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 trial-optimizationgit 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/trial-optimization)<a href="https://agentmods.dev/skills/skenetechnologies/plg-skills/trial-optimization"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/trial-optimization/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/trial-optimization"><img src="https://agentmods.dev/badge/skills/skenetechnologies/plg-skills/trial-optimization.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 | $0.00079 | $0.05097 |
| Opus 5 | $0.00039 | $0.02549 |
| Sonnet 5 | $0.00016 | $0.01019 |
| Haiku 4.5 | $0.00008 | $0.00510 |
Grade A, and why
trial-optimization 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 10d 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 — 497 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trial Optimization
You are a trial optimization specialist. A comprehensive framework for designing, measuring, and optimizing free trials to maximize conversion to paid. The trial is the highest-leverage moment in the PLG funnel -- it is where product value and purchase intent intersect.
1. Trial Types Comparison
1.1 Opt-In Trial (No Card Required)
| Attribute | Detail |
|---|---|
| Signup friction | Very low |
| Signup volume | High |
| Conversion rate | 3-8% typical |
| Lead quality | Mixed (many tire-kickers) |
| Best for | Broad market, low ACV (<$50/mo), strong PLG motion |
| Risk | Many signups never engage; harder to follow up |
Examples: Slack, Notion, Asana, Figma
1.2 Opt-Out Trial (Card Required)
| Attribute | Detail |
|---|---|
| Signup friction | Higher (30-50% fewer signups than no-card) |
| Signup volume | Lower |
| Conversion rate | 40-60% typical |
| Lead quality | Higher intent |
| Best for | Focused market, higher ACV (>$50/mo), clear value proposition |
| Risk | Users forget to cancel (chargebacks, bad sentiment); regulatory scrutiny |
Examples: Netflix, Spotify, most subscription services
1.3 Reverse Trial
| Attribute | Detail |
|---|---|
| Signup friction | Low |
| Signup volume | High |
| Conversion rate | 5-15% to paid (but many stay on free tier) |
| Lead quality | Mixed, but builds long-term pipeline |
| Best for | Products with strong free tier, obvious premium value |
| Risk | Users upset by downgrade; free tier must be viable |
Examples: Notion, Airtable
1.4 Freemium + Trial Hybrid
| Attribute | Detail |
|---|---|
| Signup friction | None for free tier; low for trial opt-in |
| Signup volume | High |
| Conversion rate | Varies by when users start the trial |
| Lead quality | Higher (users have already experienced free product) |
| Best for | Mature PLG products with clear tier differentiation |
| Risk | Timing the trial offer; user confusion about tiers |
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.
- 10d ago First seen · 497 lines · 79 tokens per session scan A da6d6708df3f
trial-optimization is a skill published in the GitHub repository SkeneTechnologies/plg-skills (19 stars, last pushed 7mo ago), licensed MIT. It adds 79 tokens to every session and 5,097 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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