sequence-load

anthropics/knowledge-work-plugins · updated Apr 8, 2026

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$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill sequence-load
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summary

Find, enrich, and load contacts into an outreach sequence — end to end. The user provides targeting criteria and a sequence name via "$ARGUMENTS".

skill.md

Sequence Load

Find, enrich, and load contacts into an outreach sequence — end to end. The user provides targeting criteria and a sequence name via "$ARGUMENTS".

Examples

  • /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"

Step 1 — Parse Input

From "$ARGUMENTS", extract:

Targeting criteria:

  • Job titles → person_titles
  • Seniority levels → person_seniorities
  • Industry keywords → q_organization_keyword_tags
  • Company size → organization_num_employees_ranges
  • Locations → person_locations or organization_locations

Sequence info:

  • Sequence name (text after "to", "into", or "→")
  • Volume — how many contacts to add (default: 10 if not specified)

If the user just says "list sequences", skip to Step 2 and show all available sequences.

Step 2 — Find the Sequence

Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_search to find the target sequence:

  • Set q_name to the sequence name from input

If no match or multiple matches:

  • Show all available sequences in a table: | Name | ID | Status |
  • Ask the user to pick one

Step 3 — Get Email Account

Use mcp__claude_ai_Apollo_MCP__apollo_email_accounts_index to list linked email accounts.

  • If one account → use automatically
  • If multiple → show them and ask which to send from

Step 4 — Find Matching People

Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with the targeting criteria.

  • Set 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.

Step 5 — Enrich and Create Contacts

For each approved lead:

  1. 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 person
    • reveal_personal_emails set to true
  2. Create contacts — For each enriched person, use mcp__claude_ai_Apollo_MCP__apollo_contacts_create with:

    • first_name, last_name, email, title, organization_name
    • direct_phone or mobile_phone if available
    • run_dedupe set to true

Collect all created contact IDs.

Step 6 — Add to Sequence

Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_add_contact_ids with:

  • id: the sequence ID
  • emailer_campaign_id: same sequence ID
  • contact_ids: array of created contact IDs
  • send_email_from_email_account_id: the chosen email account ID
  • sequence_active_in_other_campaigns: false (safe default)

Step 7 — Confirm Enrollment

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 Email

Step 8 — Offer Next Actions

Ask the user:

  1. Load more — Find and add another batch of leads
  2. Review sequence — Show sequence details and all enrolled contacts
  3. Remove a contact — Use mcp__claude_ai_Apollo_MCP__apollo_emailer_campaigns_remove_or_stop_contact_ids to remove specific contacts
  4. Pause a contact — Re-add with status: "paused" and an auto_unpause_at date
how to use sequence-load

How to use sequence-load on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add sequence-load
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/anthropics/knowledge-work-plugins --skill sequence-load

The skills CLI fetches sequence-load from GitHub repository anthropics/knowledge-work-plugins and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/sequence-load

Reload or restart Cursor to activate sequence-load. Access the skill through slash commands (e.g., /sequence-load) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.651 reviews
  • Ganesh Mohane· Dec 28, 2024

    sequence-load has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Shikha Mishra· Dec 24, 2024

    Registry listing for sequence-load matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Michael Diallo· Dec 24, 2024

    sequence-load has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • James Bhatia· Dec 12, 2024

    sequence-load fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sakura Jain· Dec 4, 2024

    Keeps context tight: sequence-load is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Ava Taylor· Dec 4, 2024

    Registry listing for sequence-load matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Camila Liu· Nov 23, 2024

    Useful defaults in sequence-load — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • James Chawla· Nov 23, 2024

    I recommend sequence-load for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kofi Johnson· Nov 23, 2024

    sequence-load reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Yash Thakker· Nov 15, 2024

    sequence-load reduced setup friction for our internal harness; good balance of opinion and flexibility.

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