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 jpeslar1/john-peslar-ai-skills --skill kara-cold-outboundgit clone --depth 1 https://github.com/jpeslar1/john-peslar-ai-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/jpeslar1/john-peslar-ai-skills/kara-cold-outbound)<a href="https://agentmods.dev/skills/jpeslar1/john-peslar-ai-skills/kara-cold-outbound"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/kara-cold-outbound/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/jpeslar1/john-peslar-ai-skills/kara-cold-outbound"><img src="https://agentmods.dev/badge/skills/jpeslar1/john-peslar-ai-skills/kara-cold-outbound.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 | $0.00097 | $0.02589 |
| Opus 5.5 | $0.00039 | $0.01036 |
| Sonnet 5.5 | $0.00019 | $0.00518 |
| Haiku 4.5 | $0.00010 | $0.00259 |
Grade A, and why
kara-cold-outbound 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 2mo 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kara Cold Outbound
Write and fix LinkedIn cold outbound that gets replies. Core doctrine comes from Kara McMaster (https://www.karamcmaster.com/) and her own description of her playbook; gaps (exact message-by-message structure, timing, objection handling) are filled from Kellen Casebeer / GTM Cafe teachings and high-engagement practitioner sources. Every rule below is tagged with its origin: [Kara] = from Kara directly, [Casebeer] = GTM Cafe / Kellen Casebeer, [Research] = consensus across practitioner videos/threads. When Kara and research conflict, Kara wins.
The Five Laws
- Optimize for the reply, not the call. [Kara] This is where everyone goes wrong. The first touches exist to start a conversation. The ask for time comes only after engagement.
- Don't be a dick. Personalize. Show effort. Bring personality. [Kara] Her entire messaging framework in one line. A message that could be sent unchanged to 1,000 people is not personalized [Research].
- Never pitch in the connection request. [Kara][Casebeer][Research - strongest consensus] The request's only job is acceptance. Any pitch smell kills it.
- Signals are for targeting, not for messaging. [Casebeer] Commodity triggers (hiring, funding, job change) tell you WHO to message; the message itself should talk about the underlying problem, not congratulate them on the trigger. Kara goes further and calls most signal-based copy marketing BS [Kara].
- No trust, no offer. [Kara] Track what they open and watch. If they haven't engaged with anything, don't make an offer. If someone shows up to a call asking "who are you?", end it.
The Sequence
Step 0: Connection request - no note [Kara]
Send blank. Kara's rule, and it matches Casebeer's default ("a connection request is not a pitch... treat acceptance as access, not conversion") and Reddit A/B data (26% blank vs 13% with a salesy note).
Exception [Casebeer][Research]: a note is fine ONLY if it is genuinely non-salesy and segment-specific (mutual group, mutual connections, shared space). Test blank vs note per audience; never pitch in the note either way. If you can't write a note that couldn't go to 1,000 other people, go blank.
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.
- 2mo ago First seen · 110 lines · 97 tokens per session scan A 1885a114ec63
kara-cold-outbound is a skill published in the GitHub repository jpeslar1/john-peslar-ai-skills (12 stars, last pushed 18d ago), licensed MIT. It adds 97 tokens to every session and 2,589 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-31.
kara-cold-outbound compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| kara-cold-outboundjpeslar1/john-peslar-ai-skills | Skill | 12 | 18d ago | MIT | A | 97 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |