An evidence-based decision-making framework for pregnancy health applications, applying economic methodology to risk assessment and clinical evidence.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install expecting-better
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install expecting-better using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Expecting Better — Data-Driven Pregnancy Decision-Making?
The Expecting Better skill translates the analytical framework of Emily Oster's landmark book into an actionable, rule-based AI routing schema. Designed for integrations within Openclaw Skills, this tool enables developers to build pregnancy and parenting applications that bypass blanket medical dogmas in favor of objective, data-driven reasoning.
By prioritizing absolute risk, evaluating the evidence quality ladder, and acknowledging confounding variables, this skill empowers agents to guide users through complex health choices like caffeine consumption, prenatal screenings, and labor interventions. Utilizing this in your Openclaw Skills workspace ensures your health AI provides highly nuanced, evidence-based, and value-validated support.
Expecting Better — Data-Driven Pregnancy Decision-Making Use Cases
- Evaluating the safety of dietary substances such as caffeine, alcohol, fish, and deli meats during pregnancy.
- Helping users interpret prenatal screening and diagnostic test risks including amniocentesis, CVS, and NIPT.
- Analyzing labor and delivery interventions, such as debunking epidural myths or estimating induction risks.
- Providing analytical frameworks for resolving conflicting pregnancy advice from different sources.
- Navigating queries on maternal age, fertility decline, and miscarriage rates with absolute risks.
How Expecting Better — Data-Driven Pregnancy Decision-Making Works
- Triage and Intent Routing: The agent intercepts a pregnancy-related query and maps it to a specific topic (Diet, Risk, Labor, or Methodology) using the defined routing table.
- Reference Retrieval: The skill loads the corresponding data reference (from ref-01 to ref-05) containing clinical trials, observational studies, and statistical outcomes.
- Evidence Ranking: The agent evaluates claims using the evidence quality ladder, prioritizing randomized controlled trials (RCTs) over observational studies.
- Risk Calculation: Relative risks are automatically converted to absolute terms (e.g., expressing a 50% relative increase as an absolute risk of '1 in 8,000') to avoid fear-based framing.
- Personalized Evaluation: The agent presents options neutrally, validating the user's personal values and prompting cost-benefit trade-offs.
Expecting Better — Data-Driven Pregnancy Decision-Making Setup
To deploy this capability within your agentic ecosystem, clone the repository and configure it inside your skill path. Here is how to configure it in your Openclaw Skills project:
# Navigate to your Openclaw Skills workspace
cd my-agent/skills
# Clone or download the Expecting Better skill specification
git clone https://github.com/openclaw/skill-expecting-better.git
# Initialize and verify the routing integration
openclaw register expecting-better
Ensure that your LLM prompts are configured to default to English and enforce the 7 Key Principles during medical triage.
Expecting Better — Data-Driven Pregnancy Decision-Making Data Schema & Taxonomy
The skill organizes its analytical framework into a structured reference schema mapping intent to empirical data:
| Reference ID | Domain | Primary Data Focus | Key Statistical Cases |
|---|---|---|---|
ref-01 |
Methodology | Causal inference vs correlation | Confounder analysis, 1800s fertility datasets |
ref-02 |
Diet & Substances | Absolute risk of ingestible items | Caffeine limits, alcohol studies, listeria ratios |
ref-03 |
Prenatal Health | Physical activity and morning sickness | Exercise safety, bed rest harms, nausea protection |
ref-04 |
Labor & Delivery | Clinical outcomes of delivery choices | Epidural C-section RCTs, 39-week induction data |
ref-05 |
Risk Assessment | Maternal age and prenatal testing rates | Amniocentesis vs Down syndrome risk curves |
Expecting Better — Data-Driven Pregnancy Decision-Making Advanced Features
- Structured Intent Routing Table: Dynamically routes conversational inputs directly to specific evidentiary reference blocks.
- Absolute Risk Translation Engine: Converts scary relative-risk statistics into calm, baseline-vs-increment absolute fractions.
- Evidence Quality Ladder: Built-in mechanism to weigh systematic reviews and RCTs heavily over expert opinion or anecdotal lore.
- Controversial Position Guardrails: Explicitly tags high-disagreement zones (like light second-trimester alcohol and breastfeeding confounders) to present multi-perspective balance.
- Value-Driven Decision Framework: Prompts agents to refuse universal 'one-size-fits-all' answers, forcing a balance of maternal values with clinical statistics.
SKILL.md
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