1 stars, last pushed 10d ago), licensed MIT. It adds 17 tokens to every session and 1,425 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to quickstart, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
onboard
/anty:onboard — QUEST-Based Conversational Interview.
plan
/anty:plan — Strategy Kernel Generation.
actions
/anty:actions — 3-Option Choice Approval with Nudge Architecture.
quickstart
/anty:quickstart — 5-Minute Quick Onboarding to First Action.
review
/anty:review — 5-Question Review Engine.
scan
/anty:scan — Analysis Cycle (10-Point).