replace-skill-name

name	[REPLACE: SKILL_NAME]
description	Mention/keyword sweep on social platforms for [REPLACE: KEYWORDS] — trends, sentiment, top posts

${var} — Optional. Pass alternative keywords (comma-separated) to override the default. If empty, monitors [REPLACE: KEYWORDS].

Today is ${today}. Monitor social mentions of [REPLACE: KEYWORDS] and produce a summary.

Steps

1. Resolve keywords — KEYWORDS="${var:-[REPLACE: KEYWORDS]}". Split on commas, trim each, lower-case. Each token becomes its own search query.

2. Search X — for each keyword, use the X / xAI search path (project's standard pattern):

   # Uses XAI_API_KEY in-run via ./secretcurl (the key is injected via requires:).
   # Mirror the fetch-tweets skill: POST https://api.x.ai/v1/responses with
   #   ./secretcurl -H "Authorization: Bearer {XAI_API_KEY}" -d @/tmp/payload.json
   # WebFetch (or a Nitter mirror) is the last-resort fallback.
   

   Restrict to language [REPLACE: LANGUAGE] (e.g. en, fr, any). Drop posts with fewer than [REPLACE: MIN_LIKES] likes — that filter is what protects the channel from low-signal noise.

3. Search Reddit — for each keyword:

   # Reddit's keyless JSON endpoint. WebFetch fallback if curl fails (sandbox).
   curl -sf "https://www.reddit.com/search.json?q=$KEYWORD&t=day&restrict_sr=0" \
     -H "User-Agent: aeon/1.0" > .reddit-cache.json || \
     echo "use WebFetch on https://www.reddit.com/search.json?q=$KEYWORD&t=day"
   

4. Score and pick top 5 per platform — score on engagement (likes, comments, score) × recency (last 24h gets full marks). Drop reposts and obvious bot accounts (handles like *_bot, account age < 7 days with > 100 posts).

5. Tag sentiment — for the top 10 posts overall, label each positive / neutral / negative based on tone of the post text. Keep this lightweight — one-token classification, no nested reasoning.

6. Write output/articles/[REPLACE: SKILL_NAME]-${today}.md:

   # [REPLACE: KEYWORDS] — ${today}
   
   ## Volume
   - X: N posts (vs 7d avg M)
   - Reddit: N posts (vs 7d avg M)
   
   ## Sentiment
   positive: X · neutral: Y · negative: Z
   
   ## Top posts
   1. [Author · platform · timestamp]
      "Excerpt or paraphrase."
      → URL
   
   2. ...
   

7. Notify via ./notify with a 2-3 line summary: *[REPLACE: KEYWORDS] — ${today}* · N posts · sentiment skews positive/negative · top: <one-line title>. Full digest: <url>. Silent on quiet days (volume < 25% of 7d average AND no negative-sentiment spike).

8. Log to memory/logs/${today}.md:

   ## [REPLACE: SKILL_NAME]
   - **Volume**: x_posts=N, reddit_posts=N, vs_7d_avg=Δ%
   - **Sentiment**: pos=X, neu=Y, neg=Z
   - **Status**: SOCIAL_OK | SOCIAL_QUIET | SOCIAL_SPIKE (vol > 2x avg) | SOCIAL_DEGRADED
   

Network note

X / xAI requires XAI_API_KEY; a bare $XAI_API_KEY on a curl line is refused by the Bash analyzer, so call ./secretcurl with the {XAI_API_KEY} placeholder (the key is injected via requires:). Reddit's JSON endpoint is keyless but rate-limited per IP — WebFetch is the fallback when curl returns 429.

Constraints

• Bot filter is critical. New accounts with high posting velocity dominate any keyword and are almost always inauthentic. Strict drop.
• Volume is more honest than sentiment. A SPIKE (volume > 2x 7d avg) is a real signal; sentiment shifts within normal volume often aren't.
• Engagement filters scale. MIN_LIKES = [REPLACE: MIN_LIKES] is a starting threshold — raise it as the topic gains attention so noise stays out.