ClawHub

Attention Research Pipeline

Workflows

Scheduled intelligence research pipeline — monitors topics on a twice-daily cadence, produces signal-first digests, maintains META.json freshness state. Use for tracking geopolitical conflicts, AI trends, macro signals, climate, and biotech with structured state and delta-based updates.

Install

openclaw skills install attention-research

attention-research Skill

Scheduled intelligence research pipeline with topic monitoring, freshness state, signal-first digest delivery, and per-topic alerting based on topic-defined threshold criteria.

What It Is

A twice-daily research cadence (morning + afternoon) that:

  • Scans configured topics via the agent's web search tool
  • Maintains per-topic freshness state in META.json
  • Uses each PROMPTS/TOPICS/<topic>.md file as the standing monitoring note for that topic
  • Produces digests that connect signals, not just dump headlines
  • Alerts the user when key events or threshold criteria defined by the topic are met
  • Delivers via Telegram or WhatsApp

Core Concepts

Topic

A long-running monitoring domain (e.g., us-iran-conflict, ai, geopolitics).

Topic Monitoring Note

PROMPTS/TOPICS/<topic>.md is not just setup text. It is the standing monitoring note for that topic. It should contain the topic's methodology, analysis framework, entity framing, signal criteria, watch items, and threshold/key event logic used to decide when the user should be alerted.

Thread

An active research question within a topic. Threads have state, typed connections, and delta updates.

Digest

The default output surface. A structured readout of what changed and why.

META.json Freshness Contract

Every topic has a META.json that acts as a shared freshness marker for all writers:

  • Morning and afternoon slots have independent timestamps
  • Max 2 retries per topic per day total
  • After 2 failures → topic skipped for that day

Research Root

$HOME/.openclaw/workspace/docs/research/

Package Structure

attention-research/
├── PROMPTS/
│   ├── CORE/                    # Generic analysis framework shared across topics
│   │   ├── system-prompt.md
│   │   ├── signal-rules.md
│   │   └── digest-format.md
│   ├── TOPICS/                  # Topic-local methodology and monitoring notes
│   │   ├── us-iran-conflict.md
│   │   ├── ai.md
│   │   ├── geopolitics.md
│   │   ├── finance-markets.md
│   │   ├── climate-changes.md
│   │   └── bio-tech.md
│   ├── TEMPLATES/
│   │   ├── morning-research.md
│   │   ├── afternoon-research.md
│   │   └── onboarding.md
│   └── GENERATOR/
│       └── generator.md
├── CONFIG/
│   ├── topics.yaml
│   └── default-paths.yaml
├── SCHEMA/
│   └── META.json.template
├── SCRIPTS/
│   ├── research-executor.sh
│   └── setup-cron.sh
├── INSTALL/
│   └── install.sh
├── SKILL.md
├── README.md
└── package.json

Installation

# Install via clawhub (after publishing)
clawhub install attention-research

# Or install directly from GitHub with git
mkdir -p ~/.openclaw/skills
git clone https://github.com/riverho/attention-research.git ~/.openclaw/skills/attention-research
cd ~/.openclaw/skills/attention-research
bash INSTALL/install.sh --fresh

# Or install directly from GitHub with curl
mkdir -p ~/.openclaw/skills && \
cd ~/.openclaw/skills && \
curl -L https://github.com/riverho/attention-research/archive/refs/heads/master.tar.gz \
  | tar -xz && \
mv attention-research-master attention-research && \
cd attention-research && \
bash INSTALL/install.sh --fresh

Configuration

Agent onboarding contract

Any agent using this skill should follow this order:

  1. Install the repo or skill files
  2. Ask the user to confirm the default research root:
    • preferred: ~/.openclaw/workspace/docs/research
    • fallback: ~/docs/research
  3. Check delivery configuration (Telegram chat ID or WhatsApp recipient)
  4. Present the default OSS topic set:
    • us-iran-conflict
    • ai
    • finance-markets
  5. For each selected topic, read PROMPTS/TOPICS/<topic>.md and extract the key entities / entity framework back to the user before activation
  6. Ask whether to activate the topic
  7. Ask whether to register the morning and afternoon cron jobs
  8. Only after approval: update config, run setup, and activate monitoring

topics.yaml — What to Track

topics:
  us-iran-conflict:
    display_name: "US-Iran Conflict"
    description: "US-Iran tensions, Hormuz, nuclear talks, sanctions"
    enabled: true
    search_query: "US Iran conflict Hormuz nuclear talks"

Delivery Channel

Edit CONFIG/default-paths.yaml:

delivery:
  telegram:
    chat_id: "YOUR_CHAT_ID"

Cron Jobs

JobSchedulePurpose
AR morning digest08:00 HKTMorning research scan
AR afternoon update16:00 HKTAfternoon research scan

Publishing

clawhub publish ./attention-research \
  --slug attention-research \
  --name "Attention Research Pipeline" \
  --version 1.0.0 \
  --changelog "First public OSS cut"

Requirements

  • Python 3 + PyYAML
  • OpenClaw with cron daemon
  • A web search tool available to the agent (agent-chosen: Tavily, Brave, native, etc.)
  • Telegram or WhatsApp delivery channel

Agent operating rule

Agents should treat this skill as an attention-research workflow, not just a folder of prompts.

On every run:

  • follow the prompt stack order
  • read PROMPTS/TOPICS/<topic>.md as the live monitoring note for that topic
  • use the topic's own methodology and key-event criteria to judge significance
  • keep runtime artifacts isolated per topic under the research root
  • alert the user when threshold or key-event criteria in the topic file is met
  • do not let one topic's analysis framework bleed into another topic's reasoning
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