outreach-paced

outreach-paced is a skill for Claude Code, Codex from dailyaiagents-cpu/dailyai-os. It costs 76 tokens per session (5,417 once invoked), scanned A, original, MIT.

A guided routine for sending a limited number of daily outreach messages on LinkedIn, Reddit, and email. It drafts personalized messages, checks them against recent messages, and routes each send through an approval step.

In plain words
What is it for?
Use it to prepare and approve weekday outreach: LinkedIn direct messages, useful Reddit comments and messages, and personalized cold emails.
Why use it?
It reduces the work of finding prospects, writing messages, and controlling daily volume while keeping a person responsible for approving sends.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: positional $N argument; built for openclaw.

Good fit Use it to prepare and approve weekday outreach: LinkedIn direct messages, useful Reddit comments and messages, and personalized cold emails.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dailyaiagents-cpu/dailyai-os/outreach-paced
Install

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.

Any agent
npx skills add dailyaiagents-cpu/dailyai-os --skill outreach-paced
Clone the repo
git clone --depth 1 https://github.com/dailyaiagents-cpu/dailyai-os

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for outreach-paced

README.md
[![agentmods](https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/outreach-paced/github.svg)](https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/outreach-paced)
Your own site
<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.

agentmods 80×15 button for outreach-paced

Your own site · 80×15
<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>
Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,417 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00076 $0.05417
Opus 5.5 $0.00030 $0.02167
Sonnet 5.5 $0.00015 $0.01083
Haiku 4.5 $0.00008 $0.00542

Measured 2mo ago against content hash 09954b44917d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (test.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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(
skills/outreach-paced/SKILL.md · 470 lines

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
linkedin view-first DM 5
reddit substantive comment 3
reddit DM 1
email 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)}")

Read the full file on GitHub · 470 lines

Files

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.

Changes

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.

  1. 2mo ago First seen · 470 lines · 76 tokens per session scan A 09954b44917d

Subscribe to this mod's changes

outreach-paced is a skill published in the GitHub repository dailyaiagents-cpu/dailyai-os (0 stars, last pushed 6mo ago), licensed MIT. It adds 76 tokens to every session and 5,417 once invoked, about $0.0003 per session on Opus 5.5. 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.

Related

outreach-paced compared with skills from other repositories

outreach-paced next to 6 skills from other repositories
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