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 warm-intro-findergit 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/warm-intro-finder)<a href="https://agentmods.dev/skills/kalyvask/winning-writing/warm-intro-finder"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/warm-intro-finder/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/warm-intro-finder"><img src="https://agentmods.dev/badge/skills/kalyvask/winning-writing/warm-intro-finder.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 | $0.00133 | $0.02468 |
| Opus 5 | $0.00067 | $0.01234 |
| Sonnet 5 | $0.00027 | $0.00494 |
| Haiku 4.5 | $0.00013 | $0.00247 |
Grade A, and why
warm-intro-finder 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Warm-intro finder
Source: points/cold-email-rules.md rule 4 ("Find a mutual contact and name them") + the AB test result that the same email sent to [email protected] got zero response while sent direct via warm intro got a reply at 8:25 AM the next morning.
The premise
A warm intro from someone the recipient trusts roughly doubles open rate and triples response rate compared to the same email sent cold. Most cold emails fail not because they're bad, but because the writer never checked their network.
This skill exists because the check is mechanical and almost everyone skips it.
What "warm" actually means
Not all mutual contacts are useful. The bar is:
- The connector has engaged with both parties recently (within the last 12 months) — a forgotten LinkedIn link from 2014 doesn't count
- The connector has positive standing with the recipient — a fired ex-employee is worse than a stranger
- The connector is willing to spend social capital — being a 1st-degree connection is necessary but not sufficient
Rank candidates by these three filters, not just by connection-degree.
What to search
Cross-reference the recipient (from recipient-research) against the user's profile (from context/about-me.md) along nine bridge categories. Highest leverage at top.
1. Recent direct collaboration (1st degree)
Anyone the user has shipped a project with or worked closely alongside in the last 24 months who also has a current connection to the target.
2. Investor / board overlap
If the target is a founder or exec, who funds them? Who sits on their board? The user's network of investors and advisors is the highest-leverage bridge to founders.
3. Same-cohort alumni (operative cohort)
Not just "we both went to Stanford" — same year, same program, same section. "Section H 2026" opens doors that "Stanford alum" doesn't.
4. Ex-colleagues at the recipient's current company
People who have left the recipient's company in the last 24 months and remain on good terms. They know the culture and can frame the email correctly.
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 · 184 lines · 133 tokens per session scan A 1ea1f953cf07
warm-intro-finder is a skill published in the GitHub repository kalyvask/winning-writing (13 stars, last pushed 5d ago), licensed MIT. It adds 133 tokens to every session and 2,468 once invoked, about $0.0007 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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