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 JohnWayneeee/ai-agent-skills --skill payoff-action-modelinggit clone --depth 1 https://github.com/JohnWayneeee/ai-agent-skillsWrote 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/johnwayneeee/ai-agent-skills/payoff-action-modeling)<a href="https://agentmods.dev/skills/johnwayneeee/ai-agent-skills/payoff-action-modeling"><img src="https://agentmods.dev/badge/skills/johnwayneeee/ai-agent-skills/payoff-action-modeling/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/johnwayneeee/ai-agent-skills/payoff-action-modeling"><img src="https://agentmods.dev/badge/skills/johnwayneeee/ai-agent-skills/payoff-action-modeling.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 | $0.00086 | $0.03798 |
| Opus 5 | $0.00043 | $0.01899 |
| Sonnet 5 | $0.00017 | $0.00760 |
| Haiku 4.5 | $0.00009 | $0.00380 |
Grade A, and why
payoff-action-modeling 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 9d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outcome Action Modeling
Use this skill before designing or polishing any UI state where the user has achieved a meaningful outcome and now needs to understand what happened, what exists, and what to do next.
This applies to AI products, CRUD systems, dashboards, onboarding flows, imports, uploads, reporting tools, automation tools, admin panels, PM tools, CMS platforms, deployment or sync flows, collaborative review systems, and workflow products.
The goal is to answer: "Now that the user has value or progress, what might they reasonably want to do next, and where should each action live?"
Core Output
Produce an action model with:
Primary outcome: the result, resource, state, or completion the user should understand first.Outcome state type:completion,generation,workspace,review,handoff,recovery, orcontinuation.User questions: realistic "What if I want to..." questions from the user's point of view.Action scope:outcome,selection,item,continuation,navigation,recovery, orassistance.Intent pressure:immediate,contextual, ordeferred.UI placement:primary CTA,secondary action,per-item action,row menu,accordion detail,bulk toolbar,empty/optional state,status area,shell navigation, ornext screen.Required vs optional: whether the action must be visible now or can be deferred.Copy: short labels written as user-facing outcomes, not implementation details.
Use this table shape unless the user requests another format:
| User question | Scope | Pressure | Action | Placement | Required? | Label |
|---|---|---|---|---|---|---|
| How do I open one item? | item | contextual | open item details | per-item action | yes, if item inspection matters | Open |
| How do I act on everything? | outcome | immediate | apply action to full result | primary CTA | yes | Download all |
| How do I recover a removed item? | recovery | deferred | restore from history | secondary action or history screen | yes, if removal exists | Restore |
What ships with it
1 file 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.
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.
- 9d ago First seen · 299 lines · 86 tokens per session scan A ce2890e4b965
payoff-action-modeling is a skill published in the GitHub repository JohnWayneeee/ai-agent-skills (1 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 3,798 once invoked, about $0.0004 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.
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