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 deciqAI/knowledge-skills --skill activation-onboarding-playbookgit clone --depth 1 https://github.com/deciqAI/knowledge-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/deciqai/knowledge-skills/activation-onboarding-playbook)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/activation-onboarding-playbook"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/activation-onboarding-playbook/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/deciqai/knowledge-skills/activation-onboarding-playbook"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/activation-onboarding-playbook.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 | $0.00080 | $0.00632 |
| Opus 5 | $0.00040 | $0.00316 |
| Sonnet 5 | $0.00016 | $0.00126 |
| Haiku 4.5 | $0.00008 | $0.00063 |
Grade A, and why
activation-onboarding-playbook 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 11d 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 — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Activation & Onboarding — Get to First Value Fast
Overview
Activation is the moment a new user first experiences real value — the "aha moment" — and the single biggest lever on retention and revenue for most products/services. Onboarding's job is to shrink time-to-value: get the user to that moment as fast as possible, removing every step that isn't required to feel the win.
The Process
- Define the aha moment — the specific action correlated with retention (e.g. "invited a teammate," "sent first invoice," "got first result"). Gate: no defined activation event = you're optimizing blind.
- Find the setup moment(s) — the minimum steps required to reach aha; everything else is optional.
- Design the shortest path — sequence only the must-do steps to first value; defer configuration.
- Remove/relieve friction — pre-fill, sensible defaults, do-it-for-them (concierge/agent) where possible.
- Nudge to the milestone — timely prompts/emails toward the aha, not generic "welcome" noise (pairs with an onboarding-sequencer).
- Measure activation rate and time-to-value; iterate the biggest drop-off. Gate: adding onboarding steps without measuring drop-off usually lowers activation.
When to Use
- High signup, low activation/stick
- New onboarding flow or high-touch service onboarding
- Trials that don't convert to paid
Applying It Well
- Deliver a win before asking for full setup.
- One activation metric; instrument the funnel to it.
- Doing the setup for the user (concierge/AI) often beats teaching them.
Red Flags
- Long setup wizard before any value.
- Generic welcome emails not tied to the aha.
- No activation metric or funnel instrumentation.
Verification
- Aha moment defined and measurable
- Minimum path to first value mapped
- Friction removed / setup assisted
- Activation rate + time-to-value tracked, drop-offs iterated
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/activation-onboarding-playbook · Built by deciqAI · github.com/deciqAI · Contributions welcome.
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.
- 11d ago First seen · 44 lines · 80 tokens per session scan A 2961a25274de
activation-onboarding-playbook is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 9d ago), licensed MIT. It adds 80 tokens to every session and 632 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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