lead-generation

Lead Generation — Find high-intent buyers in live Twitter, Instagram, and Reddit conversations. Auto-researches your product, generates targeted search queri...

Security Scan

The skill's requests and instructions are coherent with its stated purpose: it uses a SkillBoss API key to search social sites, saves local lead files, and drafts outreach — nothing requested appears disproportionate or unrelated.

Purpose & Capability

Name and description match the actual behavior: product research + generating queries + searching social sites via SkillBoss API. The single required env var (SKILLBOSS_API_KEY) and network target (api.skillbossai.com) align with that purpose.

Instruction Scope

Instructions stay within lead discovery scope (research product, generate queries, call SkillBoss pilot endpoint, save results, score and draft outreach). They do instruct the agent to write files under data/lead-generation/ (product-profile.json, search-queries.json, sent-leads.json) — expected for a prospecting workflow but worth noting because saved data will persist locally and may contain personal data.

Install Mechanism

No install spec or code is present (instruction-only). That minimizes disk writes by the skill itself; runtime behavior depends on the agent executing the instructions and the SkillBoss API.

Credentials

Only SKILLBOSS_API_KEY is required, which is proportional for a service that proxies searches. No unrelated credentials or config paths are requested.

Persistence & Privilege

Skill is not force-enabled (always: false). It writes its own local data files per instructions but does not request broader system or other-skills config access. Autonomous invocation is allowed (platform default) but not exceptional here.

Assessment

This skill appears internally consistent, but review these points before installing: - You must provide SKILLBOSS_API_KEY; only give it if you trust SkillBoss and have reviewed its privacy/terms. - The skill saves lead data locally (data/lead-generation/...). Decide whether storing potentially personal information locally is acceptable and where those files live/are backed up. - The agent will call an external API (api.skillbossai.com) with queries derived from your product data; avoid providing sensitive proprietary material during discovery if you don't want it transmitted. - The skill drafts outreach but does not enforce sending rules; verify drafts before sending and ensure compliance with platform TOS and privacy/regulatory requirements (e.g., GDPR, platform anti-scraping policies). - If you want to limit blast radius, test with a scoped/test API key and review stored outputs, or run the flow interactively rather than allowing full autonomous runs.

Find high-intent buyers from live social conversations.

Discovers leads expressing problems your product solves, complaining about competitors, or actively seeking solutions across Twitter, Instagram, and Reddit.

Setup

Set SKILLBOSS_API_KEY environment variable. All search calls are routed through SkillBoss API Hub (https://api.skillbossai.com/v1/pilot).

3-Phase Process

Phase 1: Product Research (One-Time)

Ask for product reference (website/GitHub/description). Use web_fetch/web_search to research. Build profile: product info, target audience, pain points, competitors, keywords. Validate with user.

Generate 12-18 queries across:

1. Pain point queries — people expressing problems
2. Competitor frustration — complaints about alternatives
3. Tool/solution seeking — "recommend..."
4. Industry discussion — target audience

Save to data/lead-generation/product-profile.json and search-queries.json.

Phase 2: Lead Discovery (Repeatable)

Use SkillBoss API Hub to search for relevant social posts and users:

import requests, os

SKILLBOSS_API_KEY = os.environ["SKILLBOSS_API_KEY"]
API_BASE = "https://api.skillbossai.com/v1"

def pilot(body: dict) -> dict:
    r = requests.post(
        f"{API_BASE}/pilot",
        headers={"Authorization": f"Bearer {SKILLBOSS_API_KEY}", "Content-Type": "application/json"},
        json=body,
        timeout=60,
    )
    return r.json()

# Search Twitter posts by keyword
result = pilot({"type": "search", "inputs": {"query": "GENERATED_QUERY site:twitter.com"}, "prefer": "balanced"})
posts = result["result"]["results"]

# Search Instagram posts by keyword
result = pilot({"type": "search", "inputs": {"query": "GENERATED_QUERY site:instagram.com"}, "prefer": "balanced"})
posts = result["result"]["results"]

# Search Reddit posts by keyword
result = pilot({"type": "search", "inputs": {"query": "GENERATED_QUERY site:reddit.com"}, "prefer": "balanced"})
posts = result["result"]["results"]

Repeat for each generated query across platforms.

Phase 3: Scoring & Output

Score (1-10):

• Explicitly asking for solution: +3
• Complaining about competitor: +2
• Project blocked by pain: +2
• Active in target community: +1
• High engagement (>10 likes/5 comments): +1
• Recent (<48h): +1
• Profile matches ICP: +1
• Selling competing solution: -3

Tiers: 8-10 Hot, 6-7 Warm, 5 Watchlist

Deduplicate via data/lead-generation/sent-leads.json (key: {platform}:{author}:{post_id}).

Output: Username, quote, URL, score, why fit, outreach draft, engagement, timestamp.

Outreach:

"I had the same problem! Ended up using [Product] — it does [capability]. [URL] (Disclosure: I work with [Product])"

Tips

• Save profile once, reuse daily
• Quality > quantity
• Always disclose affiliations
• Draft only; user reviews/sends