competitor-engagers

competitor-engagers is a skill for Claude Code from growthenginenowoslawski/coldoutboundskills. It costs 75 tokens per session (1,077 once invoked), scanned A, original, MIT.

A lead-finding workflow that identifies people who comment on or react to competitor posts on LinkedIn, a professional social network. It collects those people into a deduplicated CSV file for further qualification and outreach.

In plain words
What is it for?
Use it to discover potential prospects from competitor engagement, sample and qualify contacts, and prepare a cleaned CSV for outreach.
Why use it?
It turns scattered competitor-post activity into a contact list, while prompting you to filter out people who are not likely customers before spending money on enrichment or sending.

Skill for Claude Code

Written for Claude Code: user-invocable in frontmatter.

Good fit Use it to discover potential prospects from competitor engagement, sample and qualify contacts, and prepare a cleaned CSV for outreach.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/growthenginenowoslawski/coldoutboundskills/competitor-engagers
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add growthenginenowoslawski/coldoutboundskills --skill competitor-engagers
Clone the repo
git clone --depth 1 https://github.com/growthenginenowoslawski/coldoutboundskills

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for competitor-engagers

README.md
[![agentmods](https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/competitor-engagers/github.svg)](https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/competitor-engagers)
Your own site
<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/competitor-engagers"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/competitor-engagers/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for competitor-engagers

Your own site · 80×15
<a href="https://agentmods.dev/skills/growthenginenowoslawski/coldoutboundskills/competitor-engagers"><img src="https://agentmods.dev/badge/skills/growthenginenowoslawski/coldoutboundskills/competitor-engagers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,077 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.01077
Opus 5.5 $0.00030 $0.00431
Sonnet 5.5 $0.00015 $0.00215
Haiku 4.5 $0.00007 $0.00108

Measured 1mo ago against content hash 31c13de6e6d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

competitor-engagers scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/competitor-engagers/SKILL.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Competitor Engagers

Finds people who are actively engaging with your competitors on LinkedIn — the highest-intent prospects for cold outreach.

Required step: Qualify with /icp-prompt-builder (do not skip)

After collecting the engager CSV, sample ~50 rows and run /icp-prompt-builder before scaling further. LinkedIn engagement alone doesn't guarantee ICP fit — someone who reacted to a competitor's post might be a peer, a candidate, a student, or an actual buyer. Qualifying filters separates them.

Why required: unfiltered engager lists typically have 30-50% non-ICP rows (fans, peers, recruiters, students). Running /icp-prompt-builder on a 50-row sample, tuning the qualification prompt, then applying it to the full list cuts wasted enrichment + send costs. Takes 10-15 min.

Setup (First Time Only)

Before running, ensure these environment variables are set in ~/.env:

RAPIDAPI_KEY=<your key from https://rapidapi.com/apibuilderz/api/realtime-linkedin-bulk-data>
OPENROUTER_API_KEY=<your key from https://openrouter.ai/keys>

Test your credentials:

npm run competitor-engagers -- --check-auth

Steps

  1. Parse the user's input. They must provide a website URL (e.g., "clay.com"). Ask for:

    • How many competitors to discover (default: 20)
    • Posts per company (default: 30)
    • Any specific competitor LinkedIn URLs to include

    If the user just says "run it" or provides only a URL, use all defaults.

  2. Run the auth preflight check:

    npm run competitor-engagers -- --check-auth
    

    If it fails, help the user set up their API keys.

  3. Run the main script:

    npm run competitor-engagers -- --url={domain} --competitors={count} --posts={postsPerCompany} {--extra-competitor=URL ...} --verbose
    
  4. The script prints live progress. It can take 30-120 minutes for a full run (20 competitors x 200 employees x engagement collection). If interrupted, re-run with --resume:

    npm run competitor-engagers -- --url={domain} --resume
    

Read the full file on GitHub · 104 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed 31c13de6e6d2
  2. 2mo ago First seen · 104 lines · 75 tokens per session scan A bf9ed8c002ef

Subscribe to this mod's changes

competitor-engagers is a skill published in the GitHub repository growthenginenowoslawski/coldoutboundskills (802 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 1,077 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

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