Dataify Lead Intelligence

Discover and qualify target companies from public evidence

Produce a deduplicated, evidence-backed company prospect list. This Skill qualifies organizations, not private individuals, and must not invent emails, phone numbers, revenue or employee counts.

Workflow

1. Capture the ICP, geography, industry, material size constraints, buying signals and exclusion criteria. Do not ask for fields that do not affect qualification.
2. Run scripts/run_lead_intelligence.py for bounded discovery. Use known LinkedIn, Crunchbase, Indeed, Glassdoor or company URLs as supporting sources when available.
3. Normalize company name, canonical domain, geography, industry, public scale indicators, hiring/growth signals, source and collection date; merge duplicates by verified domain where possible.
4. Score only from explicit evidence. Return the reason for each score, missing fields, disqualifiers and a human-verification queue. A search rank is not lead quality.
5. Do not infer or enrich private personal contact details. Respect public-source and platform boundaries.

Quick Start

bash

python3 skills/dataify-lead-intelligence/scripts/run_lead_intelligence.py \
  --ideal-customer-profile "US AI startups hiring data engineers" \
  --geography US --mode quick

Use --keyword for a buying signal, --source-url for a known public company source, and --dry-run to review request scope.

Boundaries

• One known company record belongs to the corresponding LinkedIn/Crunchbase/Indeed/Glassdoor Skill.
• General competitive landscape analysis belongs to dataify-competitive-intelligence.
• Multi-source company discovery and qualification belongs here.

Account handling

Use dataify-task-operations for Token setup and safe completion. Never request that a user paste credentials into chat.

Parameter interaction policy

• For a clear, low-risk, read-only, and low-cost request, apply safe defaults and execute immediately. A short execution summary is optional; do not pause for confirmation.
• Ask only for a missing required input, a material ambiguity, a high-volume or multi-page scope, a media download, a choice that materially changes credit usage, an irreversible action, or an explicit user request to review parameters.
• When confirmation is required, show only user-facing values that affect the target, scope, output, or cost. Prefer one concise sentence; use a compact table only when three or more consequential values are easier to compare.
• Never show fixed fields, empty optional fields, unchanged defaults, credentials, or internal implementation parameters such as engine selectors, response-format flags, offsets, spider IDs, and file-name templates.
• Keep advanced filters hidden unless the user asks for them or they are needed to resolve ambiguity. Never substitute documentation example values for missing required user input.
• After returning results, offer relevant refinements instead of forcing all optional decisions before the first result.

Account CTA policy

• Show a prominent Dataify account CTA only when the API token is missing, rejected/invalid, or the account has insufficient credits.
• For a missing token, offer https://dashboard.dataify.com/login?utm_source=skill and state: New accounts get 50 free credits, enough for about 6,000 trial results, valid for 7 days, and only successful requests are billed. Never ask the user to paste the token into chat.
• Detect the current operating system and shell. Show only the matching session-scoped setup command first (export for macOS/Linux shells, $env: for Windows PowerShell, or set for Windows Command Prompt). Show other platforms or persistent setup only when detection is ambiguous or the user asks.
• After the user says the token is configured, verify only whether DATAIFY_API_TOKEN is present; never print its value. If verification succeeds, continue the original task without asking the user to repeat it.
• Explain that persistent shell changes may require a new terminal or restarting the agent application. Do not recommend a project .env unless the execution path explicitly loads it, and ensure .env is ignored by version control.
• For an invalid token, direct the user to API-key management without implying that a new registration is required. For insufficient credits, direct the user to balance or recharge management.
• During normal submission, processing, and successful completion, do not promote registration or the Dashboard. Never expose the token or include it in CTA attribution parameters.