fetch-tweets — Claude Code skill: X/Twitter digests
Pulls tweets by keyword, topic, account or X list and turns them into a digest grouped by sub-narrative and ranked by signal.
npx skills add aeonfun/aeon --skill fetch-tweets -a claude-codeWhat fetch-tweets does
One selector drives everything. You pass source: plus an argument, and the skill runs one of five branches: keyword, topic, account, list or agent-buzz. The argument changes with the branch — a search query, a subject, a handle, numeric list IDs, or an optional focus. Leave the prefix off and the mode is guessed from the argument's shape: empty means topic, digits mean list, a handle means account, anything else means keyword. The agent-buzz preset is the exception; it only fires with an explicit prefix.
Output is not a chronological feed. Tweets get clustered into sub-narratives, sorted by signal, with a single take attached to each. Before fetching, the skill reads memory/MEMORY.md and recent logs so links already reported stay out. The primary route is the X.AI API and it wants XAI_API_KEY; when the key is missing or the call fails, collection falls back to WebSearch. Either way you get a digest.
Quiet runs send nothing — an empty or all-duplicate pass just writes a log line. Engagement numbers are never invented: a missing count becomes 0. Links are written as x.com/handle so Telegram does not tag anyone.
What it helps with
- pull fresh tweets on a keyword and split them by sub-topic
- get a digest of one account without scrolling the timeline
- merge several X lists into a single roundup
- track chatter around AI agents with the built-in preset
- skip links that were already reported
Example prompts for the agent
- Build a digest from
keyword:$SOL OR solanafor yesterday - Run a roundup on
topic:brain-computer interfaces - Fetch
agent-buzz:MCP protocol
How to install fetch-tweets
1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill fetch-tweets -a claude-code2Manually: put the files in place
~/.claude/skills/fetch-tweets/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/fetch-tweets/
1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill fetch-tweets -a codex2Manually: put the files in place
.agents/skills/fetch-tweets/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill fetch-tweets -a cursor2Manually: put the files in place
.cursor/skills/fetch-tweets/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install aeonfun/aeon fetch-tweets2Manually: put the files in place
.github/skills/fetch-tweets/SKILL.md1Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill fetch-tweets -a gemini-cli2Platform CLI
gemini skills install https://github.com/aeonfun/aeon --path skills/fetch-tweetsManually: put the files in place
.devin/skills/fetch-tweets/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add aeonfun/aeon --skill fetch-tweets -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about fetch-tweets
How do I install fetch-tweets in Claude Code?
Run npx skills add aeonfun/aeon --skill fetch-tweets -a claude-code. The skill lands at ~/.claude/skills/fetch-tweets/SKILL.md.
What does fetch-tweets do?
It searches X along one of five axes and assembles a curated digest: clustered by sub-narrative, ranked by signal, one insight per item.
Do I need an X.AI key?
The primary route through the X.AI API needs XAI_API_KEY. Without it the skill switches to WebSearch.
Source text
SKILL.md · MIT
---
name: fetch-tweets
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.
metadata:
title: Fetch Tweets
category: basics
var: ""
tags:
- social
requires:
- XAI_API_KEY?
---
<!-- autoresearch: variation B — sharper output via clustering + signal scoring + insight extraction. Merged HUB: absorbs tweet-digest, tweet-roundup, list-digest, refresh-x, agent-buzz behind a `source:` selector. -->
> **${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):
```bash
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-4.7", 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`.
```bash
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-4.7", 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 — ${today}*
_Source: api:X websearch:Y failed:Z_
*[Topic 1]* — _conversation shape_
- x.com/handle — insight (signal: 12.3k) [View](https://x.com/handle/status/ID)
- x.com/handle — insight (signal: 4.1k) [View](https://x.com/handle/status/ID)
*[Topic 2]* — _conversation shape_
- x.com/handle — insight (signal: 8k) [View](https://x.com/handle/status/ID)
```
Show `signal: <score>` only when engagement counts were available (api path); omit silently on WebSearch.
6. **Persist + log** (see Log). Append each reported URL (one per line) to `memory/tweet-roundup-seen.txt` (create if missing).
**Constraints:** never notify an empty roundup (silence beats filler); never `@handle` anyone; never report a URL already in `SEEN_TWEETS`. **Status codes:** `TWEET_ROUNDUP_OK` | `TWEET_ROUNDUP_EMPTY`.
---
## Branch: account (`source:account`)
Two sub-modes: **single handle** (decision-ready gist of one account) vs. **all tracked accounts** (theme-grouped digest of a watchlist). Choose by `ARG`.
**Seen set:** last 2 days of logs — extract every `https://x.com/` URL under a prior `### fetch-tweets` account entry into `SEEN_URLS`.
### account — single handle (`ARG` is one @handle)
1. **Normalize `ARG`.** Strip leading `@`, `https://x.com/`, `https://twitter.com/`, `https://nitter.net/`, trailing slash / `/status/...`. Lowercase. Reject if empty, contains whitespace, or >15 chars. On reject → `REFRESH_X_NO_VAR`: send `./notify "fetch-tweets: REFRESH_X_NO_VAR — set an X handle"` and exit 0. Store the cleaned handle as `ACCOUNT`.
2. **Load tweets:**
- **Path A — X.AI API** (primary): search this account's recent tweets via Grok's `x_search`.
```bash
PROMPT="Search X for the latest tweets, replies, and quote tweets from @${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-4.7", 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:
```yaml
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):
```bash
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 "$PROMPT" '{model:"grok-4.7", input:[{role:"user",content:$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 — ${today}*
*Theme: <theme>*
@handle: <one-sentence summary> — [link](url)
@handle: <one-sentence summary> — [link](url)
*Theme: <theme>*
...
```
If no notable tweets across all accounts: log `TWEET_DIGEST_OK` and end (no notification).
**Status codes:** `TWEET_DIGEST_OK` (notified or clean) | `TWEET_DIGEST_NO_CONFIG` | `TWEET_DIGEST_NO_KEY`.
---
## Branch: list (`source:list`)
Cross-list narrative resonance + signal-scored top tweets from tracked X lists in the past 24h. Lists are *curator signal* — the value is cross-list resonance + insight + a verdict, not a flat top-N-per-list dump.
**Seen set:** `memory/list-digest-seen.txt` + last 2 days of logs.
1. **Parse and validate `ARG`.**
```bash
if [ -z "$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):
```bash
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-4.7", 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 — ${today}* — quiet across all tracked lists" instead of padding.
8. **Log and persist** (see Log). Append every reported URL (one per line) to `memory/list-digest-seen.txt` (create if missing).
**Exit taxonomy:** `LIST_DIGEST_NO_CONFIG` (var empty/invalid OR no fetch path — log only) | `LIST_DIGEST_EMPTY` (every list 0 tweets OR all candidates already seen — log only) | `LIST_DIGEST_PARTIAL` (some lists succeeded/some failed — notify survivors, surface failures) | `LIST_DIGEST_OK` (≥1 fresh tweet — notify).
---
## Branch: agent-buzz (`source:agent-buzz`)
A topic-filtered preset: a curated, narrative-aware read on what the AI-agent scene on X talked about in the last 24h. **Curation, not aggregation** — 6 high-signal tweets in 2 clusters beats 10 of mixed noise. `ARG` (optional) is a project/topic to prioritize.
**Seen set:** last 3 days of logs — extract every `https://x.com/.../status/<id>` already posted by this skill; treat those IDs as the dedup set.
1. **Fetch candidates:**
```bash
FROM_DATE=$(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):
```bash
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-4.7", 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/...
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