What is competitor-post-engagers?

A lead-finding tool that examines who reacts to and comments on a competitor’s LinkedIn posts. LinkedIn is a professional social network.

Use it with company-page URLs to select highly engaged recent posts, classify the people involved against an ideal-customer profile, and export them as a CSV file.

It helps find potential customers already showing interest in a competitor’s content, instead of searching for leads without that signal.

competitor-post-engagers — Skill by gooseworks-ai/gooseworks-skills

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 gooseworks-ai/gooseworks-skills --skill competitor-post-engagers

Clone the repo

git clone --depth 1 https://github.com/gooseworks-ai/gooseworks-skills

Made for: Claude Code, Codex.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/gooseworks-ai/gooseworks-skills/competitor-post-engagers/github.svg)](https://agentmods.dev/skills/gooseworks-ai/gooseworks-skills/competitor-post-engagers)

Your own site

<a href="https://agentmods.dev/skills/gooseworks-ai/gooseworks-skills/competitor-post-engagers"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/gooseworks-skills/competitor-post-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/gooseworks-ai/gooseworks-skills/competitor-post-engagers"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/gooseworks-skills/competitor-post-engagers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,893 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.

Origin unknown 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.

Model	Per session	Once invoked
Fable 5.1	$0.00086	$0.01893
Opus 5.5	$0.00034	$0.00757
Sonnet 5.5	$0.00017	$0.00379
Haiku 4.5	$0.00009	$0.00189

Measured 2mo ago against content hash 025cdb649621, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

competitor-post-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 2mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/competitor_post_engagers.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/capabilities/competitor-post-engagers/SKILL.md · 169 lines

The source is not reproduced here

No licence file

Read it on GitHub

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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.

2mo ago First seen · 169 lines · 86 tokens per session scan A 025cdb649621

Subscribe to this mod's changes

competitor-post-engagers is a skill published in the GitHub repository gooseworks-ai/gooseworks-skills (12 stars, last pushed 7mo ago), with no licence file. It adds 86 tokens to every session and 1,893 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 2026-08-31.

Marketing and SEO lead-generation

competitor-post-engagers next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
competitor-post-engagersgooseworks-ai/gooseworks-skills	Skill	12	7mo ago	none	A	86	Claude Code, Codex
squeeze-page-builderAffitor/affiliate-skills	Skill	767	18d ago	MIT	A	114	Claude Code
deepline-ads-audiencesgetaero-io/gtm-eng-skills	Skill	73	yesterday	MIT	A	133	Claude Code, Codex
deepline-enginegetaero-io/gtm-eng-skills	Skill	73	yesterday	MIT	A	43	Claude Code, Codex
deepline-plays-reviewgetaero-io/gtm-eng-skills	Skill	73	yesterday	MIT	A	121	Codex
b2b-marketing-playbookGingiris-1031/gingiris-skills	Skill	89	2d ago	MIT	A	53	Claude Code, Codex
deepline-crm-cleaninggetaero-io/gtm-eng-skills	Skill	73	yesterday	MIT	A	73	Claude Code, Codex