dripcampaignskill
Use when Codex, Hermes, OpenClaw, Claude Code, Cowork, or another AI agent needs to plan, review, implement, audit, or improve email work focused on…
Skill by Sequenzy
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/sequenzy-dripcampaignskill
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: security, sales, productivity, agent-building
Reviews
Write a reviewNo reviews yet. Installed it? Reviews come from verified installers only. How reviews work
Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/dripcampaignskill
What's inside
- Skill name
dripcampaignskill- Description
- 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.
- License
- MIT
- Instructions
- 47 lines in SKILL.md · view on GitHub
Files (3)
- SKILL.md
- openai.yaml · agents/
- operating-checklist.md · references/
SKILL.md (first lines)
# 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 1. Primary lens: time-based nurture streams, branching logic, lead scoring hooks, and conversion analysis. 2. Common request signals: nurture sequence planning, lead scoring hooks, drop-off analysis, sales handoffs, and timed education streams. 3. Default posture: Design nurture as progressive context, not a pile of delayed emails. Each step should advance readiness or route the contact elsewhere. 4. Useful output family: drip maps, nurture briefs, branch logic tables, scoring hooks, drop-off diagnoses, and conversion-focused rewrite plans. 5. Production boundary: separate recommendation from execution. 6. Evidence boundary: say which source material supports the recommendation. 7. Review boundary: identify the human owner for risky changes. 8. Data boundary: do not assume missing fields, consent, or suppression state. 9. Platform boundary: describe provider-specific steps in operational language. 10. 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 1. Define the campaign entry moment and the recipient's current intent, source, and awareness level. 2. Map the sequence promise across time: what the contact should understand, believe, or do after each message. 3. Set delays and branches based on decision points, not arbitrary spacing. 4. Add lead scoring or qualification hooks only when sales, onboarding, or support will act on them. 5. Audit drop-off by message, branch, source, segment, and CTA to find where readiness stalls. 6. Recommend edits that improve sequence logic before rewriting every email. ## Acceptance Checks 1. Every email has a distinct job in the nurture path. 2. Branching criteria are measurable and available in the platform. 3. Sales or product handoff thresholds are explicit. 4. The sequence has exits for conversion, disqualification, inactivity, and suppression. 5. 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.
From Sequenzy/skills, MIT.
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Signals
- Stars
- 2
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jun 17, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/Sequenzy/skills
- Website
- sequenzy.com
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
dripcampaignskill. Agent Mart. https://agentmart.dev/skills/sequenzy-dripcampaignskill (last updated 2026-10-24)