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 First-Touch-Inc/firsttouch-agent-skill-packs --skill sequence-qa-reviewergit clone --depth 1 https://github.com/First-Touch-Inc/firsttouch-agent-skill-packsWrote 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/first-touch-inc/firsttouch-agent-skill-packs/sequence-qa-reviewer)<a href="https://agentmods.dev/skills/first-touch-inc/firsttouch-agent-skill-packs/sequence-qa-reviewer"><img src="https://agentmods.dev/badge/skills/first-touch-inc/firsttouch-agent-skill-packs/sequence-qa-reviewer/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/first-touch-inc/firsttouch-agent-skill-packs/sequence-qa-reviewer"><img src="https://agentmods.dev/badge/skills/first-touch-inc/firsttouch-agent-skill-packs/sequence-qa-reviewer.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 | $0.00086 | $0.01404 |
| Opus 5 | $0.00043 | $0.00702 |
| Sonnet 5 | $0.00017 | $0.00281 |
| Haiku 4.5 | $0.00009 | $0.00140 |
Grade A, and why
sequence-qa-reviewer 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 9d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sequence QA Reviewer
Outcome: Catch a bad campaign before it launches - protecting the account and improving reply rates.
First-run onboarding gate
Before running this skill for the first time in a workspace, load ../../references/onboarding.md and complete the onboarding questions. Do not proceed until you know: LinkedIn account type (free/basic = no connection notes; recommend 10 connection requests/day and never exceed the FirstTouch max of 20/day; Sales Navigator/Premium = connection notes available; recommend 20 connection requests/day and never exceed the FirstTouch max of 30/day), HubSpot access (MCP, service key/private app token, HubSpot list only, or none), and which play the user wants to run. Recommend high-intent plays before outbound to keep the LinkedIn account healthy. If HubSpot is unavailable, do not run HubSpot-specific steps unless the user provides a HubSpot list FirstTouch can access.
When to use
- "Review this campaign before I launch"
- QA-ing a sequence a rep or agent built
- Auditing already-live campaigns for drift/risk
- Pre-flight check for any new batch
Step-by-step
1. Pull the campaign (FirstTouch MCP)
List the workspace's flow plans with list_flow_plans and pull the matching definition (use get_flow_workspace for workspace context and get_flow_available_variables to see which personalization variables the flow can resolve). Get the campaign/flow/sequence definition: steps, message types, timing, target list, sender seat, and personalization variables used. If the MCP cannot retrieve the full definition, ask the user to paste or export the sequence steps and mark missing fields as manual review required rather than failing silently.
2. Run the 5 review dimensions
A. Send safety
- Total volume vs. seat daily/weekly limits
- Step timing (too aggressive = account risk)
- Mix of action types (connection request, LinkedIn message, email/call/manual task where supported) within safe bounds
- Seat on frequent cooldown? → recommend lower daily volume so sends flow evenly
- Seat status not Available (Action required / Disconnected / Restricted)? → resolve before launch
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.
- 9d ago First seen · 107 lines · 86 tokens per session scan A db5454c47f6f
sequence-qa-reviewer is a skill published in the GitHub repository First-Touch-Inc/firsttouch-agent-skill-packs (5 stars, last pushed 2mo ago), licensed MIT. It adds 86 tokens to every session and 1,404 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
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