reddit-warmup
The reddit-warmup skill automates a 30-day progression to build credible Reddit accounts, starting with a lurking phase, advancing to commenting, and culminating in brand promotion through scheduled posts and replies. Use this skill to establish authentic-looking account identities, systematically accumulate karma and community standing, and eventually deploy coordinated promotional content across multiple subreddits without detection.
git clone --depth 1 https://github.com/browser-act/skills /tmp/reddit-warmup && cp -r /tmp/reddit-warmup/solutions/social-listening/reddit-warmup ~/.claude/skills/reddit-warmupSKILL.md
# Reddit Account Warm-up (30-Day Brand Promotion)
Builds authentic-looking Reddit accounts through a 30-day progression, then uses them to promote any brand the user configures. State files are managed as local files under `~/.reddit-warmup/<username>/`.
**Agent compatibility:** this skill is runtime-neutral. It can be used by any agent environment that can load local Markdown references, read/write local JSON/YAML/text files, list local directories, run shell commands for `browser-act`, and continue a conversation after a user approval reply. Do not depend on runtime-specific question widgets, background agents, sub-agents, notification events, or tool names.
**Execution boundary:** every Reddit-facing browser, page, network, publishing, and verification operation must use `browser-act` CLI as the only browser execution tool. No other browser execution path is allowed. Use `browser-act state` / `get markdown` / `get title` / screenshots / network capture for verification. Local state files are the only non-browser exception and may be handled with the host agent's normal local file read/write/list capabilities. If browser-act cannot complete a step, stop, log/notify, or use browser-act `remote-assist`; never fall back to another browser execution tool. Details in `references/browser-act-rules.md`.
**Three stages:**
- **Days 2-14 -Lurk**: browse and upvote only
- **Days 15-29 -Comment**: AI-generated natural comments; up to 1 non-brand practice post per week
- **Days 30+ -Promote**: comments + up to 2 posts/week (1 normal + 1 promotional)
**Core rule: one account = one browser profile = one proxy.** Never swap, never mix.
---
## Step 0 -Load Reference Files (required before any action)
Instructions live in separate files loaded on demand. Load the files for the current context before taking any action. Prior knowledge does not substitute for loading the current reference files.
| Context | Load these files (in order) |
|---|---|
| `config.yaml` not found (onboarding) | `references/phase1-onboarding.md` only |
| Phase 2, any stage -start of run | `references/browser-act-rules.md` ->`references/phase2-preflight.md` |
| Stage 1 (Days 2-14) execution | `references/stage1-lurk.md` |
| Stage 2 (Days 15-29) execution | `references/stage2-comment.md` ->`references/approval-rules.md` |
| Stage 3 (Day 30+) execution | `references/stage3-promote.md` ->`references/approval-rules.md` |
| Notification / inbox check | `references/notification-check.md` (called from phase2-preflight Step 6) |
| Daily tail recon + feedback | `references/end-of-day-recon.md` ->`references/comment-follow-up.md` |
| Daily report + anomaly check | `references/anomaly-detection.md` |
---
## Language
All process output to the user (plan confirmation, progress updates, process notifications) follows the user's language.
---
## Red Lines (every account, every stage, no exceptions)
| Rule | Why |
|------|-----|
| Never delete any post/comment in first 30 days | Deletion is a stronger bot signal than silence |
| Email verified immediately after registration | Unverified accounts get silently filtered by many subs' AutoMod |
| No DMs, no friend requests in first 30 days | New-account DMs trigger anti-harassment heuristics instantly |
| No unsubscribing in first 30 days | Sub-hopping looks like a burner account |
| Never edit the same comment more than twice | Repeated edits flag the account |
| All `browser open` uses `--headed` | |
| All browser work goes through `browser-act` CLI | No alternate execution path |
---
## State Files
All state lives in `~/.reddit-warmup/<username>/`:
| File | Purpose |
|------|---------|
| `config.yaml` | Browser ID, proxy, subreddits, keywords, timezone |
| `progress.json` | Current day, karma, stage, last_run, pause flags, week counters |
| `sub_profiles.json` | Per-sub risk cache (karma/age gate, rules, flair, filter rate) -14-day TTL |
| `activity_log.jsonl` | Append-only log; every action recorded here |
| `pending_approval/<batch_id>/` | Batches awaiting user pick -no expiry while unpicked, then retired after skip/publish |
| `drafts/<batch_id>/` | Published / auto-pick audit records and delayed replay records |
| `evidence/` | Per-event screenshots |
| `images/` | Downloaded images for Stage 3 image posts |
| `last_run.png` | Most recent run screenshot |
Templates: `assets/config.yaml.template`, `assets/progress.json.template`, `assets/sub_profiles.json.template`, `assets/persona.json.template`.
**How the agent manages these:** use the host environment's normal local-file capabilities to load JSON/YAML/text, rewrite whole state files, and list matching account/batch directories. Append to JSONL by reading existing content and writing it back with a new line. Do not use shell text-processing shortcuts for state mutation.
---
## Keyword Rotation
The user's `keywords.find` and `keywords.brand` typically have more than one entry. **Daily tasks automatically rotate** one term from the list so activity naturally disperses.
| List | Rotates? | Reason |
|------|--------|------|
| `keywords.find` | Daily rotation | Swap daily so search result subsets naturally shift |
| `keywords.brand` | Rotates per brand post | Alternate between different selling points per post |
| `keywords.content` | NONo rotation | Persona descriptors form a whole; splitting them causes voice drift |
### Rotation state in `progress.json`
```json
"keyword_rotation": {
"find_index": <int>, // index to use next time Stage 2/3 uses keywords.find
"brand_index": <int> // index to use next time Path B uses keywords.brand
}
```
Both indices start at 0 and increment by 1 per use. Retrieval: `list[index % len(list)]`.
- List has only 1 item ->always uses that one (no error)
- User adds/removes items mid-stream ->index just modulos to the new length
- List becomes empty ->skip the current use case
### When to increment
| Trigger | Increment |
|----------------|---------|
| Stage 2/3 uses `find` keywExtracts comprehensive wholesale product data from 1688.com product detail pages: title, tiered pricing, SKU variants with dimensions/weight, product images, seller info, shop scores, buyer protection, cross-border flags, product attributes, coupon/promotion data, and review stats. Use when user mentions 1688, 1688.com, wholesale China, alibaba wholesale, B2B China sourcing, Chinese wholesale scraper, 1688 product scrape, 1688 offer, 1688 detail, extract 1688 data, pull 1688 listings, get wholesale price, 1688 supplier info, factory stats 1688, 1688 SKU variants, 1688 product attributes, 1688 shop score, DSR score 1688, 1688 buyer protection, 1688 cross-border, 1688 dropship. Also applies to: scraping bulk product data from 1688 by offer ID list, monitoring 1688 supplier metrics, extracting 1688 pricing tiers for resale analysis.
Fetches complete Airbnb listing details for a given numeric listing ID via the internal GraphQL API, returning title, room type, description, amenities, photos, coordinates, city, house rules, highlights, ratings, review count, bedroom configuration, and property overview. Use when user mentions Airbnb listing details, Airbnb property info, Airbnb room details, get Airbnb listing data, Airbnb amenities list, Airbnb house rules, Airbnb property description, Airbnb detail page scraper, Airbnb rooms detail, Airbnb property page data, Airbnb listing info, fetch Airbnb room details, pull Airbnb listing.
Extracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.
Amazon Alexa for Shopping Q&A automation: submits questions to Amazon's Alexa/Rufus AI shopping assistant and collects response text; supports optional keyword search context (navigate to search results page before asking for category-specific answers). Use when user mentions Amazon Alexa, Rufus, Amazon shopping assistant, Amazon AI chat, ask Amazon, Amazon Q&A, automate Alexa questions, Rufus chatbot, Amazon assistant automation, collect Alexa responses, bulk question submission to Amazon, keyword search context, category research. Also applies to extracting Amazon product recommendations from conversational AI, automating repeated queries to Amazon's AI shopping feature, collecting Alexa shopping responses at scale, or market research within a specific product category.
This skill helps users extract structured product details from Amazon using a specific ASIN (Amazon Standard Identification Number). Use this skill when the user asks to get Amazon product details by ASIN, lookup Amazon product title and price using ASIN, extract Amazon product ratings and reviews count for a specific ASIN, check Amazon product availability and current price, get Amazon product description and features via ASIN, enrich product catalog with Amazon data using ASIN, monitor Amazon product price changes for specific ASINs, retrieve Amazon product brand and material information, fetch Amazon product images and specifications by ASIN, validate Amazon ASIN and get product metadata.
This skill helps users extract structured best-selling product data from Amazon via the BrowserAct API. Agent should proactively apply this skill when users express needs like search for best selling products on Amazon, extract Amazon product data based on keywords, find top rated Amazon products, monitor Amazon competitor prices and sales, discover trending products on Amazon marketplace, extract Amazon product titles prices and ratings, gather Amazon product sales volume for market research, search Amazon best sellers in specific region, collect Amazon product reviews and promotion details, analyze Amazon product availability and badges, get Amazon product data for market analysis.
Amazon Best Sellers listing scraper: extract product cards from any Amazon Best Sellers (zgbs) or /gp/bestsellers/ category page — returns rank (position on chart), asin, title, url, image, imageAlt, price, stars, reviewCount, ratingRaw per item, plus category metadata (categoryName, categoryFullName, categoryUrl) and pagination state (currentPage, hasNextPage, nextPageUrl). Works across all Amazon regional TLDs (amazon.com, amazon.co.uk, amazon.de, amazon.co.jp, amazon.fr, amazon.it, amazon.es, amazon.ca, amazon.com.au, amazon.in, etc.). Use when user mentions Amazon Best Sellers, Amazon bestsellers, Amazon top 100, Amazon zgbs, Amazon /zgbs/, Amazon /gp/bestsellers/, Amazon Best Sellers Rank, Amazon BSR, Amazon top ranked products, Amazon top-selling products, Amazon chart, Amazon category ranking, Amazon best sellers by category, Amazon best sellers electronics, Amazon best sellers kitchen, Amazon best sellers toys, scrape Amazon bestsellers, extract Amazon top 100, Amazon rank scraper, Amazon best seller list, Amazon leaderboard, Amazon trending products, discover trending Amazon products, Amazon niche discovery, Amazon top ranked ASINs. Also applies to competitive intelligence via ranking snapshots, spotting up-and-coming products, sourcing bestseller ASINs for further enrichment, tracking rank changes over time, and building bestseller-per-category datasets.
This skill helps users extract basic product details other sellers prices and seller ratings from Amazon via ASIN automatically using the BrowserAct API. Agent should proactively apply this skill when users express needs like query Amazon buy box information, monitor Amazon product prices, extract Amazon product details by ASIN, check other sellers prices on Amazon, get Amazon seller ratings and feedback count, monitor buy box ownership for a specific ASIN, track Amazon fulfillment methods for competitors, compare Amazon product prices across different sellers, retrieve Amazon buy box availability status, analyze Amazon seller profile details.