fetch-tweets

type	Skill
name	Fetch Tweets
category	basics
description	Search and curate X/Twitter behind one selector - keyword, topic roundup, a single or tracked-account digest, an X list, or the AI-agent buzz preset - clustered into signal-scored sub-narratives.
var
tags	["social"]
requires	["XAI_API_KEY?"]

${var} — <source>:<arg> where <source> ∈ keyword | topic | account | list | agent-buzz. The <arg> is source-specific (a query, a topic, a handle, comma-separated list IDs, or an optional focus). If no source: prefix is given, the source is inferred from the shape of <arg> (see Source selector). Required for keyword and list; optional for topic, account, and agent-buzz.

Today is ${today}. This skill fetches X/Twitter content along one of five source axes and produces a curated digest — clustered by sub-narrative, ranked by signal, one insight per item — never a flat chronological dump.

Source selector

Parse ${var} into SOURCE and ARG before doing anything else.

Explicit form (recommended): <source>:<arg>

• keyword:$SOL OR solana OR "solana network" — raw X search query, passed to Grok verbatim (OR/AND honored).
• topic:brain-computer interfaces — a single topic roundup. topic: (empty arg) → resolve a topic list from MEMORY.md, then built-in defaults.
• account:vitalikbuterin — one account's recent tweets. account: (empty arg) → digest every handle in memory/topics/tracked-accounts.yml.
• list:1953536336675365173,1937207796270829766 — one or more numeric X list IDs. Append |<topic> for a topic booster: list:195...,193...|AI agents.
• agent-buzz — the curated AI-agent-ecosystem preset. agent-buzz:MCP protocol prioritizes a project/topic within the preset.

Implicit form (back-compat with migrated bare-var configs): when ${var} has no recognized source: prefix, infer SOURCE in this order:

1. ${var} is empty → topic (default multi-topic roundup).
2. ${var} is all-digits, or comma-separated all-digits (optionally with a |<topic> suffix) → list.
3. ${var} is @handle or matches ^[A-Za-z0-9_]{1,15}$ (a bare handle) → account.
4. Anything else → keyword.

Note: agent-buzz has no distinct implicit shape (its arg looks like a keyword/topic), so it is only selectable via the explicit agent-buzz / agent-buzz:... prefix.

Once SOURCE and ARG are set, jump to the matching branch below. Only one branch runs per invocation.

Shared preamble (all branches)

1. Read memory/MEMORY.md for context and the recent memory/logs/ (each branch specifies its lookback window — 2 or 3 days) to dedup already-reported tweets.

2. Load the dedup set SEEN_TWEETS by unioning two sources:

   • The branch's persistent seen-file (per-mode path below), if it exists — read all URLs.
   • The branch's log lookback window — grep each memory/logs/*.md file in range for lines matching https://x.com/.

   Per-mode seen-files (kept at their legacy paths so dedup history survives the merge):

   mode	seen-file	log lookback
   keyword	memory/fetch-tweets-seen.txt	3 days
   topic	memory/tweet-roundup-seen.txt	3 days
   account	(logs only — see branch)	2 days
   list	memory/list-digest-seen.txt	2 days
   agent-buzz	(logs only — 3-day status/<id> set)	3 days

3. Formatting invariants shared by every branch's notification:

   • Use x.com/handle (never @handle) so Telegram doesn't ping/tag users. (Exception: the account-digest and agent-buzz formats below historically use @handle in-body; keep their documented format but prefer x.com/handle when practical.)
   • Every surviving tweet gets a tappable Markdown link — [View](url) / [View tweet](url). If a URL is unavailable, drop the link and say "(link unavailable)".
   • Never fabricate engagement counts. Missing → 0, not a guess.
   • Notify only on signal. A legitimately empty or all-duplicate run logs its status and sends nothing.

Voice

Used by the account and agent-buzz branches for one-line takes/insights. If soul/SOUL.md and soul/STYLE.md are populated, read both and match the operator's voice. If they are empty templates or absent, write in a clear, direct, neutral tone — state what the tweet says, no hedging or editorializing beyond the tweet itself.

---

Branch: keyword (source:keyword)

Search X for tweets matching ARG and produce a curated digest grouped by sub-narrative.

Seen set: memory/fetch-tweets-seen.txt + last 3 days of logs (loaded in preamble).

1. Build the search prompt. Pass ARG to Grok verbatim as the query — do NOT narrow it to a single angle; broad coverage is the goal. Ask for at least 15–20 candidate tweets (you'll cull to ~7–10). Always require explicit engagement counts (likes, retweets, replies) so ranking is data-driven.

2. Fetch tweets. Record SOURCE_PATH=api|websearch for the log.

   Path A — X.AI API (primary; see the Fetching (all branches) contract — attempt this, set the Bash tool timeout ≥180000, capture the HTTP status):

   FROM_DATE=$(date -u -d "yesterday" +%Y-%m-%d 2>/dev/null || date -u -v-1d +%Y-%m-%d)
   TO_DATE=$(date -u +%Y-%m-%d)
   PROMPT="Search X for tweets about: ${ARG}. Date range: ${FROM_DATE} to ${TO_DATE}. Return at least 15-20 candidate tweets — mix of high-engagement posts and smaller accounts that add a distinct angle. For each tweet include: @handle, the full text, date posted, exact engagement counts (likes, retweets, replies — never N/A; if unknown, say 0), and the direct link (https://x.com/handle/status/ID). Return as a numbered list."
   jq -n --arg p "$PROMPT" '{model:"grok-4.3", input:[{role:"user",content:$p}], tools:[{type:"x_search"}]}' > /tmp/xai-ft-keyword.json
   HTTP=$(./secretcurl -s -o /tmp/xai.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-ft-keyword.json)
   echo "xai http=$HTTP bytes=$(wc -c </tmp/xai.json)"
   

   On HTTP=200, parse /tmp/xai.json with: jq -r '.output[] | select(.type == "message") | .content[] | select(.type == "output_text") | .text' and mark SOURCE_PATH=api.

   Path B — WebSearch fallback (only if the key is KEY_UNSET, or Path A gave a non-2xx / empty / timeout per the contract): use the built-in WebSearch tool with site:x.com "<query terms>" after:${FROM_DATE}. Note at the top of the log the true reason (http-<code> / timeout / empty, never "unavailable" when the key was set) and "results compiled via WebSearch — quality lower than usual". WebSearch favours high-engagement older tweets — prioritise results dated within the last 48 hours. Mark SOURCE_PATH=websearch.

3. Empty vs. error handling (distinguish):

   • Legitimate empty (0 tweets): log FETCH_TWEETS_EMPTY (source=${SOURCE_PATH}) and stop — no notification.
   • API/cache error (HTTP error, malformed JSON, all paths failed): log FETCH_TWEETS_ERROR (last_path=${SOURCE_PATH}, reason=...) and stop — no notification.

4. Deduplicate each candidate URL against SEEN_TWEETS. If ALL are dupes: log FETCH_TWEETS_NO_NEW: all results already reported and stop — no notification.

5. Curate (the core step): a. Cluster survivors into 2–4 sub-narratives by what they're claiming/discussing (e.g. for a token: "price action", "team announcement", "criticism/FUD", "ecosystem integration"). Name the angle, not the topic. b. Rank within each cluster by signal (not raw engagement): signal = likes + 2×retweets + replies, but demote pure replies, generic shilling, and near-duplicate paraphrases. Drop tweets with <5 total engagement unless they add a unique angle. c. Cap each cluster at 2–3 tweets, total 7–10. Quality over quantity — if only 5 pass, send 5. Don't pad. d. Extract the claim/signal per tweet — what's new or interesting, not a literal paraphrase. Bad: "User says token is going up." Good: "Calls out the team's silence on the postponed unlock — first major holder to do so publicly." e. Compute a one-line signal for the top of the notification — one observation about the shape of the conversation (e.g. "Sentiment split — 4 bullish on the launch, 3 critical of the unlock terms.").

6. Save + update seen-file (see Log). Append each kept tweet URL (one per line) to memory/fetch-tweets-seen.txt (create if missing).

7. Notify via ./notify with the clustered output:

   *Top Tweets — ${ARG} (${today})*
   _${signal_one_liner}_
   
   *${cluster_1_name}*
   1. x.com/handle — [insight summary]
   Likes: X | RTs: Y | Replies: Z
   [View tweet](https://x.com/handle/status/ID)
   
   2. x.com/handle — [insight summary]
   Likes: X | RTs: Y | Replies: Z
   [View tweet](https://x.com/handle/status/ID)
   
   *${cluster_2_name}*
   3. x.com/handle — [insight summary]
   ...
   

   The signal one-liner is italic (_..._) directly under the title; cluster headers are *bold*.

Status codes: FETCH_TWEETS_OK (notified) | FETCH_TWEETS_EMPTY | FETCH_TWEETS_ERROR | FETCH_TWEETS_NO_NEW.

---

Branch: topic (source:topic)

Gist of the latest X chatter on one or more configurable topics.

Seen set: memory/tweet-roundup-seen.txt + last 3 days of logs.

1. Resolve the topic list (priority order):

   1. ARG set → TOPICS=("$ARG") (single-topic mode).
   2. Else if MEMORY.md has a ## Tweet Roundup Topics section → use its bulleted lines, one query per line.
   3. Else built-in defaults:
      • artificial intelligence OR AI agents OR LLM
      • crypto OR bitcoin OR DeFi
      • technology OR startups OR open source

2. Fetch per topic — track SOURCE ∈ {api, websearch, failed} per topic.

   Path A — direct X.AI curl (primary): for each topic, call Grok's x_search.

   FROM_DATE=$(date -u -d "yesterday" +%Y-%m-%d 2>/dev/null || date -u -v-1d +%Y-%m-%d)
   TO_DATE=$(date -u +%Y-%m-%d)
   PROMPT="Search X for recent tweets about: ${TOPIC}. Date range: ${FROM_DATE} to ${TO_DATE}. Return up to 8 substantive tweets. For each: @handle, full text, date, exact engagement counts (likes, retweets, replies; 0 if unknown), and the direct link https://x.com/handle/status/ID."
   jq -n --arg p "$PROMPT" '{model:"grok-4.3", input:[{role:"user",content:$p}], tools:[{type:"x_search"}]}' > /tmp/xai-ft-topic.json
   ./secretcurl -s -o /tmp/xai-topic-out.json -X POST "https://api.x.ai/v1/responses" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer {XAI_API_KEY}" \
     -d @/tmp/xai-ft-topic.json
   

   Parse with the standard jq extractor. If it yields text, SOURCE=api. Extract each tweet's @handle, text, engagement counts, and permalink.

   Path B — WebSearch fallback (only if XAI_API_KEY unset, or Path A errors/empty): site:x.com "<topic keywords>" after:<YESTERDAY>. Always include the word "today" and ${today} to force fresh results. Discard any result whose visible date is older than 48h. Collect up to 5 candidates per topic. Mark SOURCE=websearch. If both paths return nothing, mark SOURCE=failed.

3. Score and filter. Require: a known @handle; a https://x.com/<handle>/status/<id> URL (if missing, keep but mark "link unavailable"); posted within 48h; URL not in SEEN_TWEETS. Compute signal_score = likes + 2×retweets + replies (on WebSearch path with no counts, use result rank as a weak proxy). Demote −50%: replies to a parent tweet; near-duplicates of a higher-scoring tweet (>70% text overlap or same linked URL).

4. Curate per topic:

   • 0 survivors → drop the topic. Do NOT pad.
   • 1–3 survivors → list ranked by signal_score, highest first.
   • 4+ survivors → group into 2–3 sub-narratives (shared keywords/entity/claim); label each, surface the top-1 tweet per narrative as exemplar. Write an insight per reported tweet (what it asserts/reveals, not a headline paraphrase). Write a one-line conversation shape per topic ("bullish momentum, dissenters quiet", "split opinion on X's launch", "single story dominating — Y").

5. Notify. If every topic dropped: log TWEET_ROUNDUP_EMPTY and stop — no notify. Otherwise send via ./notify (≤4000 chars):

   *Tweet Roundup — ${today}*
   _Source: api:X websearch:Y failed:Z_
   
   *[Topic 1]* — _conversation shape_
   - x.com/handle — insight (signal: 12.3k) [View](https://x.com/handle/status/ID)
   - x.com/handle — insight (signal: 4.1k) [View](https://x.com/handle/status/ID)
   
   *[Topic 2]* — _conversation shape_
   - x.com/handle — insight (signal: 8k) [View](https://x.com/handle/status/ID)
   

   Show signal: <score> only when engagement counts were available (api path); omit silently on WebSearch.

6. Persist + log (see Log). Append each reported URL (one per line) to memory/tweet-roundup-seen.txt (create if missing).

Constraints: never notify an empty roundup (silence beats filler); never @handle anyone; never report a URL already in SEEN_TWEETS. Status codes: TWEET_ROUNDUP_OK | TWEET_ROUNDUP_EMPTY.

---

Branch: account (source:account)

Two sub-modes: single handle (decision-ready gist of one account) vs. all tracked accounts (theme-grouped digest of a watchlist). Choose by ARG.

Seen set: last 2 days of logs — extract every https://x.com/ URL under a prior ### fetch-tweets account entry into SEEN_URLS.

account — single handle (ARG is one @handle)

1. Normalize ARG. Strip leading @, https://x.com/, https://twitter.com/, https://nitter.net/, trailing slash / /status/.... Lowercase. Reject if empty, contains whitespace, or >15 chars. On reject → REFRESH_X_NO_VAR: send ./notify "fetch-tweets: REFRESH_X_NO_VAR — set an X handle" and exit 0. Store the cleaned handle as ACCOUNT.

2. Load tweets:

   • Path A — X.AI API (primary): search this account's recent tweets via Grok's x_search.

     PROMPT="Search X for the latest tweets, replies, and quote tweets from @${ACCOUNT} in the last 2 days. Return each with full text, timestamp, type (original|reply|quote), what it replies to/quotes if any, exact engagement counts (likes, retweets, replies; 0 if unknown), and the permalink https://x.com/${ACCOUNT}/status/ID. Skip retweets of others. Return chronological."
     jq -n --arg p "$PROMPT" '{model:"grok-4.3", input:[{role:"user",content:$p}], tools:[{type:"x_search"}]}' > /tmp/xai-ft-account.json
     ./secretcurl -m 30 -s -o /tmp/xai-account-out.json -X POST "https://api.x.ai/v1/responses" \
       -H "Content-Type: application/json" \
       -H "Authorization: Bearer {XAI_API_KEY}" \
       -d @/tmp/xai-ft-account.json
     

     Parse with the standard jq extractor. Record source=api.
   • Path B — WebFetch fallback (only if XAI_API_KEY unset, or Path A errors / parsed text has zero x.com status URLs): WebFetch https://x.com/${ACCOUNT} with prompt: "List every tweet, reply, and quote tweet visible on this profile with its full text, timestamp, engagement counts (likes/retweets/replies) if shown, and the permalink https://x.com/handle/status/ID. Return a chronological list." Record source=webfetch.
   • Path C — degraded: if XAI_API_KEY unset and WebFetch returns nothing → skip to step 8 with status REFRESH_X_NO_API_KEY (key missing) or REFRESH_X_ERROR (key set but both paths failed).

3. Parse into structured tweets: url, text, timestamp, type (original/reply/quote), reply_to, quoted_text, likes, retweets, replies. Drop retweets of others. Missing counts → 0. Compute signal_score = likes + 2*retweets + replies − (3 if type=reply else 0).

4. Dedup and gate: drop any tweet whose url is in SEEN_URLS (deduped_count). If fewer than 3 tweets survive AND no thread is detectable (step 5) → skip to step 8 with REFRESH_X_NO_NEW (everything deduped) or REFRESH_X_EMPTY (account posted nothing).

5. Detect threads: a thread = 2+ tweets by ACCOUNT within 30 minutes where later tweets reply to earlier ones OR share ≥2 meaningful keywords with the opener. Thread tweets are atomic units regardless of individual score. Record {opener_url, tweet_count, combined_signal}.

6. Cluster and extract insights: group survivors (threads = one unit) into 2–4 sub-narratives by topic overlap; if <2 emerge, use one cluster. Per cluster: Title (3–8 words), Top tweet(s) (1–3 excerpts ≤200 chars each, with permalink + engagement), Insight (one sentence — what the cluster reveals about the author's stance/claim/shift; not a paraphrase — if you can't beat paraphrase, drop the cluster). Per thread: a 1–2 sentence landing summary + opener URL.

7. Write the verdict (pick exactly one) + a ≤20-word lede:

   Verdict	When
   ANNOUNCEMENT	launch, hire, policy, or product drop
   ARGUMENT	majority signal from contrarian takes or fights
   BUILDING	ships/code/tech-progress clusters dominate
   SHITPOST	jokes, memes, low-stakes banter dominate
   CONTEXT	mostly reacting to a news cycle, not driving one
   QUIET	<3 originals and no thread

8. Save gist (see Log). On empty/no-new/error/no-var statuses, write only the account header + status footer, skip cluster sections.

9. Update MEMORY.md (conditional): only if a cluster carries an announcement, specific claim, named project, or stance shift — add one bullet under a ## Tracked X Accounts section (create if missing): - @ACCOUNT YYYY-MM-DD: [one-sentence claim] — [permalink]. No paraphrases/memes/generic opinions.

10. Notify via ./notify. On REFRESH_X_OK:

    x refresh — @ACCOUNT ([VERDICT])
    [lede]
    top cluster: [title] — "[≤80 char excerpt]" ([likes]❤)
    [N tweets, T threads, K deduped]
    

    On REFRESH_X_EMPTY / REFRESH_X_NO_NEW: skip notify (write the log entry only). On REFRESH_X_NO_API_KEY / REFRESH_X_ERROR / REFRESH_X_NO_VAR: notify with the status code + a one-line hint (e.g. "fetch-tweets: REFRESH_X_NO_API_KEY — set XAI_API_KEY in workflow secrets").

Constraints: never fabricate engagement; never include a SEEN_URLS URL; an insight that only paraphrases is not an insight (drop the cluster); MEMORY.md updates are one line each. Status codes: REFRESH_X_OK | REFRESH_X_EMPTY | REFRESH_X_NO_NEW | REFRESH_X_NO_API_KEY | REFRESH_X_ERROR | REFRESH_X_NO_VAR.

account — all tracked accounts (ARG empty)

Use this to answer "what did these specific people post" across a watchlist.

1. Read config memory/topics/tracked-accounts.yml. If missing or accounts: [] → log TWEET_DIGEST_NO_CONFIG and exit (no notification). Schema:

   accounts:
     - handle: vitalikbuterin
       why: ethereum core thinking      # optional — grouping/context label
     - handle: balajis
       why: macro + tech narratives
   

2. Fetch recent tweets per account. For each handle:

   • Path A — live curl (primary, XAI_API_KEY is injected and set):

     PROMPT="Search X for the latest tweets from:${HANDLE} in the last 3 days. Return the 5 most interesting or substantive tweets. For each: full text, date, direct link (https://x.com/${HANDLE}/status/ID). Skip retweets of others."
     jq -n --arg p "$PROMPT" '{model:"grok-4.3", input:[{role:"user",content:$p}], tools:[{type:"x_search"}]}' > /tmp/xai-ft-acct1.json
     ./secretcurl -m 30 -s -o /tmp/xai-acct1-out.json -X POST "https://api.x.ai/v1/responses" \
       -H "Content-Type: application/json" \
       -H "Authorization: Bearer {XAI_API_KEY}" \
       -d @/tmp/xai-ft-acct1.json
     

     Parse with the standard jq extractor. If XAI_API_KEY is unset, log TWEET_DIGEST_NO_KEY: skill requires XAI_API_KEY and exit (no notification). Dedup: drop any candidate URL already in SEEN_URLS (last 2 days of logs).

3. Group by theme, not by account. Walk the full candidate set; identify 2–4 themes (e.g. "L2 design decisions", "macro / rates", "AI model releases", "regulation"). Each tweet maps to one theme; a why: label can seed theme naming for single-topic feeds.

4. Write a one-sentence take per notable tweet — what the tweet says, not your opinion of it. Voice per the Voice section.

5. Notify via ./notify:

   *Tweet Digest — ${today}*
   
   *Theme: <theme>*
   @handle: <one-sentence summary> — [link](url)
   @handle: <one-sentence summary> — [link](url)
   
   *Theme: <theme>*
   ...
   

   If no notable tweets across all accounts: log TWEET_DIGEST_OK and end (no notification).

Status codes: TWEET_DIGEST_OK (notified or clean) | TWEET_DIGEST_NO_CONFIG | TWEET_DIGEST_NO_KEY.

---

Branch: list (source:list)

Cross-list narrative resonance + signal-scored top tweets from tracked X lists in the past 24h. Lists are curator signal — the value is cross-list resonance + insight + a verdict, not a flat top-N-per-list dump.

Seen set: memory/list-digest-seen.txt + last 2 days of logs.

1. Parse and validate ARG.

   if [ -z "$ARG" ]; then
     echo "LIST_DIGEST_NO_CONFIG: var must contain at least one X list ID" \
       >> "memory/logs/$(date -u +%Y-%m-%d).md"
     exit 0
   fi
   IDS_PART="${ARG%%|*}"
   TOPIC_FILTER=""
   [ "$ARG" != "$IDS_PART" ] && TOPIC_FILTER="${ARG#*|}"
   for LIST_ID in $(echo "$IDS_PART" | tr ',' ' '); do
     if ! [[ "$LIST_ID" =~ ^[0-9]+$ ]]; then
       echo "LIST_DIGEST_NO_CONFIG: invalid list ID '$LIST_ID' (must be numeric)" \
         >> "memory/logs/$(date -u +%Y-%m-%d).md"
       exit 0
     fi
   done
   

   If XAI_API_KEY is unset, fall back to Path B. If no path returns data, log LIST_DIGEST_NO_CONFIG: XAI_API_KEY required and stop without notifying.

2. Fetch each list's top tweets (past 24h) — API primary, WebSearch fallback. Path A — X.AI Responses API (primary):

   FROM_DATE=$(date -u -d "yesterday" +%Y-%m-%d 2>/dev/null || date -u -v-1d +%Y-%m-%d)
   TO_DATE=$(date -u +%Y-%m-%d)
   PROMPT="Look at X list https://x.com/i/lists/${LIST_ID}. Step 1: report the list name and a one-line description. Step 2: identify the most engaging tweets posted by members of this list between ${FROM_DATE} and ${TO_DATE} UTC. Return the top 12 tweets ranked by engagement (likes, retweets, replies). For EACH tweet you MUST return: (a) @handle, (b) the full tweet text (not a paraphrase), (c) explicit engagement counts as separate fields — likes:N, retweets:N, replies:N, views:N if available, (d) the direct permalink in the form https://x.com/<handle>/status/<id>, (e) media type (image|video|none), (f) one-line context if it's a reply or quote tweet (who/what). Skip retweets of accounts NOT on this list. If a tweet has an image and you can analyze it, include a one-line image description."
   jq -n --arg p "$PROMPT" --arg fd "$FROM_DATE" --arg td "$TO_DATE" \
     '{model:"grok-4.3", input:[{role:"user",content:$p}], tools:[{type:"x_search", from_date:$fd, to_date:$td, enable_image_understanding:true}]}' \
     > /tmp/xai-ft-list.json
   ./secretcurl -s -o /tmp/xai-list-out.json --max-time 180 -X POST "https://api.x.ai/v1/responses" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer {XAI_API_KEY}" \
     -d @/tmp/xai-ft-list.json
   

   Parse with the standard jq extractor. Path B — WebSearch fallback (only if XAI_API_KEY unset, OR Path A errors / returns nothing): site:x.com "i/lists/${LIST_ID}" OR list:${LIST_ID} after:${FROM_DATE}. Lower quality; mark this list's source as websearch. Per-list outcome: ok (≥3 tweets) | quiet (1–2) | empty (0, list found but no posts) | error (API/access failure — note reason).

3. Build the candidate pool. Record per tweet {handle, text, likes, retweets, replies, views, url, list_ids_seen_on:[], list_names_seen_on:[], media, is_reply, is_quote}. Dedup by URL across lists — same tweet on multiple lists → merge records, keep both list_ids_seen_on and list_names_seen_on (cross-list appearance is a signal). Dedup against history — drop URLs in memory/list-digest-seen.txt or the last 2 days of logs.

4. Score every candidate (natural-log engagement to stop one viral tweet dominating):

   base = ln(1+likes) + 2.0*ln(1+retweets) + 1.5*ln(1+replies)
   bonuses:
     +2.0  appeared on ≥2 distinct lists (cross-list resonance)
     +1.5  appeared on ≥3 distinct lists
     +1.0  topic_filter set AND tweet text/context matches (case-insensitive substring or obvious semantic match)
     +0.5  small-account-signal (≤25k followers per Grok's note OR no follower data + technical/insider content)
     +0.3  media is image OR video
   penalties:
     -1.0  is_reply AND replied-to NOT on any tracked list
     -0.5  pure link share with <10 words of original commentary
   score = base + sum(bonuses) - sum(penalties)
   

5. Cluster into cross-list narratives when ALL hold: ≥2 tweets from ≥2 distinct lists; shared ≥2 substantive keywords/entities (proper nouns, project names, tickers, technical terms — ignore stop words); posted within the same 24h window. narrative score = sum of constituent tweet scores; narrative title ≤80 chars capturing what the cluster collectively says. Pick an anchor tweet (highest individual score) + up to 2 supporting. Cluster-count cap: if clustering yields <2 or >4 clusters, fall back to a flat ranked list with inline [cluster-name] labels (no "🔗 Cross-list narratives" section).

6. Compose the digest (cap 4000 chars): up to 3 narratives at top (by narrative score); then up to 5 standalone tweets per list (highest individual score, not already in a narrative); hard total cap 12 items — cut from the bottom of standalones. Insight discipline: every item needs a one-line so-what (implication, contrarian angle, missing number, deal-flow signal); a paraphrase must be rewritten. Quiet-list rule: if a list's top surviving tweet scores <2.0 (≈<8 likes raw), write a one-line "quiet day" for that list. Topic filter is a scoring booster (step 4), NOT a hard filter. Verdict line: one line at the very top capturing what today's lists collectively say.

7. Send the notification via ./notify, verbatim format (x.com/handle, [label](url)):

   *List Digest — ${today}*
   
   [VERDICT LINE — one line, ≤140 chars, plain text]
   
   🔗 *Cross-list narratives*
   1. *[narrative title]* — appeared on [List A] + [List B]
      x.com/handle: [insight, not paraphrase] (♥ likes, ↻ rt) — [View](url)
      x.com/handle2: [insight] (♥ likes, ↻ rt) — [View](url)
   
   2. *[narrative title]* — appeared on [List A] + [List C]
      ...
   
   *[List Name 1]*
   - x.com/handle — [insight] (♥ likes, ↻ rt) — [View](url)
   - x.com/handle — [insight] (♥ likes, ↻ rt) — [View](url)
   
   *[List Name 2]*
   - quiet day
   
   ---
   sources: list1=ok | list2=quiet | list3=error(no-access)
   status: LIST_DIGEST_OK
   

   If cross-list narratives is empty, drop that whole section. If every list is quiet/empty, send a single-line "List Digest — ${today} — quiet across all tracked lists" instead of padding.

8. Log and persist (see Log). Append every reported URL (one per line) to memory/list-digest-seen.txt (create if missing).

Exit taxonomy: LIST_DIGEST_NO_CONFIG (var empty/invalid OR no fetch path — log only) | LIST_DIGEST_EMPTY (every list 0 tweets OR all candidates already seen — log only) | LIST_DIGEST_PARTIAL (some lists succeeded/some failed — notify survivors, surface failures) | LIST_DIGEST_OK (≥1 fresh tweet — notify).

---

Branch: agent-buzz (source:agent-buzz)

A topic-filtered preset: a curated, narrative-aware read on what the AI-agent scene on X talked about in the last 24h. Curation, not aggregation — 6 high-signal tweets in 2 clusters beats 10 of mixed noise. ARG (optional) is a project/topic to prioritize.

Seen set: last 3 days of logs — extract every https://x.com/.../status/<id> already posted by this skill; treat those IDs as the dedup set.

1. Fetch candidates:

   FROM_DATE=$(date -u -d "1 day ago" +%Y-%m-%d 2>/dev/null || date -u -v-1d +%Y-%m-%d)
   TO_DATE=$(date -u +%Y-%m-%d)
   

   Path A — X.AI API (primary; the response for each tweet must include explicit engagement counts + follower count, or step 3 scoring can't run):

   PROMPT="Search X from ${FROM_DATE} to ${TO_DATE} for tweets in the AI-agents conversation: autonomous agents, agent frameworks, MCP / agent protocols, agent products, agent benchmarks, agent research papers. Return up to 40 candidates. For EACH candidate you MUST return: @handle, follower_count (integer or null), role_guess (builder|founder|researcher|investor|commentator|anon), one-line claim (what they actually said — not a paraphrase, the thesis), likes (int), retweets (int), replies (int), posted_at (ISO), direct_link (https://x.com/username/status/ID). Prefer builders/founders/researchers. Skip obvious engagement-farming threads (\"RT if you agree\", reply-guy pileons, giveaways)."
   jq -n --arg p "$PROMPT" --arg fd "$FROM_DATE" --arg td "$TO_DATE" \
     '{model:"grok-4.3", input:[{role:"user",content:$p}], tools:[{type:"x_search", from_date:$fd, to_date:$td}]}' \
     > /tmp/xai-ft-buzz.json
   ./secretcurl -s -o /tmp/xai-buzz-out.json -X POST "https://api.x.ai/v1/responses" \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer {XAI_API_KEY}" \
     -d @/tmp/xai-ft-buzz.json
   

   Parse with the standard jq extractor. Record source=xai. Path B — WebSearch fallback (only if XAI_API_KEY unset, or Path A errors/empty): forced-fresh query "AI agents twitter today ${today}" — discard anything >48h old, expect degraded metadata. Record source=websearch.

   If ARG is set, also issue a second call constrained to that topic with the same schema; merge results.

2. Skip-gates (before clustering) — drop any candidate matching ANY:

   • Dup: status/<id> already in the 3-day dedup set.
   • Engagement-farming: poll threads, "bookmark this", "drop a 🔥", reply-guy pileons with <follower_count/10 likes.
   • Self-promo only: pure product shill with no claim/benchmark/datapoint. Launch tweets OK IF they include a concrete capability claim or number.
   • Staleness: posted_at older than 30h.
   • Anon + low engagement: role_guess=anon AND (likes+retweets) < 200.

3. Signal scoring: signal = likes + 2*retweets + replies, then × 1.3 if role_guess ∈ {builder, founder, researcher}; × 0.7 if a pure hot-take with no concrete referent (no named project, number, paper, or bench); × 0.5 if near-duplicate of another survivor (keep the higher-scored one only).

4. Narrative clustering: group survivors into 2–4 narrative clusters — a cluster is a shared thesis, not a keyword ("MCP vendor lock-in debate", not "MCP"). Name each ≤5 words. If one cluster holds >60% of tweets, split it. A tweet fitting no cluster is dropped unless its signal is top-3 overall. Target: 2–4 clusters, 2–3 tweets each, 6–9 total (strictly ≤10).

5. Insight extraction — per tweet, a one-line insight (≤20 words): the actual claim/datapoint, not a paraphrase; if opinion, state what they're arguing against; if an announcement, state what's new vs. prior art (not "X launched"). Anti-hype lint — rewrite any insight containing: game-changing, revolutionary, mind-blowing, wild, huge, massive, unreal, insane, vague "AI agents are evolving", "the future of X".

6. Conversation-shape lead — one opening sentence (≤25 words) naming what the conversation was actually about ("Mostly protocol debate — MCP vs. A2A — with two concrete launches on the side."). If you can't characterize it honestly in one sentence, the clustering is wrong — redo step 4.

7. Notify via ./notify:

   *Agent Buzz — ${today}*
   _<conversation-shape one-liner>_
   
   **<Cluster 1 name>**
   • @handle — <insight>
     <link>
   • @handle — <insight>
     <link>
   
   **<Cluster 2 name>**
   • @handle — <insight>
     <link>
   
   <!-- _src: xai|websearch · candidates: N → kept: M_ -->
   

   Keep the footer — it's how future self-audits debug empty days. Never pad to hit 10. 6 good > 10 mid.

Status codes: AGENT_BUZZ_OK (≥1 cluster notified) | AGENT_BUZZ_EMPTY (fetch succeeded, nothing survived — send Agent Buzz — ${today}: quiet day, no survivors.) | AGENT_BUZZ_ERROR (all sources failed — notify Agent Buzz — ${today}: all sources failed (${error summary}). and log the per-source failure).

---

Log (all branches)

Append ONE entry per run to memory/logs/${today}.md under a single ### fetch-tweets heading (the health loop parses this shape). The first bullet is the discriminator naming the branch/mode that ran; the rest are branch-specific bullets. Always include the reported tweet URLs as bullets (for next-run dedup).

### fetch-tweets
- mode: <keyword|topic|account|list|agent-buzz>
- status: <STATUS_CODE for the branch that ran>
- source: <SOURCE_PATH / per-source counts / per-list outcome, as applicable>
- <branch-specific bullets — carry over each branch's fields:>
    - keyword:     signal one-liner; per-cluster URLs with `likes:N rts:N replies:N` + insight
    - topic:       `topics: [t1: N tweets, t2: 0 (dropped)]`; `source: cache:X websearch:Y failed:Z`
    - account(1):  Verdict + lede; Counts (N tweets, X orig/Y reply/Z quote, T threads, deduped K); Clusters; Threads; Vibe
    - account(all):themes covered; per-account tweet counts
    - list:        Lists tracked; Per-list `list1=ok(N) | list2=quiet(N) | list3=error`; Verdict; Narratives count
    - agent-buzz:  source used; candidates N → kept M; cluster names
- urls:
    - https://x.com/handle1/status/...
    - https://x.com/handle2/status/...

On empty/no-new/error/no-config statuses, write the ### fetch-tweets heading + mode: + status: bullets only (skip the detail sections) so skill-health still observes the run. After logging, update the branch's persistent seen-file where one exists (keyword / topic / list — see the seen-file table).

Output shape note

No chain consumes this skill's output as of this commit (no consume: [fetch-tweets] references). If a downstream chain step starts consuming it, emit a flat list of URLs before the clustered/branch output so consumers aren't broken by cluster or narrative headers.

Fetching (all branches)

XAI_API_KEY is injected into your environment for this skill (declared in requires:). It is present and valid. The primary fetch path in every branch is a direct curl to https://api.x.ai/v1/responses with Authorization: Bearer {XAI_API_KEY}. There is no network sandbox blocking this; earlier versions of this skill claimed there was — that is stale and wrong. Just make the call.

You MUST attempt the direct curl before any fallback. The rules:

1. Check, don't assume. Run [ -n "$XAI_API_KEY" ] && echo KEY_PRESENT || echo KEY_UNSET. If KEY_PRESENT (it will be), you are required to try Path A.
2. Allow enough time. The x_search call typically takes 30–120s (it searches X live). When you invoke the Bash tool for the curl, set the tool's timeout to at least 180000 (180s), and add --max-time 150 to the curl itself so it fails cleanly rather than hanging. A curl that is slow is not a missing key — do not treat a timeout as "key unavailable".
3. Capture the HTTP status so the fallback decision is based on fact, not assumption. Build the JSON body to a fixed file with jq -n first (as each branch above does), then send it with -d @file — every ./secretcurl command must be 100% literal (no $VAR, or the permission layer blocks it):

   HTTP=$(./secretcurl -s -o /tmp/xai.json -w '%{http_code}' --max-time 150 -X POST "https://api.x.ai/v1/responses" \
     -H "Content-Type: application/json" -H "Authorization: Bearer {XAI_API_KEY}" -d @/tmp/xai-ft-keyword.json)
   echo "xai http=$HTTP bytes=$(wc -c </tmp/xai.json)"
   

   Then parse /tmp/xai.json with the standard jq extractor. HTTP=200 with non-empty body → use it (SOURCE_PATH=api).
4. Fall back only on a real failure, and record the true reason — never write "XAI_API_KEY unavailable" when the key was set. Use one of: key-unset (only if step 1 said KEY_UNSET), http-<code> (non-2xx), empty (200 but no tweets parsed), timeout (curl exceeded --max-time).

WebSearch / WebFetch are last-resort fallbacks only — lower quality (WebSearch favours old high-engagement tweets). Never reach for them while the key works.

Environment Variables

• XAI_API_KEY — X.AI API key for Grok's x_search tool. Declared in requires:, so it is injected into this skill's environment and is the primary fetch path for every branch. If it is ever unset, branches degrade to WebSearch/WebFetch at lower quality; the account (all) sub-mode instead hard-exits (TWEET_DIGEST_NO_KEY).