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-fundraisinggit 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-fundraising)<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/playbook-fundraising"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-fundraising/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-fundraising"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/playbook-fundraising.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 245 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00098 | $0.04643 |
| Opus 5 | $0.00049 | $0.02322 |
| Sonnet 5 | $0.00020 | $0.00929 |
| Haiku 4.5 | $0.00010 | $0.00464 |
Grade A, and why
playbook-fundraising 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 12d 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 — 269 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playbook: Fundraising
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: the angle depends on the prospect having money to spend or a mandate to grow — either a TAM filtered to funded companies, or a first line that names their round.
Do not use when: you want headcount growth or open roles as the signal (playbook-hiring-surge).
If you want the round as a pure list filter and no copy at all, you only need lane A in §3.
One-line output: funding_line = "Saw you raised $52M in the Series B." — the whole sentence
lives in the variable. §2's downstream gate explains why that matters more than it looks.
1. Trigger and scope
Three separate questions get called "fundraising", and they route to different sources. Do not mix them up: one is a solved free problem and one is not solved at all.
- Lane A, ever raised. "Only show me companies with venture backing." A search filter applied when the list is built. Free, reliable, no per-row work.
- Lane B, the copy line. "Write a first line about their round." A per-row lookup producing
funding_line. This is the main deliverable and the tested path. - Lane C, raised in the last 30 days. "Who closed this month?" The people-database funding indexes lag 3 to 4 months, measured. Lane C has no proven end-to-end path and is documented honestly below rather than faked.
Not covered: valuation claims, investor-name personalization, and anything reported only as a rumor.
2. Output contract
Inputs required per row
| Field | Type | Required? |
|---|---|---|
domain (bare, lowercase, no www, no scheme) |
string | yes |
company_name |
string | no, used only for the abstain log and QA |
Output fields
| Field | Type | Example | Max | Null? |
|---|---|---|---|---|
funding_line |
string | Saw you raised $52M in the Series B. |
90 | no, use the abstain value |
funding_clause |
string | you raised $52M in the Series B |
80 | no, use "" |
funding_evidence_url |
string | a funding-round URL | 300 | no, use "" |
funding_confidence |
enum | high / low |
4 | no |
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.
- 12d ago First seen · 269 lines · 98 tokens per session scan A 3d6fe7cf5f8b
playbook-fundraising is a skill published in the GitHub repository growthenginenowoslawski/coldoutboundskills (702 stars, last pushed 25d ago), licensed MIT. It adds 98 tokens to every session and 4,643 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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