mention-radar

mention-radar is a skill for Claude Code, Codex from aaronjmars/aeon-agent. It costs 31 tokens per session (2,763 once invoked), scanned A, a copy of mention-radar, MIT.

A monitoring workflow that searches websites and social networks for mentions of your active projects. It highlights what people are finding, misunderstanding, and discussing.

In plain words
What is it for?
Tracking project names, domains, and GitHub repositories across sources such as X, Reddit, Hacker News, and the web, then finding places where you may want to respond.
Why use it?
It removes the need to search several sources manually and helps avoid reviewing the same mentions repeatedly.

Skill for Claude CodeCodex

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

Good fit Tracking project names, domains, and GitHub repositories across sources such as X, Reddit, Hacker News, and the web, then finding places where you may want to respond.

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

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 mention-radar

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/aaronjmars/aeon-agent/mention-radar"><img src="https://agentmods.dev/badge/skills/aaronjmars/aeon-agent/mention-radar.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,763 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod 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 $0.00031 $0.02763
Opus 5 $0.00015 $0.01381
Sonnet 5 $0.00006 $0.00553
Haiku 4.5 $0.00003 $0.00276

Measured 5d ago against content hash 41875dfe0e27, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

mention-radar scanned grade A with 1 finding 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 5d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

`XAI_API_KEY` is **injected into your environment** for this skill (declared in `requires:`). It is present and valid. **The primary fetch path for X/Twitter mentions is a direct `curl` to `https://api.x.ai/v1/responses`
Origin

This is a copy

100% identical to mention-radar — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/mention-radar/SKILL.md · 141 lines

How it starts

The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.

${var} — Comma-separated project names to track (e.g. "MyApp, my-lib"). If empty, derives targets from MEMORY.md and memory/topics/projects.md.

Read memory/MEMORY.md for current project status. Read the last 3 days of memory/logs/ to avoid re-surfacing already-noted mentions.

Steps

  1. Define the targets.

    • If ${var} is set: parse it as a comma-separated list of project names.
    • Otherwise: scan memory/MEMORY.md (goals, active topics) and memory/topics/projects.md (if it exists) for the operator's active projects. A target needs at least a name; collect a site/domain and a GitHub owner/repo too when known.
    • Cap at 6 targets — prefer the most active ones.
    • If zero targets can be derived: log MENTION_RADAR_SKIP: no projects configured — set var or add projects to memory/topics/projects.md and stop. No notification.

    For each target, build search terms:

    • The exact project name in quotes (e.g. "MyApp" site:x.com OR site:reddit.com OR site:news.ycombinator.com)
    • The domain if known (e.g. "myapp.xyz")
    • The repo if known (e.g. site:github.com owner/myapp)
  2. Search for external mentions. X/Twitter is fetched via the X.AI Responses API (primary); the rest of the public web (Reddit, Farcaster, blogs, newsletters, GitHub Discussions, HN, Product Hunt) goes through WebSearch, which is also the last-resort fallback for X itself. Derive the operator's handle from soul/SOUL.md if present (call it $OPERATOR) so you can exclude their own posts.

    Path A — X.AI API (primary, X/Twitter mentions). For each target, ask Grok's x_search who is talking about the project on X. See the Fetching contract below — attempt this whenever the key is present, set the Bash tool timeout to ≥180000, and capture the HTTP status. Use a unique tmp filename per target if you loop (e.g. /tmp/xai-mr-$SLUG.json). $NAME/$DOMAIN/$REPO come from the target built in step 1 ($DOMAIN/$REPO may be empty — leave them out if so):

    FROM_DATE=$(date -u -d "7 days ago" +%Y-%m-%d 2>/dev/null || date -u -v-7d +%Y-%m-%d)
    TO_DATE=$(date -u +%Y-%m-%d)
    PROMPT="Search X for posts by OTHER people mentioning the project \"${NAME}\" (also its site ${DOMAIN} and repo ${REPO} when given), posted between ${FROM_DATE} and ${TO_DATE}. Exclude posts by the operator @${OPERATOR} and by the project's own accounts. For each mention return: @handle, the full post text, date, exact engagement counts (likes, retweets, replies; 0 if unknown), the poster's approximate follower count if visible, and the direct link https://x.com/handle/status/ID. Prioritize people discovering it for the first time, asking confused questions, hitting friction (setup/docs/missing feature), comparing it to a competitor, or requesting a feature. Return a numbered list; if nobody is talking about it, say so explicitly."
    jq -n --arg p "$PROMPT" --arg fd "$FROM_DATE" --arg td "$TO_DATE" \
      '{model:"grok-4.6", input:[{role:"user",content:$p}], tools:[{type:"x_search",from_date:$fd,to_date:$td}]}' \
      > /tmp/xai-mr-payload.json
    HTTP=$(./secretcurl -s -o /tmp/xai-mr.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer {XAI_API_KEY}" \
      -d @/tmp/xai-mr-payload.json)
    echo "xai http=$HTTP bytes=$(wc -c </tmp/xai-mr.json)"
    

    On HTTP=200 with a non-empty body, parse /tmp/xai-mr.json with jq -r '.output[] | select(.type == "message") | .content[] | select(.type == "output_text") | .text' and feed the X mentions into categorization (step 4). Record X_SOURCE=api.

    Path B — WebSearch (broader web + X fallback). Always use WebSearch for the non-X surfaces — Reddit, Farcaster, personal blogs, newsletters, GitHub Discussions, HN, Product Hunt:

    • Try both brand name and URL variants
    • Time-box to last 7 days where the search engine supports it
    • Skip results from the operator's own accounts and the project's own repos

Read the full file on GitHub · 141 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. 5d ago First seen · 141 lines · 31 tokens per session scan A 41875dfe0e27

Subscribe to this mod's changes

mention-radar is a skill published in the GitHub repository aaronjmars/aeon-agent (11 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 2,763 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to mention-radar, differing in 0 lines, and is treated as a copy.

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