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/ipythoning/b2b-sdr-agent-template/delivery-queuenpx skills add iPythoning/b2b-sdr-agent-template --skill delivery-queuegit clone --depth 1 https://github.com/iPythoning/b2b-sdr-agent-templateWhat 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 | $0.00000 | $0.00347 |
| Opus 5 | $0.00000 | $0.00173 |
| Sonnet 5 | $0.00000 | $0.00069 |
| Haiku 4.5 | $0.00000 | $0.00035 |
Grade A, and why
delivery-queue 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 yesterday.
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.
What it actually says
delivery-queue — Delayed Segmented Delivery
Schedule and deliver messages in timed segments to simulate human-like sending patterns.
Use Cases
- Break long product introductions into 3-5 digestible messages
- Schedule follow-ups at optimal times (e.g., prospect's local 9 AM)
- Drip campaigns: space out nurture messages over days
- Avoid WhatsApp spam detection by pacing outbound messages
Commands
deliver:schedule— Queue a message for future deliverydeliver:list— View pending deliveriesdeliver:cancel— Cancel a scheduled deliverydeliver:flush— Send all pending messages immediately
Configuration
default_delay_ms: 3000 # Delay between segments
max_segments: 10 # Max segments per delivery
timezone: "{{timezone}}" # Owner's timezone
quiet_hours: # Don't send during these hours
start: "22:00"
end: "07:00"
Usage Example
Schedule a 3-part product intro to +1234567890:
1. Company overview (send now)
2. Product highlights (send after 5 min)
3. Pricing inquiry (send after 15 min)
How It Works
- AI composes the full message sequence
- Skill splits into segments with timing
- Each segment is queued with a delivery timestamp
- Background worker sends at scheduled times
- Failed deliveries retry up to 3 times
File Structure
delivery-queue/
├── SKILL.md # This file
└── deliver.sh # Queue management script
What ships with it
1 file 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.
- yesterday First seen · 48 lines · 0 tokens per session scan A b36ae36902de
delivery-queue is a skill published in the GitHub repository iPythoning/b2b-sdr-agent-template (166 stars, last pushed 12d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 347 tokens. 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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