Snipe: Twitter Trending Post Traffic Capture

Reply to pikiclaw under high-traffic promotional posts in the coding agent/AI development tool field to capture traffic and gain stars. This skill only generates reply drafts and pushes them to Feishu, does NOT auto-post to Twitter (to avoid account suspension).

Core Strategy

User-verified effective tactics:

Reply under promotional posts of same-field products (the higher the traffic, the better)
One-sentence differentiation +
```
npx pikiclaw@latest
```
+ GitHub link
Core differentiators of pikiclaw (in order of importance): multi-agent session parallel management (control Claude/Codex/Gemini simultaneously in the dashboard), smooth local experience (launch with one line of npx, zero configuration), practical skill/MCP plugin ecosystem (community-built skills and MCPs ready to use), IM integration (take over the same session anytime via Telegram/Feishu/WeChat), fully open-source + local runtime
IM is not the headline selling point, but part of the "experience" — the headline is always multi-session management + smoothness + plugin ecosystem

Historical case references:

Reply under a remote agent management tool's promotional post → 7,792 views, 31 likes (original post 140,000 views)
Reply under a macOS agent management tool's promotional post → 1,258 views (original post 54,000 views)
Rule: The closer the original post's product functions are to pikiclaw, the higher the reply conversion rate

Workflow

Step 1: Read Replied Records

Read

.pikiclaw/skills/snipe/sniped_posts.txt

to avoid duplicate recommendations.

Step 2: Search for High-Traffic Promotional Posts

Use browser tools to search for recent trending promotional posts on Twitter.

If the user passes keyword parameters, search directly with those keywords. Otherwise, search dynamically according to the following strategy. Do not memorize a specific product name; instead, use scenario keywords to capture promotional posts across the entire field:

Search Keywords (from general to specific, search 3-5 groups):

coding agent tool
AI coding assistant 推荐
claude code 工具
coding agent 开源
remote coding agent
AI agent dashboard
vibe coding 工具
coding agent mobile
AI dev tool launch

Search Operation Steps:

For each keyword:

Navigate to

https://x.com/search?q={keyword}&src=typed_query&f=top

Wait for the page to load (confirm tweet content is visible)

Read the content of

.pikiclaw/skills/snipe/scripts/extract_tweets.js

Inject and execute the JS via
```
browser_evaluate
```
to get the returned JSON string
Parse the JSON to get the tweet array
If there are not enough results, scroll once by pressing the End key and execute the JS again to extract
Merge results from all keywords, deduplicate by
```
text[:80] + url
```

Important: If a keyword yields few or no promotional posts, skip it immediately; do not waste time on invalid keywords.

Step 3: Filter Candidate Posts

From all extracted tweets, identify posts that are promoting specific products/tools.

Signals to identify promotional posts:

The post mentions specific product names, feature introductions, or installation commands
has_product_signal is true (contains GitHub links, npm/pip installation commands, or product domains)
External links point to product official websites or GitHub
Tone is introductory/recommendatory/announcement-based (not pure discussion or question)

Must meet:

views > 5,000 (too small a traffic pool is not worth it)
Published within the last 48 hours
The promoted product/tool has functional overlap with pikiclaw (coding agent management, remote control, multi-agent switching, IM integration, etc.)
Not in
```
sniped_posts.txt
```

Priority Order (high to low):

Promotional posts of products with highly overlapping functions with pikiclaw (best traffic capture effect)
Promotional posts of products in the same track but with different entry points (can play up differentiation)
General AI tool promotional posts (exposure exists but conversion is low)

Exclude:

Own posts (@sthnavy)
Pure news/information/discussion posts (no specific product promotion)
Posts from existing pikiclaw users/retweeters
Political/controversial/irrelevant topics

Select Top 3-5 candidate posts. For each candidate post, briefly analyze the functional overlap and differences between the promoted product and pikiclaw.

Step 4: Generate Reply Drafts

Generate a reply draft for each candidate post.

Core Principle: Understand what the original post is promoting, find the sharpest differentiator of pikiclaw compared to it, and break through with the shortest text.

Reply Format (minimalism first):

{One-sentence differentiation that directly targets the original product's weakness or pikiclaw's unique advantage}
npx pikiclaw@latest

GitHub: https://github.com/xiaotonng/pikiclaw

Differentiation Entry Angles (recommended priority from top to bottom, pick the one that best matches the original post's product):

The other party only manages single agent / single session → "Manage Claude/Codex/Gemini multi-sessions in parallel in the dashboard, switch anytime"
The other party has rough experience / complex configuration → "Launch with one line of npx, dashboard ready to use, zero configuration"
The other party has a closed ecosystem / no plugins → "Open skill/MCP plugin system, practical community-built tools ready to use"
The other party is closed-source / SaaS → "Fully open-source, all sessions and code stay local"
The other party only has CLI → "Built-in web dashboard, full control of multi-sessions in the browser"
The other party can only be used at the desk → "Direct connection via Telegram/Feishu/WeChat, take over the same session anytime on mobile"
The other party only supports English / single platform → "Bilingual (Chinese/English), macOS desktop automation + Playwright browser control"

Reply Rules:

Follow the language of the original post (use Chinese for Chinese posts, English for English posts)
Keep it minimal, 1-2 sentences are best, never more than 3
Must include
```
npx pikiclaw@latest
```
and the GitHub link
Use a builder/developer tone, avoid promotional words like "recommend" or "endorse"
Do not belittle the original post's product, only emphasize pikiclaw's differences

Step 5: Generate Report Markdown

Organize candidate posts and reply drafts into a Markdown report:

markdown

# Snipe Candidates — {YYYY-MM-DD}

A total of {n} high-traffic promotional posts were found, arranged below by recommendation priority.

---

## Candidate 1: {Original post content summary, no more than 20 characters}
- **Author**: @{handle} ({name})
- **Link**: {tweet_url}
- **Data**: {views} views / {likes} likes / {retweets} RT
- **Promoted Product**: {product_name} — {One-sentence description of what this product does}
- **Overlap with pikiclaw**: {Functional overlap points}
- **Differentiated Advantage of pikiclaw**: {The sharpest differentiator}

### Recommended Reply
> {draft}

---

## Candidate 2: ...
...

---

## Operation Guide
1. Prioritize replying to Candidate 1 (highest traffic / closest functions), then proceed in order
2. Paste the GitHub link when posting the reply on Twitter, Twitter will automatically generate a card
3. After posting, append the tweet URL to `.pikiclaw/skills/snipe/sniped_posts.txt`

Step 6: Push to Feishu

Write the Markdown report generated in Step 5 to
```
/tmp/snipe_report.md
```
Execute the Feishu push script:

bash

cd /Users/admin/Desktop/project/pikiclaw && python3 .pikiclaw/skills/snipe/scripts/push_feishu.py --report-file /tmp/snipe_report.md

Check the script output:
- Starts with
```
OK:
```
  → Success, both the report document URL and notification have been sent
- Starts with
```
PARTIAL:
```
  → Document created successfully but notification not sent (missing FEISHU_RECEIVE_ID)
- Starts with
```
ERROR:
```
  → Failed, directly display the report content in the conversation as a fallback

Step 7: Update Records

Append the URLs of all candidate posts from this round to

.pikiclaw/skills/snipe/sniped_posts.txt

Notes

Never auto-post to Twitter — All reply drafts are only pushed to Feishu for manual review
Quality > Quantity — 3-5 candidates per round are sufficient
Do not fixate on searching for a specific product name — Use scenario keywords to discover dynamically; new tools emerge in this field every day
Feishu credentials are read from the project root directory
```
.env
```
(requires
```
FEISHU_APP_ID
```
,
```
FEISHU_APP_SECRET
```
,
```
FEISHU_CHAT_ID
```
)

snipe

NPX Install

Tags

SKILL.md Content (Chinese)