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 runxhq/runx --skill cold-outreachgit clone --depth 1 https://github.com/runxhq/runxWrote 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/runxhq/runx/cold-outreach)<a href="https://agentmods.dev/skills/runxhq/runx/cold-outreach"><img src="https://agentmods.dev/badge/skills/runxhq/runx/cold-outreach/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/runxhq/runx/cold-outreach"><img src="https://agentmods.dev/badge/skills/runxhq/runx/cold-outreach.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 | $0.00037 | $0.00988 |
| Opus 5.5 | $0.00015 | $0.00395 |
| Sonnet 5.5 | $0.00007 | $0.00198 |
| Haiku 4.5 | $0.00004 | $0.00099 |
Grade A, and why
cold-outreach 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 5d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cold outreach
Prepare one useful first contact, not a campaign or an email transport. Reuse
supplied prospect research, approved offer facts, brand-voice and taste-profile
context. The calling application selects that workspace context; this skill never
searches global memory or a private credential database.
prepare admits the evidence and writing context through ghostwrite#admit and
checks the supplied contact-policy disposition and suppression state. Only then
does it take one writing turn of its own, which ghostwrite#verify binds to the
admitted evidence before review. A matched
lead, public address or good commercial fit does not establish contact permission.
Unknown eligibility, suppression, missing policy evidence or an unsupported draft
produces a hold. No approval, delivery or provider acknowledgement is implied.
Composes
ghostwrite#admitghostwrite#verify
Outreach judgment
Find a specific, public or disclosure-approved reason this offer relates to the recipient's work. Prefer an observed change, relevant problem or request to a generic compliment. If the same rationale fits every company, narrow it or hold for better evidence. An uncertain need is a question or hypothesis, not a claim about their internal problems, budget, tools or authority.
Offer a concrete useful result or small next step. Use current supported proof; voice and taste cannot approve factual claims. Start around 60–120 words in short paragraphs, excluding the supplied identity and opt-out footer. Adapt that length to the offer. Lead with relevance, explain value plainly and make one simple ask.
Use a truthful subject and plain text. No fake Re: or Fwd:, invented familiarity,
metrics, urgency, exaggerated praise, tracking tricks or private personal details.
Keep internal citations and reasoning in the review artifact. The exact footer
must appear unchanged in the body. If the evidence does not support a worthwhile
angle, return needs_more_evidence instead of producing generic sales copy.
What ships with it
11 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.
- cold-outreach.mjs 2.6 KB runs code
- cold-outreach.test.mjs 4.1 KB runs code
- fixtures/composes-in-graph.yaml 178 B
- fixtures/holds-out-of-scope-context.yaml 3.3 KB
- fixtures/holds-suppressed-recipient.yaml 2.7 KB
- fixtures/prepares-approved-context-email.yaml 7.2 KB
- fixtures/support/composed.graph 3.1 KB
- fixtures/withholds-missing-footer.yaml 3.1 KB
- fixtures/withholds-unsupported-claim.yaml 3.3 KB
- references/catalog.md 176 B
- run.yaml 12 KB
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.
- 5d ago First seen · 88 lines · 37 tokens per session scan A e0503b6b58d0
cold-outreach is a skill published in the GitHub repository runxhq/runx (103 stars, last pushed today), licensed Apache-2.0. It adds 37 tokens to every session and 988 once invoked, about $0.0001 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 2026-10-12.
Other skills, from other repositories
dd-synthesis
Synthesize findings from multiple specialist agents into a coherent investment recommendation. Use when combining reports from 9+ specialist agents into a single decision document.
dd-report-format
Standard output format for due diligence analysis reports. Use when producing your final analysis to ensure consistency across all specialist agents.
dd-risk-scoring
Calculate weighted risk scores from specialist agent reports. Use when synthesizing multiple due diligence reports into an overall investment risk assessment.
dd-sourcing
Document sources properly for due diligence reports. Use when citing any external data, ensuring full traceability and auditability of findings.
dd-verification
Verify claims in pitch decks and founder statements using multiple independent sources. Use when analyzing any claim about market size, traction, team background, or competitive positioning.
codew-release-qa-sweep
Use before claiming Codewhale release work is done: run the full gate sweep and list the manual QA targets.