How to Build an OpenClaw Research Agent for $8/Month

Summary:

1. Build a 4-layer research agent: fetch, summarize, filter noise, compare changes.
2. Configure source monitoring for 50+ RSS feeds, APIs, and web sources.
3. Set up daily Telegram briefings and instant alerts for high-significance findings.
4. Get the baseline file that filters noise so you only see what changed.

A hedge fund pays a junior analyst $185,000 a year to scan 40 sources every morning and write a summary. Your OpenClaw research agent daily briefing scans 50+, runs 24 hours instead of 10, catches patterns across weeks, and costs $8 per month.

You don’t need to work at a hedge fund. The same system works for tracking competitors, monitoring regulatory changes, or staying on top of a fast-moving technical field.

How does the 4-layer architecture work?

I built this wrong three times before getting it right.

Version 1: search Google for five keywords, email the links. Terrible. A wall of links with no context.

Version 2: search and summarize. Better. Still noisy. Reported “Company X announced a product” even when they announce something every Tuesday.

Version 3: added a baseline file. “Company X launches minor updates weekly. Only flag major product lines.” The agent stopped reporting noise.

Version 4 (final): added yesterday’s comparison. Each day’s findings get stored. The next cycle reads both and reports only what changed.

Four layers: fetch from sources, summarize findings, filter against baseline, compare against yesterday. That’s the whole architecture.

How do you configure source monitoring?

Create skills/research-analyst/sources.yaml:

sources:
  - name: "TechCrunch AI"
    type: rss
    url: "https://techcrunch.com/category/artificial-intelligence/feed/"
    check_interval: "6h"

  - name: "Hacker News Top"
    type: api
    url: "https://hacker-news.firebaseio.com/v0/topstories.json"
    check_interval: "2h"
    filter: "ai OR agent OR openclaw OR automation"

  - name: "r/openclaw Hot"
    type: rss
    url: "https://www.reddit.com/r/openclaw/hot.rss"
    check_interval: "4h"

  - name: "Competitor Blog"
    type: web
    url: "https://competitor.com/blog"
    check_interval: "12h"

50 sources is a good starting point. RSS feeds cost almost nothing to monitor. Web sources cost more (browser skill fetches). The check_interval matters: hourly news gets checked every 2-6 hours, weekly publications get checked daily.

The Hacker News API is free and public. Try it right now:

import requests

# No auth needed - live Hacker News top stories
resp = requests.get("https://hacker-news.firebaseio.com/v0/topstories.json")
story_ids = resp.json()[:5]
for sid in story_ids:
    story = requests.get(f"https://hacker-news.firebaseio.com/v0/item/{sid}.json").json()
    print(f"{story.get('score', 0)} pts: {story.get('title', 'No title')}")

How do you build the skill?

Create skills/research-analyst/config.yaml:

name: research-analyst
description: Monitors sources and produces intelligence briefings
trigger:
  schedule:
    cron: "0 6 * * *"
context:
  sources:
    - type: file
      path: "./skills/research-analyst/sources.yaml"
    - type: file
      path: "./data/research-baseline.md"
    - type: file
      path: "./data/research-yesterday.json"
actions:
  - name: gather
    type: multi_source_fetch
    sources_file: "./skills/research-analyst/sources.yaml"
    max_articles_per_source: 10

  - name: analyze
    type: llm_query
    model_override: kimi-k2.5
    prompt: |
      Analyze today's articles against:
      1. The baseline (what's normal, not worth reporting)
      2. Yesterday's report (what changed)

      For each significant finding:
      - HEADLINE: One-line summary
      - SOURCE: Where you found it
      - SIGNIFICANCE: Why it matters (1 sentence)
      - ACTION: What to do about it (or "Monitor")

      Ignore routine announcements. Report: new competitors,
      pricing changes, partnerships, security incidents.

      Baseline: {{research-baseline}}
      Yesterday: {{research-yesterday}}
      Today: {{gathered_articles}}

  - name: report
    type: format_and_send
    channels: [telegram]

  - name: store
    type: file_write
    path: "./data/research-yesterday.json"
    content: "{{today_structured_results}}"

How does the baseline file filter noise?

This is the secret. Create data/research-baseline.md:

# Research Baseline

## Normal Activity (don't report)
- OpenClaw minor version updates (report only major versions)
- Routine blog posts from [your monitored competitors]
- AI funding rounds under $10M (report $10M+)
- Conference talk announcements (report only keynotes)

## Always Report
- Security vulnerabilities in any tool I use
- Pricing changes from any AI model provider
- New competitors entering my specific market
- Regulatory actions related to AI agents
- Any mention of my company or products

Week 1, the baseline is rough. You get 15 findings per day, 5 are noise. Tighten it. By week 2, you get 6-8 findings, almost all signal. The briefing takes 90 seconds to read.

What about real-time alerts?

Some things can’t wait until tomorrow. Add an alert action:

  - name: alert
    type: conditional_notify
    condition: "significance == 'HIGH' or mentions_my_company == true"
    channel: telegram
    immediate: true
    template: |
      ALERT: {{headline}}
      Source: {{source}}
      Why: {{significance}}
      Action: {{action}}

I get 2-3 alerts per week. If you’re getting more than one per day, your baseline needs tightening.

What broke (and the compounding effect that fixed it)

I lost a week of Hacker News monitoring because the API URL changed and I hadn’t set up failure notifications. I didn’t notice until the briefings felt thinner. Add failure handling:

failure_handling:
  max_retries: 3
  retry_delay: "30m"
  on_persistent_failure: "notify"

Here’s what surprised me: the agent gets smarter over time without configuration changes.

After week 4, the weekly analysis started identifying patterns across daily snapshots. “Competitor X posted three ML engineer job listings in two weeks. They’re building an AI feature.” I didn’t ask for this. The agent noticed the pattern across stored daily reports.

After month 2, patterns got deeper. “Three companies in your market raised funding this month. Sector investment is tracking 40% above last quarter.” Strategy consultants charge $50,000 for this kind of synthesis. Your agent does it as a side effect of daily monitoring.

What does it actually cost?

def research_agent_cost(sources=50, model="kimi-k2.5"):
    """Calculate monthly research agent cost."""
    daily = {
        "source_monitoring": 0.18,  # 50 sources, Kimi K2.5
        "alert_processing": 0.02,   # occasional alerts
    }
    weekly = {
        "claude_deep_analysis": 0.15,  # weekly trend report
    }
    monthly = sum(daily.values()) * 30 + sum(weekly.values()) * 4
    return {
        "daily_cost": f"${sum(daily.values()):.2f}",
        "monthly_kimi_only": f"${sum(daily.values()) * 30:.2f}",
        "monthly_with_claude": f"${monthly:.2f}",
    }

# >>> research_agent_cost()
# {'daily_cost': '$0.20', 'monthly_kimi_only': '$6.00', 'monthly_with_claude': '$8.60'}

The biggest cost lever: max_articles_per_source. Dropping from 10 to 5 cuts daily cost nearly in half. Top 5 articles per source capture 90% of the signal.

What sources should you monitor by industry?

The sources.yaml is generic. Here are starter sets by field:

Industry	Source types	Example URLs	Check interval
Tech/AI	HN API, r/openclaw RSS, TechCrunch RSS	hacker-news.firebaseio.com, reddit.com/r/openclaw	2-6h
Real estate	Zillow API, MLS feeds, permit databases	zillow.com/howto/api, local county permit sites	12-24h
Finance	SEC EDGAR, earnings calendars, analyst feeds	sec.gov/cgi-bin/browse-edgar, seekingalpha.com/feed	4-8h
E-commerce	Competitor pricing pages, Amazon BSR, supplier dirs	competitor.com/pricing, jungle-scout feeds	6-12h
Healthcare	FDA approvals, ClinicalTrials.gov, PubMed RSS	clinicaltrials.gov/api, pubmed.ncbi.nlm.nih.gov	12-24h

The skill config stays the same across industries. Only the sources.yaml and baseline.md change.

What should you actually do?

• If you track one industry → start with 20 sources, not 50. Easier to tune the baseline. Scale up after week 2.
• If you need competitor intelligence → add their blog, job boards, and social accounts. The agent catches hiring patterns, product launches, and pricing changes before they’re announced.
• If you want the weekly deep-dive → route it through Claude. One Claude query per week costs $0.15 and produces genuinely useful trend analysis.

bottom_line

• The baseline file is the entire difference between a useful research agent and a noisy link dump. Spend 30 minutes writing it. Update it weekly.
• Companies pay six figures for the output you automate for $8. The difference isn’t quality. It’s that you built it yourself instead of hiring someone.
• The compounding intelligence effect is real. After a month, the agent identifies cross-day patterns you’d never catch reading individual articles. This is the closest thing to passive intelligence gathering that exists for a solo professional.