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Prompt ollama Sales Untested local free

Lead Research — Ollama

Build a respectful lead brief from public facts you paste — no scraping secrets, no spam scripts. (Ollama Modelfile + chat prompt for offline runs.)

Id: ollama/lead-research · Slug: lead-research · Status: Untested · Untested on this machine — recipe reviewed for safety (no secrets, no destructive commands, no auto-spend). Mark tested after you run it locally.

What is this

Prompt for Ollama. Build a respectful lead brief from public facts you paste — no scraping secrets, no spam scripts. (Ollama Modelfile + chat prompt for offline runs.)

How this runtime fits

This Ollama pack is a local AI agent / local AI workflow for the parent AI Team. It is a single-prompt (or system-prompt) pack — do not force multi-agent system language here.

Parent AI Team: lead-research · What is an AI Team?

What it does

Build a respectful lead brief from public facts you paste — no scraping secrets, no spam scripts. (Ollama Modelfile + chat prompt for offline runs.)

Who for

Builders working on Sales jobs who can run Ollama themselves.

Need to run

How to use

  1. Install Ollama and pull a base model you already trust (example: llama3.2).
  2. Save the Modelfile above in an empty folder.
  3. Run: ollama create botshelf-lead-research -f Modelfile
  4. Run: ollama run botshelf-lead-research
  5. Paste your input (see Example in). Expect the structured output; then stop.

Limitations

Test status

Status: Untested · Untested on this machine — recipe reviewed for safety (no secrets, no destructive commands, no auto-spend). Mark tested after you run it locally.

# Modelfile — Lead Research (Ollama)
FROM llama3.2
SYSTEM """
You are a respectful lead-research desk. Goal: brief from public facts the user pasted.

RULES:
- No scraping instructions that bypass auth or harvest personal emails at scale.
- No spam sequences. Suggest one human-sent outreach angle max.
- Separate public fact vs guess.
- Do not store or request secrets.

OUTPUT:
1) Company/person snapshot
2) Relevant public facts
3) Fit hypothesis (low confidence unless evidenced)
4) One human outreach angle
5) Do-not-do list
"""
PARAMETER temperature 0.3
PARAMETER num_ctx 8192

# Build & run (safe, local):
#   ollama create botshelf-lead-research -f Modelfile
#   ollama run botshelf-lead-research
#
# Safety: no network tools required. Do not pass API keys into the Modelfile.
Public paste:
- Acme Tools — indie SaaS for shop floors
- Blog post 2026-08: adopting local LLMs for SOP drafts
- Careers: 1 openings (generalist)
Goal: see if Build Library is relevant
Snapshot: small SaaS, local-LLM interest
Facts: blog theme matches Library; team size unclear
Fit: medium hypothesis — content-led, not hard sell
Outreach angle: share one Library recipe that matches SOP drafting
Do-not: scrape employee emails; no auto-DM sequences

Works best with 8k+ context instruct models. Smaller models may skip sections — ask it to continue once.

Parent: lead-research · implementation_id: ollama/lead-research

teams/lead-research/ollama · commit eb87e8e049fdf9908e438305ff827c7b617505b5 · license: free-use-at-own-risk

Structural check: PASS · env: structural (structural only — does not set Verified)

Soft thematic links only — not identity merges. Marketplace Teams ≠ Library AI Team records.