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 toflow-ai/toflow-mcp --skill toflow-create-sequencegit clone --depth 1 https://github.com/toflow-ai/toflow-mcpWrote 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/toflow-ai/toflow-mcp/toflow-create-sequence)<a href="https://agentmods.dev/skills/toflow-ai/toflow-mcp/toflow-create-sequence"><img src="https://agentmods.dev/badge/skills/toflow-ai/toflow-mcp/toflow-create-sequence/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/toflow-ai/toflow-mcp/toflow-create-sequence"><img src="https://agentmods.dev/badge/skills/toflow-ai/toflow-mcp/toflow-create-sequence.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 | $0.00065 | $0.01407 |
| Opus 5.5 | $0.00026 | $0.00563 |
| Sonnet 5.5 | $0.00013 | $0.00281 |
| Haiku 4.5 | $0.00006 | $0.00141 |
Grade A, and why
toflow-create-sequence 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 4d 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
toflow — Create and Enroll a Sequence
This skill orchestrates toflow's sequence tools into one workflow: build the sequence, get it approved, then enroll people with correctly personalized (or AI-generated) content. The guide tools below are the live source of truth for schema, required fields, and node/edge structure — always call them before the matching mutating call, even if this skill summarizes the shape. This skill's own guidance covers what the guides don't own: message content quality and writing style, and the order to call things in.
Part 1 — Building the sequence
- Call
get_sequence_creation_guide(andget_sequence_schemafor the full field/node-type reference) before callingcreate_sequence. These cover sequence-level scheduling config (timezone, send window, weekends, holidays), sending accounts, node types, field requirements, edge shape, and template variables. Node types includeemail,send_linkedin_connection,is_in_linkedin_network,linkedin_message,linkedin_inmail,whatsapp_message, andview_linkedin_profile— confirm the current full list viaget_sequence_schemasince it may expand.send_linkedin_connectionandis_in_linkedin_networkare the only branching node types (true/falseedges, never a merge point);is_in_linkedin_network's true branch can't contain another conditional, and its false branch allows at most onesend_linkedin_connection. - Before drafting account IDs into any node: call
list_connected_accountsandget_account_load_stats, present accounts by label with their load stats, and let the user choose. Email nodes take afrom_account_idslist (even for one account — never the singularfrom_account_id); LinkedIn/WhatsApp nodes take a singlefrom_account_id. For every selected email account, confirm it has a signature configured before drafting any content — if not, stop and have the user set one viaset_email_signatureor in Settings first. - Call
list_sequencesandlist_sequence_templatesfirst — check whether an existing sequence or template already covers this campaign before building from scratch. - Never skip straight to
create_sequence. Show the full plan (steps, channels, timing) and wait for explicit confirmation before creating it — treat a request like "make me a sequence for X" as the start of planning, not permission to create immediately. - See
references/writing-and-scheduling-rules.mdfor message-writing quality (subject/body rules, tone, avoiding AI writing tells) and suggested step-to-step timing gaps — these are proposable defaults to confirm with the user, not enforced by the schema, so don't apply them silently. - If
thread_emailsis enabled, only the first email node gets an original subject — every later email node's subject must beRe: <first email's subject>since they land as replies in one thread.
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.
- 4d ago First seen · 36 lines · 65 tokens per session scan A c95eb16ac036
toflow-create-sequence is a skill published in the GitHub repository toflow-ai/toflow-mcp (6 stars, last pushed 5d ago), licensed MIT. It adds 65 tokens to every session and 1,407 once invoked, about $0.0003 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 2026-10-10.
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