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x-use-engage

x-use-engage is a skill for Claude Code from ihuzaifashoukat/x-use. It costs 57 tokens per session (1,207 once invoked), scanned A, original, MIT.

A draft-first workflow for finding relevant conversations on X and writing replies in a user-defined voice. It reads posts, including attached images, and waits for approval before anything is posted.

In plain words
What is it for?
Use it to find posts by topic or profile, inspect their text and images, draft persona-matched replies, and stage a small set of replies for approval.
Why use it?
It removes the effort of searching a niche and deciding which posts deserve a useful reply. It also reduces off-topic or off-voice replies by filtering candidates and requiring review.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the x-use plugin — 6 skills shipped together

not rated 179repo +1 changed 16d ago A scan SkillSpector: pass 57 tokens original MIT

Good fit Use it to find posts by topic or profile, inspect their text and images, draft persona-matched replies, and stage a small set of replies for approval.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ihuzaifashoukat/x-use/x-use-engage
View source ↗ ihuzaifashoukat/x-use pypi.org
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 ihuzaifashoukat/x-use --skill x-use-engage
Clone the repo
git clone --depth 1 https://github.com/ihuzaifashoukat/x-use

Made for: Claude Code.

Or install x-use, the plugin that ships this one along with the rest of its 6 skills.

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 x-use-engage

README.md
[![agentmods](https://agentmods.dev/badge/skills/ihuzaifashoukat/x-use/x-use-engage/github.svg)](https://agentmods.dev/skills/ihuzaifashoukat/x-use/x-use-engage)
Your own site
<a href="https://agentmods.dev/skills/ihuzaifashoukat/x-use/x-use-engage"><img src="https://agentmods.dev/badge/skills/ihuzaifashoukat/x-use/x-use-engage/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 x-use-engage

Your own site · 80×15
<a href="https://agentmods.dev/skills/ihuzaifashoukat/x-use/x-use-engage"><img src="https://agentmods.dev/badge/skills/ihuzaifashoukat/x-use/x-use-engage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,207 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00057 $0.01207
Opus 5.5 $0.00023 $0.00483
Sonnet 5.5 $0.00011 $0.00241
Haiku 4.5 $0.00006 $0.00121

Measured 16d ago against content hash 777b37535934, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

x-use-engage scanned grade A with 0 findings 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 16d 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.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/x-use/skills/x-use-engage/SKILL.md · 92 lines

How it starts

The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.

x-use engage

Research and prepare exact payloads for review. Ordinary reply tools stage only when mcp.draft_mode is enabled (default); DMs and follows always require draft approval. Honor prior authorization for the exact action and payload.

Workflow

  1. Scope. Default to the account's own keywords: get_account(account) shows target_keywords. If the user gave URLs or topics instead, use those. Confirm the account with list_accounts if unsure.
  2. Search. search_tweets(keywords=<one query>, limit=5, account=...). One query per call; prefer 2-3 focused queries over one broad one. To work a specific person or competitor instead of a topic, use search_profile(profile="@handle", limit=5, account=...). Profile timelines include pinned posts and reposts, so check user_handle on each result before treating it as theirs.
  3. Read candidates properly. For each promising tweet call prepare_reply(account, tweet_url). Read text, author, persona, keywords and attached images when available. Charts, memes and UI shots can change what a useful reply says. If only media URLs/alt text are available, preserve that limitation and do not claim to have seen their contents.
  4. Filter. Skip: tweets you can't add value to, pure announcements, anything off-persona, and the account's own posts. Keep at most 3-5.
  5. Compose. Write each reply yourself, in the account's persona, under 270 chars: concrete, adds one useful point or question, no hashtags/links/ emoji spam, no "Great post!".
  6. Stage. reply_to_tweet(account, tweet_url, text=<your text>) returns a draft. Repeat per candidate.
  7. Review with the user. Show a compact list: author, tweet gist, your reply, draft_id. Only call approve_draft(draft_id) for the ones the user authorizes; reject_draft drafts they reject. Inspect the complete payload with get_draft before approving; previews can omit media or context.

Individual profile outreach

Read the full file on GitHub · 92 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. 16d ago Changed · +49 lines · -2 tokens per session 777b37535934
  2. 1mo ago First seen · 43 lines · 59 tokens per session scan A 207b61c67dc4

Subscribe to this mod's changes

x-use-engage is a skill published in the GitHub repository ihuzaifashoukat/x-use (179 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 1,207 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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