x-keyword-comment
# x-keyword-comment This skill automates keyword-based reply posting on X (formerly Twitter) by searching for tweets matching a user-specified keyword, reading each tweet's content, generating contextual replies based on a configured brand persona and tone, and posting those replies directly to the platform. Use this when you need to batch-engage with tweets on specific topics, run comment-based outreach campaigns, drive traffic through strategic discussions, or systematically comment on search results that match your brand's interests.
git clone --depth 1 https://github.com/browser-act/skills /tmp/x-keyword-comment && cp -r /tmp/x-keyword-comment/solutions/social-listening/x-keyword-comment ~/.claude/skills/x-keyword-commentSKILL.md
# 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
```bash
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:
```bash
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:
```bash
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:
```bash
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)**
```bash
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)**
```bash
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:
```bash
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)**
```bash
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**
```bash
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={KEYWORD_ENCODED}&src=typed_query&f=live"`
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