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 forsvn-labs/meta-skills --skill improve-conversiongit clone --depth 1 https://github.com/forsvn-labs/meta-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/forsvn-labs/meta-skills/improve-conversion)<a href="https://agentmods.dev/skills/forsvn-labs/meta-skills/improve-conversion"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/improve-conversion/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/forsvn-labs/meta-skills/improve-conversion"><img src="https://agentmods.dev/badge/skills/forsvn-labs/meta-skills/improve-conversion.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 | $0.00067 | $0.00525 |
| Opus 5 | $0.00034 | $0.00262 |
| Sonnet 5 | $0.00013 | $0.00105 |
| Haiku 4.5 | $0.00007 | $0.00052 |
Grade A, and why
improve-conversion 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve conversion performance
Return a diagnosis, revised work, and one discriminating test—not a decorative scorecard.
Establish the intended path
Identify:
- intended audience and costly moment;
- promise, mechanism, proof, and objection;
- acquisition source and message expectation;
- primary action and downstream value;
- available exposure, behavior, conversion, revenue, retention, and qualitative evidence.
Separate missing data from zero. Treat vanity engagement as diagnostic, not business performance.
Locate the first meaningful break
- Reach: the right people did not encounter it.
- Attention: they encountered it but did not stop.
- Comprehension: they stopped but did not understand.
- Belief: they understood but did not trust the promise.
- Motivation: they believed it but did not care enough now.
- Friction: they wanted it but the next step was costly or broken.
- Value: they acted but the product or offer did not deliver.
Inspect audience, channel, device, geography, and customer mix before aggregating. Treat co-timed movement as correlation until evidence distinguishes causes. State the mechanism connecting each proposed cause to the observed behavior.
Prioritize a discriminating test
Name plausible alternative explanations. Choose the smallest change that produces different predictions for the leading explanations.
Define:
- hypothesis and causal mechanism;
- one intentional change;
- target segment and surface;
- primary outcome and guardrails;
- observation window;
- keep, revise, or kill rule.
Accept “inconclusive” when evidence cannot discriminate.
Deliver
Lead with:
- Keep: what evidence supports retaining.
- Drop: what is weak, harmful, or unnecessary.
- Change: the revised message, structure, or experience.
- Test: the highest-value uncertainty and controlled experiment.
Include the ready-to-use revision whenever the medium permits. Tie every recommendation to observed evidence, an explicit inference, or a labeled assumption. Do not claim causality or conversion lift without evidence.
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.
- 12d ago First seen · 70 lines · 67 tokens per session scan A 72017d6780bf
improve-conversion is a skill published in the GitHub repository forsvn-labs/meta-skills (14 stars, last pushed 1mo ago), licensed MIT. It adds 67 tokens to every session and 525 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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