How /last30days Auto-Discovers and Compares Competitors in One Pass

Jun 8, 2026

Problem

Comparing “OpenClaw vs Hermes vs Paperclip” manually means 3 separate research sessions. Each one re-discovers handles, subreddits, and context. The old engine ran 3 serial passes — 12+ minutes. I don’t want to wait 12 minutes for a comparison.

The Solution

/v3 runs one pass with entity-aware subqueries for all entities simultaneously. The --competitors flag auto-discovers the top 2 peers via web search, resolves each entity’s handles and repos, then fans out N pipelines simultaneously.

Here’s the flow:

User: /last30days OpenClaw --competitors
         │
         ▼
┌─────────────────────┐
│  Discover peers      │  WebSearch finds top 2 competitors
│  via --competitors   │  → Hermes, Paperclip
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Resolve each entity │  Step 0.55 for each:
│                     │  - X handle, GitHub, subreddits
│                     │  - per-entity targeting flags
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Fan out pipelines   │  N parallel pipeline.run() calls
│  in parallel         │  each saves *-raw.md
└──────────┬──────────┘
           │
           ▼
┌─────────────────────┐
│  Merge comparison    │  Quick Verdict → per-entity →
│  output              │  Head-to-Head → Bottom Line
└─────────────────────┘

How to Use It

Two ways to trigger comparison mode:

/last30days OpenClaw --competitors

The engine finds the top 2 competitors automatically.

/last30days "OpenClaw vs Hermes vs Paperclip"

If you already know who to compare, just use vs in the query.

Behind the scenes, the engine builds a --competitors-plan JSON:

{
  "Hermes": {
    "x_handle": "hermes_handle",
    "subreddits": ["r/hermes"],
    "github_user": "hermes-org"
  },
  "Paperclip": {
    "x_handle": "paperclip_handle",
    "subreddits": ["r/paperclip"],
    "github_user": "paperclipai"
  }
}

Why It’s Faster

Approach	Passes	Wall Time
Manual serial research	3 separate sessions	12+ minutes
v2 engine serial	3 serial passes	12+ minutes
v3 parallel fan-out	1 pass, N parallel pipelines	~3 minutes

Same research depth, 75% less wall-clock time. The comparison template — Quick Verdict, per-entity sections, Head-to-Head, The Bottom Line — is standardized and reusable.

Summary

In this post, I explained how /last30days makes product comparison research a single-pass operation. The key point is that with parallel fan-out, three entities, one command, and the same depth as serial research run 4x faster.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 /last30days GitHub Repository
👨‍💻 /last30days Concepts: Comparison Mode

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!