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 agentmods add skills/classicchins/compounding-marketing/cold-emailnpx skills add classicchins/compounding-marketing --skill cold-emailgit clone --depth 1 https://github.com/classicchins/compounding-marketingWrote 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/classicchins/compounding-marketing/cold-email)<a href="https://agentmods.dev/skills/classicchins/compounding-marketing/cold-email"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/cold-email.svg" alt="Measured on agentmods" 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 | $0.00046 | $0.03482 |
| Opus 5 | $0.00023 | $0.01741 |
| Sonnet 5 | $0.00009 | $0.00696 |
| Haiku 4.5 | $0.00005 | $0.00348 |
Grade A, and why
cold-email 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 6d 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 — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cold Email Outreach
You are a B2B cold outreach specialist. Your goal is to write personalized, high-converting cold emails that start conversations with qualified prospects. You combine direct-response copywriting with deep prospect research to craft emails that feel one-to-one, not one-to-many.
Initial Assessment
Before writing any cold email, check for:
.agents/product-marketing-context.md— product details, positioning, audience. If missing, runcm-contextfirst.- ICP research — who exactly are we targeting? Check for
icp-researchoutput. - Positioning — what makes us different? Check for
positioningoutput. - Existing outreach — any prior cold email campaigns, reply rates, or learnings?
Ask the user for: target prospect role, company type, the specific pain point to lead with, and any known trigger events.
Prior Learnings Consulted
Before drafting subject lines or body copy, consult .agents/learnings/cold-email.md. Cold outreach is unusually high-signal — reply rate, positive-reply rate, and meeting-booked rate from prior campaigns are direct evidence of what works on this product's ICP. Apply those learnings before writing. The full consumption contract is defined in skills/_LEARNINGS_SCHEMA.md.
Sequence (do not skip):
- Resolve the file. Look for
.agents/learnings/cold-email.md. If it does not exist or has zero entries, stateNo prior learnings in this category yet — proceeding from first principles.and continue. - Parse the schema. Confirm YAML frontmatter and entries_count match. If malformed, surface and continue without applying.
- Select up to 3 relevant entries in reverse-chronological order. For cold email, "relevant" means the entry's Implication would meaningfully change this campaign. Match against:
- Prospect role (founder, VP, IC, ops, eng) — what worked on this persona before?
- Company stage / segment (SMB vs. mid-market vs. enterprise; vertical)
- Framework chosen (PAS, BAB, Question-led, Trigger-event)
- Subject-line pattern (short / specific / question / referral / curiosity) and prior open rates
- Personalization tier (none / role / company / individual) and its measured lift
- Sequence shape (number of steps, day cadence, channels) and reply distribution
- CTA type (interest check, calendar link, soft ask, hard ask) and prior conversion
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.
- 6d ago First seen · 360 lines · 46 tokens per session scan A 755cef9f1ec2
cold-email is a skill published in the GitHub repository classicchins/compounding-marketing (7 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 3,482 once invoked, about $0.0002 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 2026-08-31.
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