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 kalyvask/winning-writing --skill sent-mail-outcome-trackergit clone --depth 1 https://github.com/kalyvask/winning-writingWrote 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/kalyvask/winning-writing/sent-mail-outcome-tracker)<a href="https://agentmods.dev/skills/kalyvask/winning-writing/sent-mail-outcome-tracker"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/sent-mail-outcome-tracker/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/kalyvask/winning-writing/sent-mail-outcome-tracker"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/sent-mail-outcome-tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00093 | $0.03058 |
| Opus 5 | $0.00046 | $0.01529 |
| Sonnet 5 | $0.00019 | $0.00612 |
| Haiku 4.5 | $0.00009 | $0.00306 |
Grade A, and why
sent-mail-outcome-tracker 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sent-mail outcome tracker
The rest of the toolkit asks "is this email well-written?" This skill asks the only question that actually matters: "did the email get a reply, and what did the messages that replied have in common?"
Composes with voice-update --source sent-mail (which audits voice files against sent mail). This skill produces outcome data; voice-update proposes voice updates if patterns emerge.
Why this exists
Two gaps the existing toolkit does not close:
- The critic measures rule recall, not response rate. The eval harness asserts the inline critic catches em-dashes and vague asks. None of that proves the resulting emails get replies. Reply rate is the only metric the recipient cares about, and it is the only one that compounds.
- You learn from outcomes, not from the draft. A draft that passes every rule and gets ignored teaches you something the rules cannot. A draft that breaks a rule and gets a same-day intro teaches you something the rules will never catch. Both signals live in the sent folder and the inbox, not in the rule library.
Auto-tracking every email is out of scope. The user invokes this on demand, the skill reads, the skill reports. The user judges what to change.
Inputs
Optional arguments:
- Time window: default
last 30 days. Accept "last week," "last 3 months," "since [date]." - Count cap: default
25 messages. Cap at 50 to keep token cost bounded. - Mode filter: default
cold outreach only. The skill applies heuristics to exclude:- Replies (any message with
In-Reply-Toheader or whose thread already has an inbound message from the same recipient before the sent date) - Automated mail (auto-replies, calendar invites, no-reply addresses)
- One-liners under 30 words
- Messages to recipients already in the user's recent inbound mail (these are warm threads, not cold outreach)
- Replies (any message with
- Ask-type tag: optional. "Only meeting requests / intro requests / job inquiries / advice asks." Different ask types have different baseline reply rates and should not be averaged together.
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 · 194 lines · 93 tokens per session scan A dc40c7e4d78c
sent-mail-outcome-tracker is a skill published in the GitHub repository kalyvask/winning-writing (13 stars, last pushed 5d ago), licensed MIT. It adds 93 tokens to every session and 3,058 once invoked, about $0.0005 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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