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.
npx skills add swan-gtm/gtm-skills --skill community-radargit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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.
[](https://agentmods.dev/skills/swan-gtm/gtm-skills/community-radar)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00036 | $0.01635 |
| Opus 5 | $0.00018 | $0.00817 |
| Sonnet 5 | $0.00007 | $0.00327 |
| Haiku 4.5 | $0.00004 | $0.00163 |
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.
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. |
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.
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.
- 9d ago First seen · 115 lines · 36 tokens per session scan A 2ff0adbe3f53
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.
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