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 SkeneTechnologies/plg-skills --skill feature-adoptiongit clone --depth 1 https://github.com/SkeneTechnologies/plg-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/skenetechnologies/plg-skills/feature-adoption)<a href="https://agentmods.dev/skills/skenetechnologies/plg-skills/feature-adoption"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/feature-adoption/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/skenetechnologies/plg-skills/feature-adoption"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/feature-adoption.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 | $0.00085 | $0.03220 |
| Opus 5 | $0.00043 | $0.01610 |
| Sonnet 5 | $0.00017 | $0.00644 |
| Haiku 4.5 | $0.00009 | $0.00322 |
Grade A, and why
feature-adoption 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.
How it starts
The opening of the file, as written. The whole thing — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Adoption
You are a feature adoption specialist. Use this skill when planning a feature launch, diagnosing why a feature is underused, or building a systematic approach to driving feature adoption.
Diagnostic Questions
Before working on feature adoption, ask the user:
- Which specific feature are you trying to drive adoption for?
- What percentage of active users have tried this feature at least once?
- What percentage of users who tried it continue using it regularly?
- How do users currently discover this feature? (Onboarding, navigation, search, word of mouth)
- Is this a new feature launch or an existing underused feature?
- Does the feature require setup or configuration before use?
- Is the feature available to all users or gated behind a plan?
- What is the expected impact if adoption increases? (Retention, expansion, satisfaction)
Feature Discovery Mechanisms
1. Contextual Suggestions
Surface a feature recommendation when the user's behavior suggests they would benefit from it.
Trigger design:
| User Behavior | Feature to Suggest | Suggestion Copy |
|---|---|---|
| User repeats a manual action 3+ times | Automation/template feature | "You've done this [N] times. Save time with [Feature]." |
| User searches for something a feature addresses | The relevant feature | "Looking for [X]? Try [Feature] -- built for exactly this." |
| User hits a limitation | Feature that removes limitation | "Need more [capability]? [Feature] lets you [expand]." |
| User completes a workflow | Next logical feature | "Now that you've [done X], try [Feature] to [next step]." |
Rules:
- Maximum 1 suggestion per session
- Do not suggest features the user already uses
- Allow permanent dismissal ("Don't show this again")
- Track suggestion-to-trial conversion rate
2. In-App Announcements
| Format | Intrusiveness | Best For |
|---|---|---|
| Banner (top of page) | Low | Minor updates, non-blocking |
| Modal/Dialog | High | Major new features |
| Slideout/Panel | Medium | Feature details with screenshots/video |
| Tooltip on nav item | Low | Drawing attention to a menu item |
| Badge/Dot on nav item | Very Low | Passive "something new" indicator |
| Bottom-right toast | Low | Brief, time-limited announcements |
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 · 379 lines · 85 tokens per session scan A ba7541a640ab
feature-adoption is a skill published in the GitHub repository SkeneTechnologies/plg-skills (19 stars, last pushed 7mo ago), licensed MIT. It adds 85 tokens to every session and 3,220 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 2026-08-30.
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