Mmcp.market

x-keyword-comment skill

by browser-act·browser-act/skills·6.0k stars·MIT

X (Twitter) keyword-based reply posting: search tweets by keyword, read each tweet's content, generate contextual replies from a configured brand persona, and post replies to the reply area. Use when user wants to batch reply to X tweets by keyword, auto-comment on X topic tweets, drive traffic via X comments, X comment outreach, search X tweets and leave comments, bulk reply to Twitter search results, keyword comment on Twitter, post replies on X search page, Twitter keyword comment marketing, X reply campaign, engage with X discussions, or comment on tweets matching a topic.

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Is the x-keyword-comment skill safe?

Clean: nothing in its files matched our rules. We read 3 files in the folder on 2026-09-28.

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Install the x-keyword-comment skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description. 

git clone --depth 1 https://github.com/browser-act/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -r /tmp/skills/solutions/social-listening/x-keyword-comment ~/.claude/skills/x-keyword-comment
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

X — Keyword Comment

keyword + reply intent → search X tweets → read tweet content → generate contextual replies → post to reply area

Language

All process output to user (progress updates, process notifications) follows the user's language.

Objective

Search X by keyword, read each tweet's content, generate contextual replies based on a configured brand persona, and post them — all within a browser-act session.

Prerequisites

  • config/keyword-comment-config.json has been filled in with actual product, persona, and tone values (all YOUR_* placeholders replaced before first run)

Session Rule

{SESSION} is a temporary, per-run session name used in all browser-act --session {SESSION} commands below. It is generated at execution start (e.g., xkc-{timestamp}) and not persisted across runs.

Pre-execution Checks

1. Tool Readiness

If browser-act has been confirmed available in the current conversation → skip this step.

Invoke browser-act via Skill tool to load usage. If installation or configuration issues arise, follow its guidance to resolve then retry.

2. Load Config

python -c "
import json, pathlib, sys
for base in ['.claude/skills/x-keyword-comment', 'output/x-keyword-comment']:
    cfg = pathlib.Path(base) / 'config/keyword-comment-config.json'
    if cfg.exists():
        print(json.dumps(json.loads(cfg.read_text(encoding='utf-8')), ensure_ascii=False, indent=2))
        sys.exit(0)
print('ERROR: config/keyword-comment-config.json not found', file=sys.stderr)
sys.exit(1)
"

Hold product., persona., tone.* fields in working memory for reply composition.

3. Browser Selection

List available browsers:

browser-act browser list
  • If browsers exist → present the list to the user and let them choose which browser to use for this X session.
  • If no browsers exist → guide the user to create one (e.g., browser-act browser create --type stealth --headed), then repeat the list step.

Once the user selects a browser, record its ID as {BROWSER_ID} for this run.

4. Open Session

Generate a unique session name (e.g., xkc-{timestamp}) as {SESSION}. Open the browser:

browser-act --session {SESSION} browser open {BROWSER_ID} https://x.com/ --headed

If the browser is already open with an active session, list sessions and reuse:

browser-act session list

Pick the session associated with {BROWSER_ID} and assign its name to {SESSION}.

5. Login Verification

If X login status has been confirmed in the current conversation → skip this step.

Otherwise: browser-act --session {SESSION} get markdown and check:

  • Sidebar bottom shows @username, top navigation shows Home / Explore → logged in, continue
  • Page shows a "Sign in" button with no logout entry → not logged in; inform the user that login is required and assist the login flow

User refuses or cannot log in → terminate execution.

Capability Components

This Skill's operational boundary = what the user can manually do in their browser. It only reads data already displayed to the logged-in user, never bypassing authentication or access controls. JS code is encapsulated in Python files under scripts/, invoked via browser-act --session {SESSION} eval "$(python scripts/xxx.py {params})". $(...) is bash syntax; use the bash tool for execution.

Below are all atomic capabilities discovered and verified during the exploration phase, listed by command template with parameters. Simply invoke them as needed — no need to read scripts/*.py source code or re-verify. Only inspect scripts when execution fails for troubleshooting. Combine freely as needed during execution.

AI Workflow: Pre-reply Warmup

Warm up the account before posting replies to simulate organic browsing behavior.

Skip condition: warmup already performed today and less than 4 hours ago, or user says "fast mode".

Step 1 — Check notifications and messages (2–3 min)

browser-act --session {SESSION} navigate "https://x.com/notifications"
browser-act --session {SESSION} wait stable
browser-act --session {SESSION} get markdown
sleep $((RANDOM % 31 + 60))   # 60–90 s
browser-act --session {SESSION} navigate "https://x.com/messages"
browser-act --session {SESSION} wait stable
browser-act --session {SESSION} get markdown
sleep $((RANDOM % 31 + 30))   # 30–60 s

Step 2 — Browse feed and like (3–5 min)

browser-act --session {SESSION} navigate "https://x.com/home"
browser-act --session {SESSION} wait stable
browser-act --session {SESSION} get markdown

Randomly pick 3–5 tweets from the feed. For each:

browser-act --session {SESSION} navigate "{tweet URL}"
browser-act --session {SESSION} wait stable
sleep $((RANDOM % 26 + 15))   # 15–40 s
# If content is relevant → like it:
browser-act --session {SESSION} state
browser-act --session {SESSION} click {Heart index}   # element with aria-label containing "Like"
browser-act --session {SESSION} wait stable
sleep $((RANDOM % 8 + 8))     # 8–15 s
browser-act --session {SESSION} navigate "https://x.com/home"
sleep $((RANDOM % 16 + 10))   # 10–25 s

Target: like 1–3 tweets; daily cap 20–30 likes (avoid fast bulk likes that trigger rate limits).

Step 3 — Keyword search browsing (2–3 min)

browser-act --session {SESSION} navigate "https://x.com/search?q={KEYWORD_ENCODED}&f=live"
browser-act --session {SESSION} wait stable
browser-act --session {SESSION} get markdown

Open 2–3 results, spend 25–60 s each reading the full tweet (as reply material).

Pre-action pause

sleep $((RANDOM % 61 + 60))   # 60–120 s — simulate "browse first, then reply"

DOM: Scan Replyable Tweets on Current Page

After navigating to the X search results page, scan all tweets with their reply button indices and content.

  1. Navigate: browser-act --session {SESSION} navigate "https://x.com/search?q={KEYWORDENCODED}&src=typedquery&f=live"
  • {KEYWORD_ENCODED} is URL-encoded (spaces as %20)
  • f=live returns newest tweets; omit for Top tweets
  1. Wait: browser-act --session {SESSION} wait stable --timeout 30000
  2. (Optional) Scroll to load more: browser-act --session {SESSION} scroll down --amount 1500 → browser-act --session {SESSION} wait stable --timeout 10000 → re-scan
  3. Scan: browser-act --session {SESSION} eval "$(python scripts/scan-search-tweets.py --limit {N})"

Parameters:

  • --limit: max tweets to return, default 10

Output example:

{
  "totalReplyBtns": 8,
  "tweets": [
    {
      "i": 0,
      "tweetSnippet": "Breaking: Alibaba just killed the browser automation stack...",
      "authorHandle": "@AIGuideHQ",
      "authorUrl": "https://x.com/AIGuideHQ",
      "tweetUrl": "https://x.com/AIGuideHQ/status/2051969984847286536",
      "replyBtnIdx": 0
    }
  ]
}

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