community-radar

community-radar is a skill for Claude Code, Codex from swan-gtm/gtm-skills. It costs 36 tokens per session (1,635 once invoked), scanned A, original, MIT.

A monitoring workflow for public mentions of a brand, competitors, or phrases that describe relevant customer problems. It labels the tone of each mention and prepares short replies for the appropriate person.

In plain words
What is it for?
Use it to monitor LinkedIn, X, Reddit, Hacker News, or other configured sources, classify sentiment, and draft human follow-up.
Why use it?
It helps teams notice relevant conversations that would otherwise be missed across public communities and social platforms. It also supports a consistent response process.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to monitor LinkedIn, X, Reddit, Hacker News, or other configured sources, classify sentiment, and draft human follow-up.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/swan-gtm/gtm-skills/community-radar
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 swan-gtm/gtm-skills --skill community-radar
Clone the repo
git clone --depth 1 https://github.com/swan-gtm/gtm-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.

agentmods badge for community-radar

README.md
[![agentmods](https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/community-radar/github.svg)](https://agentmods.dev/skills/swan-gtm/gtm-skills/community-radar)
Your own site
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/community-radar"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/community-radar/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 community-radar

Your own site · 80×15
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/community-radar"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/community-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,635 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 $0.00036 $0.01635
Opus 5 $0.00018 $0.00817
Sonnet 5 $0.00007 $0.00327
Haiku 4.5 $0.00004 $0.00163

Measured 9d ago against content hash 2ff0adbe3f53, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

community-radar 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 9d 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.

skills/ido-goldberg/community-radar/SKILL.md · 115 lines

How it starts

The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Instructions

Setup state. Not yet configured for this org. Load the Setup sub-page and walk the user through wiring the community-radar trigger (SCHEDULE + Apify search actors across LinkedIn / X / Reddit / Hacker News, or WEBHOOK from a social listening tool the user already pays for) with the org's brand-and-pain-language keyword list, the platforms to monitor, and the follow-up sequence before running this play. (After setup is performed, rewrite this paragraph via swan-update-skill to describe the current state — trigger type chosen, keywords / brand terms / pain-language phrases monitored, platforms covered, sequence wired, success metric, and last-refreshed date — so future runs see the current configuration without re-checking.)

When this fires

A SCHEDULE trigger running an Apify search actor (LinkedIn search, X / Twitter search, Reddit search, Hacker News scrape) surfaces new mentions of the configured keywords. Or a WEBHOOK from a social listening service the user already pays for (Brand24, Mention, Hootsuite, Triggify, custom Reddit / X monitoring) pushes mentions in. Payload includes: platform, author, mention text, post URL, engagement metrics on the parent post.

Note: LINKEDIN_ENGAGEMENT is not the right trigger here — that one follows specific LinkedIn profiles. For keyword-driven brand-mention sweeps across LinkedIn, use SCHEDULE + an Apify LinkedIn-search actor.

The window is short on public social — 24-48 hours feels reasonable; > 1 week and the reply looks bot-driven.

Step 1 — Classify the mention

Class Pattern Right move
Direct praise "We love [your product]" Like, optional thank-you reply. Resharable.
Customer Q / mild frustration "How do I do X in [your product]?" Helpful reply from support handle. Resolve the question.
Public complaint "[Your product] is broken / disappointing" Acknowledge, DM to take offline, don't argue publicly.
Comparison shopping "Looking at [you] vs [competitor]" Soft entry; offer to help with the eval. Don't trash competitor.
Competitor switch signal "Just switched off [competitor]" + same thread mentions you High-value lead; warm DM.
Pain mention (your wedge, no brand) "Why can't I find a tool for X" Soft helpful reply. Don't pitch — offer perspective.
Generic noise / spam / off-topic Ignore.

Read the full file on GitHub · 115 lines

Files

What ships with it

1 file 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.

  1. 9d ago First seen · 115 lines · 36 tokens per session scan A 2ff0adbe3f53

Subscribe to this mod's changes

community-radar is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,635 once invoked, about $0.0002 per session on Opus 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 2026-09-03.