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 dailyaiagents-cpu/dailyai-os --skill outreach-pacedgit clone --depth 1 https://github.com/dailyaiagents-cpu/dailyai-osWrote 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/dailyaiagents-cpu/dailyai-os/outreach-paced)<a href="https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/outreach-paced"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/outreach-paced/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/dailyaiagents-cpu/dailyai-os/outreach-paced"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/outreach-paced.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 | $0.00076 | $0.05417 |
| Opus 5 | $0.00038 | $0.02708 |
| Sonnet 5 | $0.00015 | $0.01083 |
| Haiku 4.5 | $0.00008 | $0.00542 |
Grade A, and why
outreach-paced scanned grade A with 1 finding 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 11d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run( How it starts
The opening of the file, as written. The whole thing — 470 lines — stays where its author put it; the contents beside it link to each section on GitHub.
outreach-paced
Why this exists
Cal.com is wired but has 0 inbound. Content-publish-daily builds audience slowly. Outreach drives qualified prospects directly to a 30-min audit booking THIS WEEK. This skill is the lead-acquisition surface.
Inputs
{
"for_date": "2026-04-29",
"surfaces": ["linkedin", "reddit", "email"],
"dry_run": false
}
dry_run=true runs Steps 1-4 (drafts, anti-template, queue) but skips approval-gate creation and Telegram send.
Daily quotas (hard-capped)
| Surface | Action | Max |
|---|---|---|
| view-first DM | 5 | |
| substantive comment | 3 | |
| DM | 1 | |
| personalized cold email | 5 | |
| TOTAL | 14 |
Procedure
Step 1 — Pull qualified prospects
import json, datetime, pathlib, hashlib
home = pathlib.Path.home() / "Dev/daily-ai-agent-os"
now = datetime.datetime.now(datetime.timezone.utc)
date_str = (now - datetime.timedelta(hours=5)).strftime("%Y-%m-%d")
# Reddit prospects from precise_leads.json (research agent populates)
reddit_leads_path = home / "data/leads/precise_leads.json"
reddit_leads = json.loads(reddit_leads_path.read_text()) if reddit_leads_path.exists() else []
# LinkedIn + email prospects from active.json (research agent populates this format)
active_path = home / "data/leads/active.json"
active = json.loads(active_path.read_text()) if active_path.exists() else []
linkedin_leads = [l for l in active if l.get("surface") == "linkedin"]
email_leads = [l for l in active if l.get("surface") == "email"]
# Idempotency: skip prospects already messaged in last 14 days
idx_path = home / "data/outreach/log/index.json"
recent = {}
if idx_path.exists():
recent = json.loads(idx_path.read_text())
cutoff = (now - datetime.timedelta(days=14)).isoformat()
def already_messaged(prospect_id):
return recent.get(prospect_id, "") > cutoff
reddit_leads = [l for l in reddit_leads if not already_messaged(l.get("permalink", ""))][:4] # 3 comment + 1 DM
linkedin_leads = [l for l in linkedin_leads if not already_messaged(l.get("profile_url", ""))][:5]
email_leads = [l for l in email_leads if not already_messaged(l.get("email", ""))][:5]
print(f"[outreach-paced] candidates: linkedin={len(linkedin_leads)} reddit={len(reddit_leads)} email={len(email_leads)}")
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.
- 11d ago First seen · 470 lines · 76 tokens per session scan A 09954b44917d
outreach-paced is a skill published in the GitHub repository dailyaiagents-cpu/dailyai-os (0 stars, last pushed 4mo ago), licensed MIT. It adds 76 tokens to every session and 5,417 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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