Find Leads
eracle/OpenOutreach
Find qualified B2B leads with OpenOutreach — run openoutreach find N [emails], read the CSV it prints on stdout, and hand the rows to whatever sends.
Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus.
Install Lead Scoring
$ npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills lead-scoring --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/sales/lead-scoring .claude/skills/lead-scoring && rm -rf skills-srcUse ~/.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/
Install the "lead-scoring" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/sales/lead-scoring into .claude/skills/lead-scoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lead-scoring", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/seb1n/awesome-ai-agent-skills/tree/main/sales/lead-scoringType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills lead-scoring --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/sales/lead-scoring .agents/skills/lead-scoring && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "lead-scoring" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/sales/lead-scoring into .agents/skills/lead-scoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lead-scoring", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills lead-scoring --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/sales/lead-scoring .cursor/skills/lead-scoring && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "lead-scoring" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/sales/lead-scoring into .cursor/skills/lead-scoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lead-scoring", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/seb1n/awesome-ai-agent-skills.git --path sales/lead-scoring--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills lead-scoring --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/sales/lead-scoring .gemini/skills/lead-scoring && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "lead-scoring" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/sales/lead-scoring into .gemini/skills/lead-scoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lead-scoring", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install seb1n/awesome-ai-agent-skills lead-scoringInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/sales/lead-scoring .github/skills/lead-scoring && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "lead-scoring" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/sales/lead-scoring into .github/skills/lead-scoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lead-scoring", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install seb1n/awesome-ai-agent-skills lead-scoring --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/sales/lead-scoring .opencode/skills/lead-scoring && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "lead-scoring" agent skill from https://github.com/seb1n/awesome-ai-agent-skills/tree/main/sales/lead-scoring into .opencode/skills/lead-scoring/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lead-scoring", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
lead-scoring- GitHub stars
- 206
- Token cost
- ~1.8k tokens
- SKILL.md length
- 971 words
- Files
- 1
- Skills in repo
- 92
- Repo updated
- First seen
- Licence
- MIT
At a glance
Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus.
- Works in 5 steps: Define ICP Criteria — Establish the… → Assign Fit Scores — Score each lead's… → Track Engagement Signals — Capture… → …
- The user requests lead scoring
- SKILL.md covers Workflow, Usage, Examples and Best Practices, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Lead Scoring is an agent skill from seb1n/awesome-ai-agent-skills. Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus. Use when the user requests lead scoring or provides relevant inputs for this workflow.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Marketing & SEO, covering Lead generation. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.
When your agent uses it
- The user requests lead scoring
- Provides relevant inputs for this workflow
Example prompts
- “/lead-scoring”
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- Define ICP Criteria — Establish the firmographic attributes of your ideal customer: target industries, company size ranges, revenue bands…
- Assign Fit Scores — Score each lead's company against ICP criteria. A perfect-fit lead earns maximum fit points; partial matches earn…
- Track Engagement Signals — Capture behavioral signals from marketing automation, CRM, and product analytics: email opens/clicks, website…
- Calculate Composite Score — Combine fit score (typically 0–50 points) and engagement score (typically 0–50 points) into a composite score…
- Rank and Segment into Tiers — Sort leads by composite score and assign tiers: Hot (75–100), Warm (40–74), Cold (0–39). Route Hot leads to…
What it can do on your machine
Read from SKILL.md and the folder at commit 75865a5. 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
No scripts in the folder and no shell commands in SKILL.md.
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 Scoring loads about 1.8k tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 971 words of instructions outside code blocks.
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); files beside SKILL.md are not scanned.
SKILL.md
The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 971 words, ~1,799 tokens.
Download SKILL.mdSave it as
.claude/skills/lead-scoring/SKILL.md (or your agent's skills folder).- name
- lead-scoring
- description
- Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus. Use when the user requests lead scoring or provides relevant inputs for this workflow.
- license
- MIT
- metadata.author
- community
- metadata.version
- 1.0
Lead Scoring
Score and prioritize inbound and outbound leads by combining firmographic fit (how closely a lead matches your ideal customer profile) with behavioral engagement signals (actions that indicate purchase intent). This skill builds scoring rubrics, assigns weighted points, calculates composite scores, and segments leads into actionable tiers — Hot, Warm, and Cold — so sales teams focus time on the highest-converting opportunities.
Workflow
Define ICP Criteria — Establish the firmographic attributes of your ideal customer: target industries, company size ranges, revenue bands, geographic regions, and technology stack indicators. Each attribute gets a weight reflecting its predictive importance based on historical conversion data.
Assign Fit Scores — Score each lead's company against ICP criteria. A perfect-fit lead earns maximum fit points; partial matches earn proportional scores. Negative scoring applies for explicit disqualifiers (e.g., company size below minimum threshold, industries you don't serve, students or competitors).
Track Engagement Signals — Capture behavioral signals from marketing automation, CRM, and product analytics: email opens/clicks, website page visits (especially pricing and case study pages), content downloads, webinar attendance, demo requests, free trial signups, and reply sentiment. Weight each signal by its correlation to closed-won deals.
Calculate Composite Score — Combine fit score (typically 0–50 points) and engagement score (typically 0–50 points) into a composite score (0–100). Apply decay to engagement signals older than 30 days to ensure the score reflects current intent, not stale activity.
Rank and Segment into Tiers — Sort leads by composite score and assign tiers: Hot (75–100), Warm (40–74), Cold (0–39). Route Hot leads to SDRs for immediate outreach, Warm leads to nurture sequences, and Cold leads to low-touch automated campaigns. Review tier thresholds quarterly against actual conversion rates and adjust.
Usage
Provide your ICP definition, the engagement signals you track, and a list of leads with their attributes. The skill outputs a scoring rubric and scored/ranked lead list.
Example prompt:
Build a lead scoring model for our B2B analytics platform. ICP: Series A+ SaaS companies, 50–500 employees, US/Canada, using Snowflake or BigQuery. Score these 5 leads and assign Hot/Warm/Cold tiers.
Examples
Example 1: Building a Lead Scoring Rubric
Input: B2B analytics platform targeting mid-market SaaS companies.
Fit Scoring Rubric (0–50 points):
| Criterion | Weight | Scoring Rules |
|---|---|---|
| Company size | 15 pts | 200–500 emp: 15 · 50–199 emp: 10 · 501–1000 emp: 5 · <50 or >1000: 0 |
| Industry | 10 pts | SaaS/Software: 10 · Fintech/E-commerce: 7 · Other tech: 4 · Non-tech: 0 |
| Funding stage | 10 pts | Series A–C: 10 · Seed: 5 · Public/Pre-seed: 2 |
| Geography | 5 pts | US/Canada: 5 · UK/EU: 3 · Other: 1 |
| Tech stack | 10 pts | Snowflake or BigQuery: 10 · Redshift: 6 · No cloud DW: 0 |
Engagement Scoring Rubric (0–50 points):
| Signal | Points | Decay |
|---|---|---|
| Demo requested | 20 pts | None (one-time event) |
| Pricing page visit | 8 pts | Halved after 14 days |
| Case study download | 6 pts | Halved after 21 days |
| Email link clicked | 3 pts (per click, max 12) | Halved after 14 days |
| Webinar attended | 7 pts | Halved after 30 days |
| Blog visit | 1 pt (per visit, max 5) | Expires after 30 days |
Tier Thresholds:
| Tier | Score Range | Action |
|---|---|---|
| Hot | 75–100 | Immediate SDR outreach within 4 hours |
| Warm | 40–74 | Enroll in high-touch nurture sequence |
| Cold | 0–39 | Low-touch automated drip campaign |
Show full SKILL.md (451 more words)Show less
Example 2: Scoring a Batch of Leads
Input: 5 leads with attributes and recent activity.
Scored Output:
| Lead | Company | Employees | Industry | Funding | Tech Stack | Fit Score | Key Engagement | Eng. Score | Total | Tier |
|---|---|---|---|---|---|---|---|---|---|---|
| Rachel M. | StreamOps | 320 | SaaS | Series B | Snowflake | 50 | Demo request + pricing visit + 2 email clicks | 34 | 84 | 🔥 Hot |
| David K. | PayFlow | 180 | Fintech | Series A | BigQuery | 37 | Webinar + case study download + 3 email clicks | 22 | 59 | 🟡 Warm |
| Priya S. | HealthBridge | 90 | Healthcare | Series B | Redshift | 21 | Pricing page visit + 1 email click | 11 | 32 | 🔵 Cold |
| Marcus T. | DevLayer | 450 | SaaS | Series C | Snowflake | 50 | 4 blog visits + 1 email click | 8 | 58 | 🟡 Warm |
| Lisa C. | TinyML Labs | 30 | AI/ML | Seed | BigQuery | 20 | Demo request + webinar | 27 | 47 | 🟡 Warm |
Summary: 1 Hot lead (route to SDR), 3 Warm leads (nurture sequence), 1 Cold lead (automated drip). Marcus T. has a perfect fit score but low engagement — prioritize getting him to a demo.
Best Practices
- Weight your scoring model on historical closed-won data, not intuition — run a correlation analysis between lead attributes and conversion to calibrate point values.
- Apply score decay to engagement signals so that a lead who was active 6 months ago doesn't rank above a lead showing intent today.
- Include negative scoring for explicit disqualifiers (competitors, students, non-target geographies) to keep noise out of the Hot tier.
- Review and recalibrate tier thresholds quarterly; as your ICP evolves and marketing channels shift, static thresholds drift from reality.
- Separate fit and engagement scores in your reporting so reps can distinguish "great company, not engaged yet" from "engaged lead at a poor-fit company."
- Set a minimum fit score threshold (e.g., 15 points) below which no amount of engagement can push a lead to Hot — this prevents poor-fit leads from wasting SDR time.
Edge Cases
- High engagement, zero fit — A lead who downloads every resource but works at a 5-person agency outside your ICP. Cap their maximum tier at Warm regardless of engagement score to avoid wasting sales cycles.
- Perfect fit, no engagement — A lead matching ICP criteria exactly but showing no behavioral signals. Flag for outbound prospecting rather than inbound follow-up; they may not know you exist yet.
- Duplicate leads from the same company — When multiple contacts at one company score independently, consolidate into an account-level score to avoid double-counting the same buying intent.
- Engagement signal spam — A lead who opens every email and visits every page may be a researcher, bot, or competitor. Set engagement score caps per signal type and flag anomalous activity patterns for manual review.
- Scoring model cold-start — For new products or markets without historical conversion data, start with a simple unweighted model, collect 90 days of pipeline data, then recalibrate weights based on actual outcomes.
© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in sales/lead-scoring of seb1n/awesome-ai-agent-skills.
Open the folder on GitHubat commit 75865a5
Compare with similar skills
Lead Scoring 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Lead Scoring this skillseb1n/awesome-ai-agent-skills | 206 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Find Leadseracle/OpenOutreach | 3.2k | — | ~4.6k | Automated safety check: Pass | GPL-3.0 | |
| 100m Leadsgetagentseal/founder-playbook | 721 | — | ~2.3k | Automated safety check: Pass | MIT | |
| GitHub Lead GenDucksss/codex-profiles | 177 | — | ~1k | Automated safety check: Pass | MIT | |
| LinkedIn Ads Managementivangfalco/ads-skills | 278 | — | ~1.7k | Automated safety check: Pass | Custom licence | |
| GitHub Lead QualificationDucksss/codex-profiles | 177 | — | ~1.2k | Automated safety check: Pass | MIT |
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Questions about Lead Scoring
What does Lead Scoring do?
Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus. Lead Scoring is an agent skill from seb1n/awesome-ai-agent-skills. Score and prioritize leads based on firmographic fit and behavioral engagement signals, producing ranked tiers for sales team focus.
When should I use Lead Scoring?
Lead Scoring fits situations like: the user requests lead scoring; provides relevant inputs for this workflow.
How do I install Lead Scoring in Claude Code?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a claude-code`. Or copy the skill folder (sales/lead-scoring in seb1n/awesome-ai-agent-skills) into .claude/skills/lead-scoring in your project. Claude Code loads it when a task matches its description.
How do I install Lead Scoring in Codex?
Run `npx skills add seb1n/awesome-ai-agent-skills --skill lead-scoring -a codex`. Or copy the skill folder (sales/lead-scoring in seb1n/awesome-ai-agent-skills) into .agents/skills/lead-scoring in your project. Codex loads it when a task matches its description.
Can I use Lead Scoring 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 seb1n/awesome-ai-agent-skills --skill lead-scoring -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-scoring, .gemini/skills/lead-scoring, .github/skills/lead-scoring and .opencode/skills/lead-scoring in your project.
What does Lead Scoring need to run?
SKILL.md names no scripts, command-line tools or credentials: Lead Scoring is instructions for the agent only.
Does Lead Scoring 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 Scoring 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. Review the folder before installing.
What licence does Lead Scoring use?
Lead Scoring 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 Scoring use?
About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Lead Scoring?
Skills that share tags, products or a category with Lead Scoring: Find Leads (eracle/OpenOutreach, 3.2k stars), 100m Leads (getagentseal/founder-playbook, 721 stars), GitHub Lead Gen (Ducksss/codex-profiles, 177 stars) and LinkedIn Ads Management (ivangfalco/ads-skills, 278 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Lead Scoring?
seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 92 skills in this directory. The repository was last updated on August 9, 2026.
Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.