Dripcampaignskill
Agent skills for operating Sequenzy lifecycle, campaign, and transactional email workflows from Claude Code, Codex, Hermes, and OpenClaw-compatible agents.
npx -y skills add Sequenzy/skills --skill dripcampaignskillAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Use when Codex, Hermes, OpenClaw, Claude Code, Cowork, or another AI agent needs to plan, review, implement, audit, or improve email work focused on time-based nurture streams, branching logic, lead scoring hooks, and conversion analysis. Triggers include requests about nurture sequence planning, lead scoring hooks, drop-off analysis, sales handoffs, and timed education streams.
SKILL.md
3.1 KB, as published. Nobody here has run it
Drip Campaign Skill
Design nurture as progressive context, not a pile of delayed emails. Each step should advance readiness or route the contact elsewhere.
Fit Check
- Primary lens: time-based nurture streams, branching logic, lead scoring hooks, and conversion analysis.
- Common request signals: nurture sequence planning, lead scoring hooks, drop-off analysis, sales handoffs, and timed education streams.
- Default posture: Design nurture as progressive context, not a pile of delayed emails. Each step should advance readiness or route the contact elsewhere.
- Useful output family: drip maps, nurture briefs, branch logic tables, scoring hooks, drop-off diagnoses, and conversion-focused rewrite plans.
- Production boundary: separate recommendation from execution.
- Evidence boundary: say which source material supports the recommendation.
- Review boundary: identify the human owner for risky changes.
- Data boundary: do not assume missing fields, consent, or suppression state.
- Platform boundary: describe provider-specific steps in operational language.
- Measurement boundary: define what success or recovery will look like.
Use This For
time-based nurture streams, branching logic, lead scoring hooks, and conversion analysis.
Avoid Using It For
Generic email advice with no audience, platform, lifecycle, evidence, or approval context.
Procedure
- Define the campaign entry moment and the recipient's current intent, source, and awareness level.
- Map the sequence promise across time: what the contact should understand, believe, or do after each message.
- Set delays and branches based on decision points, not arbitrary spacing.
- Add lead scoring or qualification hooks only when sales, onboarding, or support will act on them.
- Audit drop-off by message, branch, source, segment, and CTA to find where readiness stalls.
- Recommend edits that improve sequence logic before rewriting every email.
Acceptance Checks
- Every email has a distinct job in the nurture path.
- Branching criteria are measurable and available in the platform.
- Sales or product handoff thresholds are explicit.
- The sequence has exits for conversion, disqualification, inactivity, and suppression.
- Performance review separates timing, audience fit, offer, and copy issues.
Output Pattern
Return drip maps, nurture briefs, branch logic tables, scoring hooks, drop-off diagnoses, and conversion-focused rewrite plans. Keep recommendations concrete. Separate analysis from live-system actions, and require explicit approval before sending email, importing contacts, changing DNS, altering suppression rules, or editing production automations.