lead-generation

A workflow for finding possible business customers in conversations on X, the social network formerly known as Twitter. It searches for relevant posts, examines profiles, and assigns an intent score based on signs such as comparing tools or looking to buy.

In plain words
What is it for?
Searching X conversations, reviewing potential customers’ profiles and activity, and ranking leads for B2B outreach.
Why use it?
It reduces the manual effort of finding people who may need a product or service and separating likely prospects from casual topic interest.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/nirholas/xactions/lead-generation
Any agent
npx skills add nirholas/XActions --skill lead-generation
Clone the repo
git clone --depth 1 https://github.com/nirholas/XActions

Made for: Claude Code, Codex.

Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 742 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00053 $0.00742
Opus 5 $0.00026 $0.00371
Sonnet 5 $0.00011 $0.00148
Haiku 4.5 $0.00005 $0.00074

Measured 2d ago against content hash 329c4633bcf7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

lead-generation 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 2d 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.

skills/lead-generation/SKILL.md · 74 lines

How it starts

The opening of the file, as written. The whole thing — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Lead Generation

MCP-powered workflow for finding and qualifying B2B leads from X/Twitter conversations and profiles.

MCP Tools Used

Tool Purpose
x_search_tweets Find conversations by keyword/intent
x_get_profile Qualify leads with profile data
x_get_tweets Assess activity level and interests
x_get_followers Check audience size and quality
x_get_following Identify competitor usage / peer network

Workflow

  1. Define search queries -- Build 3-5 keyword queries combining pain points, competitor names, or buying signals (e.g., "looking for {tool}", "anyone recommend {category}", "switching from {competitor}").
  2. Search conversations -- Call x_search_tweets for each query with limit: 30. Collect unique usernames.
  3. Qualify profiles -- Call x_get_profile for each. Filter by: has bio, followers > 100, account age > 6 months.
  4. Score intent -- Assign 1-5 score:
    • 5: Explicit buying intent ("need a tool for...", "budget approved")
    • 4: Comparing solutions ("X vs Y", "switching from")
    • 3: Pain point discussion ("struggling with...")
    • 2: Topic interest (engages with industry content)
    • 1: Tangential mention
  5. Gather context -- For top leads (4-5), call x_get_tweets with limit: 20.
  6. Check network -- Call x_get_following for high-value leads to see competitor follows.
  7. Export lead list -- Format as structured output.

Browser Script Integration

Enhance MCP workflows with browser scripts:

Goal Script
Monitor keywords in real-time src/keywordMonitor.js
Analyze potential lead's audience src/audienceDemographics.js
Check overlap with your audience src/audienceOverlap.js
Engage with leads' content src/engagementBooster.js
Auto-follow qualified leads src/automation/keywordFollow.js

Output Template

## Lead List: {search_topic}
Generated: {date} | Total qualified: {count}

| Username | Score | Followers | Signal | Tweet URL |
|----------|-------|-----------|--------|-----------|
| @{user}  | {1-5} | {count}   | {type} | {url}     |

### High-Priority Leads (Score 4-5)

**@{username}** -- Score: {n}/5
- Signal: "{tweet excerpt}"
- Bio: {bio}
- Suggested approach: {personalized outreach note}

Read the full file on GitHub · 74 lines

Changes

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.

  1. 2d ago First seen · 74 lines · 53 tokens per session scan A 329c4633bcf7

Subscribe to this mod's changes

lead-generation is a skill published in the GitHub repository nirholas/XActions (496 stars, last pushed 5d ago), licensed Apache-2.0. It adds 53 tokens to every session and 742 once invoked, about $0.0003 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 2026-08-30.

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