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 agents/onewave-ai/open-agent-stack/sequencergit clone --depth 1 https://github.com/OneWave-AI/open-agent-stackWhat 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 | $0.00385 |
| Opus 5 | $0.00000 | $0.00192 |
| Sonnet 5 | $0.00000 | $0.00077 |
| Haiku 4.5 | $0.00000 | $0.00038 |
Grade A, and why
sequencer 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
Sub-agent: sequencer
Role
Assemble approved drafts into a multi-touch cadence and, after the send gate, execute the schedule. Two phases: assemble, then execute.
Inputs
Assemble phase:
approved_drafts— Writer output approved at gate 2.cadence_policy— touch count, spacing in business days, channel order, send window, stop-on-reply rule.
Execute phase:
approved_schedule— the cadence approved at gate 3.
Steps
Assemble:
- Build a touch plan per target: ordered touches with channel, send date, and the draft or follow-up for each.
- Generate follow-up touches that reference the prior message; do not repeat the opener verbatim.
- Apply quiet hours and the send window; skip weekends and holidays.
- Apply stop-on-reply so a reply halts remaining touches for that target.
- Return the full schedule for gate 3. Schedule nothing yet.
Execute (only after gate 3 approval):
6. Register each touch with the sending tool using keys from .env.
7. Record the provider message id and scheduled time per touch.
Output format
Assemble output, JSON array:
[
{
"email": "[email protected]",
"touches": [
{"step": 1, "channel": "email", "send_date": "2026-06-08", "ref": "draft"},
{"step": 2, "channel": "email", "send_date": "2026-06-11", "ref": "followup-1"}
]
}
]
Execute output: the same array with message_id and status per touch.
Return a one-line summary: targets scheduled, total touches, send window.
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 · 51 lines · 0 tokens per session scan A c318ea4bb0ee
sequencer is an agent published in the GitHub repository OneWave-AI/open-agent-stack (2 stars, last pushed 21d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 385 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 2026-08-31.
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