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 growthenginenowoslawski/coldoutboundskills --skill playbook-lookalikesgit clone --depth 1 https://github.com/growthenginenowoslawski/coldoutboundskillsWrote 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/growthenginenowoslawski/coldoutboundskills/playbook-lookalikes)<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-lookalikes"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-lookalikes/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/growthenginenowoslawski/coldoutboundskills/playbook-lookalikes"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-lookalikes.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 | $0.00134 | $0.03868 |
| Opus 5.5 | $0.00054 | $0.01547 |
| Sonnet 5.5 | $0.00027 | $0.00774 |
| Haiku 4.5 | $0.00013 | $0.00387 |
Grade A, and why
playbook-lookalikes 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 — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook: Case-Study Lookalikes (filter mining, not raw lookalike)
All rules here are best practice, not law. Override any of them when the campaign calls for it; note the best practice once and move on.
Use when: a client has a case study, a flagship logo, or one obviously happy customer, and you want the list of companies for whom that story is the strongest thing you could say.
Do not use when: you are building a whole market from an ICP sentence with no standout customer
to anchor on; or you want to name a customer off the prospect's own case-study page ("saw your
work with Intercom") — that is the opposite direction and belongs to playbook-case-study-page.
See also disco-like in this repo for plain seed-domain lookalike discovery. This playbook is the
higher-precision version: it mines why the story resonates before it searches.
One-line output: case_study_ref = "attentive" with
lookalike_case_study_line = "a marketing platform about your size".
⚠️ Merge-field collision warning
playbook-case-study-page pushes a lead-level field literally named case_study_line, whose value
completes a different sentence (Saw your work with Intercom.). This playbook's descriptor is
therefore named lookalike_case_study_line. Never rename it back. If both playbooks run on
the same campaign, a shared field name silently overwrites and renders
"We did this for your work with Intercom."
1. Trigger and scope
A case study only works on someone who recognises themselves in it. "We took an SMS marketing platform from 3 meetings a month to 22" lands hard on another marketing software company with a similar go-to-market, and lands on nobody else. So build the list backwards from the story, not forwards from the client's broadest ICP.
The naive move is to paste the case-study company into a lookalike engine and ship what comes back. That was measured at 40% usable: the vector matched on "is about marketing" and returned agencies, a marketing trade publication, and a Power BI blog alongside real software vendors.
What ships with it
2 files 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.
- 2mo ago First seen · 261 lines · 134 tokens per session scan A 865f56f51b4d
playbook-lookalikes is a skill published in the GitHub repository growthenginenowoslawski/coldoutboundskills (802 stars, last pushed 1mo ago), licensed MIT. It adds 134 tokens to every session and 3,868 once invoked, about $0.0005 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 2026-08-30.
playbook-lookalikes compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| playbook-lookalikesgrowthenginenowoslawski/coldoutboundskills | Skill | 802 | 1mo ago | MIT | A | 134 | 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 | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |