Find, enrich, and load contacts into an outreach sequence — end to end. The user provides targeting criteria and a sequence name via "$ARGUMENTS".
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionsequence-loadExecute the skills CLI command in your project's root directory to begin installation:
Fetches sequence-load from anthropics/knowledge-work-plugins and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate sequence-load. Access via /sequence-load in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Find, enrich, and load contacts into an outreach sequence — end to end. The user provides targeting criteria and a sequence name via "$ARGUMENTS".
/apollo:sequence-load add 20 VP Sales at SaaS companies to my "Q1 Outbound" sequence/apollo:sequence-load SDR managers at fintech startups → Cold Outreach v2/apollo:sequence-load list sequences (shows all available sequences)/apollo:sequence-load directors of engineering, 500+ employees, US → Demo Follow-up/apollo:sequence-load reload 15 more leads into "Enterprise Pipeline"From "$ARGUMENTS", extract:
Targeting criteria:
person_titlesperson_senioritiesq_organization_keyword_tagsorganization_num_employees_rangesperson_locations or organization_locationsSequence info:
If the user just says "list sequences", skip to Step 2 and show all available sequences.
Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_search to find the target sequence:
q_name to the sequence name from inputIf no match or multiple matches:
Use mcp__claude_ai_Apollo_MCP__apollo_email_accounts_index to list linked email accounts.
Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with the targeting criteria.
per_page to the requested volume (or 10 by default)Present the candidates in a preview table:
| # | Name | Title | Company | Location |
|---|
Ask: "Add these [N] contacts to [Sequence Name]? This will consume [N] Apollo credits for enrichment."
Wait for confirmation before proceeding.
For each approved lead:
Enrich — Use mcp__claude_ai_Apollo_MCP__apollo_people_bulk_match (batch up to 10 per call) with:
first_name, last_name, domain for each personreveal_personal_emails set to trueCreate contacts — For each enriched person, use mcp__claude_ai_Apollo_MCP__apollo_contacts_create with:
first_name, last_name, email, title, organization_namedirect_phone or mobile_phone if availablerun_dedupe set to trueCollect all created contact IDs.
Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_add_contact_ids with:
id: the sequence IDemailer_campaign_id: same sequence IDcontact_ids: array of created contact IDssend_email_from_email_account_id: the chosen email account IDsequence_active_in_other_campaigns: false (safe default)Show a summary:
Sequence loaded successfully
| Field | Value |
|---|---|
| Sequence | [Name] |
| Contacts added | [count] |
| Sending from | [email address] |
| Credits used | [count] |
Contacts enrolled:
| Name | Title | Company |
|---|
Ask the user:
mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_remove_or_stop_contact_ids to remove specific contactsstatus: "paused" and an auto_unpause_at dateMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
sequence-load has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for sequence-load matched our evaluation — installs cleanly and behaves as described in the markdown.
sequence-load has been reliable in day-to-day use. Documentation quality is above average for community skills.
sequence-load fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: sequence-load is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for sequence-load matched our evaluation — installs cleanly and behaves as described in the markdown.
Useful defaults in sequence-load — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend sequence-load for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
sequence-load reduced setup friction for our internal harness; good balance of opinion and flexibility.
sequence-load reduced setup friction for our internal harness; good balance of opinion and flexibility.
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