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 shawnpang/startup-founder-skills --skill onboarding-flowgit clone --depth 1 https://github.com/shawnpang/startup-founder-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/shawnpang/startup-founder-skills/onboarding-flow)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/onboarding-flow"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/onboarding-flow/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/shawnpang/startup-founder-skills/onboarding-flow"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/onboarding-flow.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 | $0.00050 | $0.02186 |
| Opus 5 | $0.00025 | $0.01093 |
| Sonnet 5 | $0.00010 | $0.00437 |
| Haiku 4.5 | $0.00005 | $0.00219 |
Grade A, and why
onboarding-flow 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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- onboarding-flow — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Onboarding Flow
When to Use
Activate when a founder or product lead needs to design onboarding for new users, improve activation rates, reduce time-to-value, fix drop-off after signup, redesign a guided setup experience, or re-engage users who stalled during onboarding. This includes prompts like "design our onboarding flow," "users are dropping off after signup," "build an activation checklist," "our time-to-value is too long," "how do we get users to their aha moment faster," or "first sessions are not sticking."
Do NOT use for employee onboarding, service process design, or when the product lacks a stable value proposition. This skill is for in-product user activation only.
Context Required
- From startup-context: product type (B2B/B2C/PLG), target user persona, current activation rate, defined "aha moment," product complexity level, existing onboarding steps, and current tools (email platform, analytics, in-app messaging).
- From the user: where users currently drop off, the key action that correlates with retention (the activation event), number of steps currently required to reach value, any qualitative feedback from churned users about the setup experience, and what "healthy" first-session behavior looks like.
Workflow
- Intake and goal-framing — Read startup-context if available. Establish the activation goal, current baseline metrics, and what success looks like. If the user does not know their activation event, help them hypothesize based on product type (see benchmarks below).
- Map the current journey — Document every step from signup to activation event, including screens, emails, wait states, and decision points. Identify friction points, unnecessary steps, and moments of confusion.
- Identify friction and drop-off — Pinpoint where users abandon the flow. Categorize blockers: too many steps, unclear value, technical obstacles, cognitive overload, or missing guidance.
- Define behavioral activation moments — Identify the specific user actions that predict long-term retention. These become the milestones the onboarding flow drives toward.
- Design the first experience — Apply the progressive onboarding framework to restructure the journey. Focus on the "first 30 seconds" experience and minimize steps before first value. Defer non-essential setup.
- Build the milestone-based onboarding plan — Create a "first mile" plan with clear milestones from signup through habit formation, with coordinated in-app and email touchpoints.
- Establish measurement and experiments — Set up tracking for each step in the funnel. Build an experiment backlog prioritized by impact, confidence, and effort. Design A/B tests for the highest-leverage changes.
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.
- 12d ago First seen · 138 lines · 50 tokens per session scan A ed18d9a7ba5e
onboarding-flow is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 5mo ago), licensed MIT. It adds 50 tokens to every session and 2,186 once invoked, about $0.0003 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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