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 matteotitta/genesys-skills --skill reply-scoringgit clone --depth 1 https://github.com/matteotitta/genesys-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/matteotitta/genesys-skills/reply-scoring)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/reply-scoring"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/reply-scoring/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/matteotitta/genesys-skills/reply-scoring"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/reply-scoring.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 | $0.00218 | $0.02256 |
| Opus 5 | $0.00109 | $0.01128 |
| Sonnet 5 | $0.00044 | $0.00451 |
| Haiku 4.5 | $0.00022 | $0.00226 |
Grade A, and why
reply-scoring 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 9d 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reply scoring
Reply rate tells you if people are paying attention. Positive reply rate tells you if they want what you're selling. This skill computes the second by classifying every reply into one of 11 buckets, then surfaces the high-positive replies that need a human — ranked most-recent first, because this runs as a batch and the freshest reply is the one still worth answering.
This is a measurement loop, not a speed-to-lead alert. Speed-to-lead is real (the sales-floor rule of thumb is minutes, not days), but nothing here delivers it: the skill fires at the 14-day mark on a Gmail pull, so a reply from day 2 is already twelve days cold when it's scored. Don't read the action list as an SLA. Honouring speed-to-lead needs an arrival trigger this skill doesn't have — the Gmail MCP is pull-only, so the pragmatic version is a scheduled poll, not a webhook. Deferred: no live campaign is feeling this today. See .claude/discovery/0726-agentmail-steal-analysis.md (A2).
Claude Code triggers
Invoke when user says:
- "Score my replies"
- "Positive reply rate"
- "How is [campaign name] doing"
- "Classify these replies"
- "What did our last campaign actually produce"
- "Pull replies from Gmail and bucket them"
Do NOT invoke when:
- User wants pre-send list QA →
/list-quality - User wants per-account fit before sending →
/lead-scoring - User wants the email content itself →
/outreach - User wants a content-strategy reply analysis (e.g., LinkedIn comments) →
/transcriptsor a content skill
Auto-suggest after: any campaign hits its 14-day mark (sample large enough to read), or user mentions "how did the campaign go" / "are we getting good replies."
Input requirements
Required
| Input | Description | Source |
|---|---|---|
| Campaign identifier | Gmail thread query, label, sequence ID, or date range that scopes which replies to pull | User provides |
| Total sent | The denominator for positive_reply_rate. Pulled from outreach skill's send log, Apollo sequence, or user-provided | User or upstream skill |
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.
- 9d ago First seen · 153 lines · 216 tokens per session scan A 971711479c14
reply-scoring is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 218 tokens to every session and 2,256 once invoked, about $0.0011 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 2026-09-03.
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