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Agent skill

Lead Researcher

by borghei in borghei/Claude-Skills

Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks.

MITAuto-check passedMarketing & SEO

Install Lead Researcher

skills CLI
$ npx skills add borghei/Claude-Skills --skill lead-researcher -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install borghei/Claude-Skills lead-researcher --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/personal-productivity/lead-researcher .claude/skills/lead-researcher && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
lead-researcher
GitHub stars
886
Token cost
~1.5k tokens
SKILL.md length
717 words
Files
5 (incl. scripts, references, assets)
Skills in repo
354
Repo updated
First seen
Licence
MIT

At a glance

Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks.

  • Works in 4 steps: Define your ICP in icp.json using the… → Save your lead list as a CSV with… → Run the qualifier → …
  • Building a target account list
  • SKILL.md covers Table of Contents, Keywords, Clarify First and Quick Start, plus 6 more sections
  • Runs Python scripts from its folder; calls python

What it does

Lead Researcher is an agent skill from borghei/Claude-Skills. Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks. Use when building a target account list, qualifying inbound leads, prepping for outreach, or scoring prospects.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts, reference files and assets (for example `assets/icp_schema.json`, `assets/outreach_template.md` and `references/icp_framework.md`).

It sits in Marketing & SEO, covering Lead generation and Cold outreach. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.

When your agent uses it

  • Building a target account list
  • Qualifying inbound leads
  • Prepping for outreach
  • Scoring prospects

Example prompts

  • “/lead-researcher”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Define your ICP in icp.json using the schema in assets/icp_schema.json
  2. Save your lead list as a CSV with columns: company,industry,size,country,website,signals
  3. Run the qualifier
  4. Review the ranked output — top 20% is your A-tier outreach list

What it can do on your machine

Read from SKILL.md and the folder at commit 4a698e8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Lead Researcher loads about 1.5k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 717 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~59
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from borghei/Claude-Skills at commit 4a698e8, republished under its MIT licence (© borghei). 717 words, ~1,504 tokens.

Download SKILL.mdSave it as .claude/skills/lead-researcher/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
lead-researcher
description
Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks. Use when building a target account list, qualifying inbound leads, prepping for outreach, or scoring prospects.
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
personal-productivity
metadata.domain
sales-prospecting
metadata.updated
2026-05-04
metadata.python-tools
lead_qualifier.py
metadata.tech-stack
sales, outbound, CRM

Lead Researcher

Score and qualify sales leads against an Ideal Customer Profile (ICP) definition, then draft outreach hooks tied to specific ICP signals.

Table of Contents

Keywords

lead, leads, prospect, prospecting, sales, outbound, ICP, ideal customer profile, qualify, qualification, scoring, account list, target account, outreach, cold email, BDR, SDR, account executive

Clarify First

Before scoring leads, confirm these inputs. If any is unknown or vague, ASK — do not assume:

  • ICP definition — the must-have / nice-to-have / disqualifier attributes; this IS the scoring model
  • Lead list columns — company, industry, size, country at minimum; missing fields mean no usable score
  • GTM motion — PLG vs sales-led vs channel changes which signals weight highest and the outreach-hook framing

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Quick Start

Score a Lead List in 10 Minutes
  1. Define your ICP in icp.json using the schema in assets/icp_schema.json
  2. Save your lead list as a CSV with columns: company,industry,size,country,website,signals
  3. Run the qualifier:
    bash
    python scripts/lead_qualifier.py icp.json leads.csv
  4. Review the ranked output — top 20% is your A-tier outreach list

Core Workflows

Workflow 1: ICP-Based Lead Scoring

Goal: Rank a list of candidate accounts so the top of the list reflects the best fit, not the most recent import.

Steps:

  1. Build your ICP in icp.json — see assets/icp_schema.json for the full schema
  2. Capture leads in a CSV with at minimum: company,industry,size,country
  3. Run: python scripts/lead_qualifier.py icp.json leads.csv
  4. Sort the result by score (highest first); the top 20% is your A-tier
  5. Discard everything below the disqualification threshold rather than mass-emailing

Expected Output: Ranked list with score, tier (A/B/C/disqualified), and reason per lead.

Time Estimate: 10-15 minutes for a list of 200 leads.

Workflow 2: ICP Definition

Goal: Convert a fuzzy "we sell to ops teams at mid-market SaaS" intuition into a structured ICP that the qualifier can actually score against.

Steps:

  1. Pull the company names of your last 20-50 best customers
  2. Identify the shared signals: industry, size band, geography, tech stack, pain trigger
  3. For each, decide whether it's a must-have, nice-to-have, or disqualifier
  4. Encode in icp.json per references/icp_framework.md
  5. Pressure-test by scoring last quarter's closed-won and closed-lost accounts — the model should rank the wins above the losses

Expected Output: A versioned icp.json that retroactively predicts your past wins.

Time Estimate: 1-2 hours for first pass, 30 minutes per quarterly refresh.

Show full SKILL.md (302 more words)Show less
Workflow 3: Outreach Hook Drafting

Goal: Write outreach where the personalization actually mentions a real signal, not a fake "I noticed you posted on LinkedIn."

Steps:

  1. Take the qualifier output for an A-tier lead
  2. Read the matched ICP signals — these are your hooks
  3. Use the outreach template in assets/outreach_template.md
  4. Personalize the opening line with the strongest signal (e.g., recent funding, hiring spike, product launch, public quote about a pain you solve)
  5. Keep the rest of the email short — sub-90 words

Expected Output: First-touch outreach email under 90 words with a real signal-based hook.

Time Estimate: 5 minutes per A-tier lead.

Tools

lead_qualifier.py

Reads an ICP JSON file and a leads CSV, returns a scored & tiered list.

bash
# Human-readable
python scripts/lead_qualifier.py icp.json leads.csv

# JSON for programmatic use
python scripts/lead_qualifier.py icp.json leads.csv --json

Scoring model:

  • Each ICP attribute has a weight (default 10) and direction (must / nice / disqualify)
  • Must-have hits: full weight
  • Nice-to-have hits: half weight
  • Disqualifier hits: lead drops out of consideration entirely
  • Score is normalized to 0-100

Reference Guides

  • references/icp_framework.md — How to define an ICP that actually predicts deal velocity, with worked examples by GTM motion (PLG, sales-led, channel)

Templates

  • assets/icp_schema.json — JSON schema for an ICP definition file
  • assets/outreach_template.md — Cold-touch email template with placeholder slots tied to ICP signals

Best Practices

  • Disqualify hard. Mediocre leads are worse than no leads — they consume rep time and damage sender reputation.
  • Keep ICP versioned. When deal velocity drops, your ICP is often stale. Re-derive every quarter.
  • One signal per email. Multi-signal openers feel like research dumps; one well-chosen signal feels human.
  • Leads are not opportunities. A scored A-tier lead is permission to reach out, not a forecasted deal.
  • Logs over feel. Track which signals correlate with closed-won — let data update the ICP, not vibes.

Integration Points

  • Pairs with marketing/cold-email/ for sequence design
  • Pairs with sales-success/ skills for account executive handoff
  • Feeds into business-growth/ revenue forecasting

© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references, assets) in personal-productivity/lead-researcher of borghei/Claude-Skills.

  • SKILL.md
  • assets/icp_schema.json
  • assets/outreach_template.md
  • references/icp_framework.md
  • scripts/lead_qualifier.py

Open the folder on GitHubat commit 4a698e8

Compare with similar skills

Lead Researcher next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Lead Researcher compared with similar skills
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Lead Researcher this skillborghei/Claude-Skills886—~1.5kAutomated safety check: PassMIT
Google Maps API Skillbrowser-act/skills6.1k1 repos~1.4kAutomated safety check: PassMIT
List Builderexplorium-ai/gtm-skills175—~1.5kAutomated safety check: PassMIT
100m Leadsgetagentseal/founder-playbook729—~2.3kAutomated safety check: PassMIT
Lead Gen Tool Builderexplorium-ai/gtm-skills175—~1.8kAutomated safety check: NotesMIT
B2B Lead Generationminhnv0807/ai-business-skills610—~1.2kAutomated safety check: PassMIT

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Categories

Questions about Lead Researcher

What does Lead Researcher do?

Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks. Lead Researcher is an agent skill from borghei/Claude-Skills. Qualify and prioritize sales leads against an ICP, score lead lists, and draft personalized outreach hooks.

When should I use Lead Researcher?

Lead Researcher fits situations like: building a target account list; qualifying inbound leads; prepping for outreach; scoring prospects.

How do I install Lead Researcher in Claude Code?

Run `npx skills add borghei/Claude-Skills --skill lead-researcher -a claude-code`. Or copy the skill folder (personal-productivity/lead-researcher in borghei/Claude-Skills) into .claude/skills/lead-researcher in your project. Claude Code loads it when a task matches its description.

How do I install Lead Researcher in Codex?

Run `npx skills add borghei/Claude-Skills --skill lead-researcher -a codex`. Or copy the skill folder (personal-productivity/lead-researcher in borghei/Claude-Skills) into .agents/skills/lead-researcher in your project. Codex loads it when a task matches its description.

Can I use Lead Researcher in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill lead-researcher -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lead-researcher, .gemini/skills/lead-researcher, .github/skills/lead-researcher and .opencode/skills/lead-researcher in your project.

What does Lead Researcher need to run?

Going by SKILL.md and its folder, Lead Researcher needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Lead Researcher access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Lead Researcher safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Lead Researcher use?

Lead Researcher is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Lead Researcher use?

About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.2k tokens, read only when the agent opens those files.

What are the alternatives to Lead Researcher?

Skills that share tags, products or a category with Lead Researcher: Google Maps API Skill (browser-act/skills, 6.1k stars), List Builder (explorium-ai/gtm-skills, 175 stars), 100m Leads (getagentseal/founder-playbook, 729 stars) and Lead Gen Tool Builder (explorium-ai/gtm-skills, 175 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Lead Researcher?

borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 886 GitHub stars. The repository holds 354 skills in this directory. The repository was last updated on October 7, 2026.

Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.