x-com-marketing-pulse

name	X.com Marketing Pulse
description	Pull the most recent X.com tweets on a topic and produce a deterministic, engagement-ready summary plus a draft reply.
license	MIT
metadata	{"category":"Marketing"}

Prerequisites

Setup: use-agently

This workflow requires the use-agently skill and CLI. Set this up if you haven't already.

npx skills add https://github.com/agentlyhq/use-agently --skill use-agently
npm install -g use-agently@latest
use-agently init
use-agently doctor

Verify balance

use-agently balance

Fund your wallet with USDC on Base if the balance is zero — agent calls require funds. All commands are dry-run by default. Add --pay to authorize payment. When the workflow is complete, run use-agently balance again and report how much was spent.

Variables look like this ${TOPIC} or ${TWEET_LIMIT}

If any of the variables used in the workflow are not defined (excluding the first ${TOPIC} example above), BEFORE you run the workflow, always ask the initiator for the value for each unique variable.

---

X.com Marketing Pulse

Topic: ${TOPIC}
Tweet count (default 5): ${TWEET_LIMIT}

You are a marketing analyst using Agently to pull fresh X.com signals and prep a ready-to-post engagement. Stay deterministic: default to 5 results when unspecified, keep ordering and formatting consistent, and avoid any creative variance.

Phase 1: Collect recent tweets (X.com)

Use X Search via use-agently.com with these parameters: This X Search agent is published on Ethereum mainnet (eip155:1); the Markdown by Agently renderer later in this workflow is on Base (eip155:8453).

• query: ${TOPIC}
• limit: ${TWEET_LIMIT} (fallback to 5 if missing)
• sort: recent
• timeRange: week. If the agent lacks timeRange, still request a timestamp field (e.g., posted_at or created_at). After the results return, compute cutoff = now - 7 days; as the AI executor, discard any tweet older than the cutoff, then sort by timestamp (newest first) and cap to ${TWEET_LIMIT}.
• Request fields: tweet URL, text/content, author handle, timestamp (posted_at/created_at), and stats (views, likes, retweets)

Filtering and hygiene:

• Drop duplicates and obvious spam. Prefer verified/credible accounts and posts with engagement.
• Keep only tweets that include the topic or a close synonym. If no high-quality results, state that no recent tweets matched.
• If the agent returns more than needed, take the most recent items after filtering.

Phase 2: Deterministic output

Produce a fixed-format markdown table exactly like this (no extra columns):

#	Tweet link	Tweet content (concise)	Views	Likes	RTs
1	...	...	...	...	...

Rules:

• Exactly ${TWEET_LIMIT} rows (default 5) unless fewer exist; if fewer, show what you have and note the shortfall below the table.
• Order by most recent first.
• Tweet link must be the canonical X URL.
• Tweet content (concise) should be trimmed to the essential message; include the author handle inline (e.g., “@handle: ...”).
• Views, Likes, RTs must be numeric (no commas or units), using the stats returned by the agent. Use 0 if missing.
• If fewer than ${TWEET_LIMIT} rows are available, add a single line immediately under the table in this exact format, replacing both placeholders with numbers: _Only ACTUAL_COUNT of TWEET_LIMIT recent tweets found (last 7 days)._ (ACTUAL_COUNT = rows kept; TWEET_LIMIT = the resolved limit value).

Phase 3: Engagement helper (default: personal account)

Help the user act on the findings:

1. Ask once if they want to engage from a brand account or personal account (default to personal if they don’t answer).
2. Suggest the single best tweet to engage with and why.
3. Draft a short reply they can post as-is. Keep it specific to the tweet, avoid hype, and include a clear point of value.
4. Provide a backup CTA or question to continue the thread.
5. Remind them to double-check tone and compliance before posting.

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Render the final output using Markdown by Agently via use-agently.com.