Claude-skill-registry rfi

Crowdsourced forecasting questions and predictions from the RAND Forecasting

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/other/other/sdk-rfi" ~/.claude/skills/majiayu000-claude-skill-registry-rfi && rm -rf "$T"

manifest: skills/other/other/sdk-rfi/SKILL.md

RAND Forecasting Initiative (RFI)

SDK that provides access to the RAND Forecasting Initiative crowdsourced forecasting platform (formerly INFER, powered by Cultivate Labs). It covers policy-relevant forecasting questions with aggregated crowd probabilities and individual forecaster predictions with rationales.

Quick Start

from sdk_rfi import Client

client = Client()  # Uses RFI_EMAIL and RFI_PASSWORD env vars

Key Methods

Method	What it does
`client.questions.list(...)`	List forecasting questions with filtering by status, tags, challenges, date ranges
`client.questions.get(question_id)`	Get a specific question with answers and crowd probabilities
`client.prediction_sets.list(question_id=...)`	Get individual forecaster predictions with rationales for a question
`client.comments.list(commentable_id=..., commentable_type=...)`	Get discussion comments on a question

Data Coverage

Domain: Politics / policy forecasting
Countries/Regions: Global (US policy focus)
Time range: Questions from ~2020 to present
Update frequency: Event-driven (new questions published, forecasts updated continuously)
Key data: Forecasting questions on geopolitics, national security, science/technology policy, economics, biosecurity, nuclear risk, AI governance

Forecasting Patterns

Crowd forecast as base rate: Use
```
client.questions.list()
```
to find questions matching your topic, then read the crowd probability from
```
question.answers[i].probability
```
as a calibrated starting point.
Trend analysis via prediction history: Use
```
client.prediction_sets.list(question_id=X)
```
to see how individual forecasts changed over time. Plot
```
created_at
```
vs
```
forecasted_probability
```
to detect momentum.
Expert rationale mining: Individual prediction sets include
```
rationale
```
text explaining the forecaster's reasoning. Use
```
client.prediction_sets.list(question_id=X)
```
and examine rationales for weak signals and arguments.
Cross-reference with resolution: Use
```
client.questions.list(status="closed")
```
to find resolved questions. Compare crowd probability at various cutoff dates against actual outcomes to measure calibration.
Comment-based signal detection: Use
```
client.comments.list(commentable_id=X, commentable_type="Forecast::Question")
```
to find discussion threads that may contain links to evidence or emerging developments.

Common Queries

# Get all active forecasting questions
questions = client.questions.list()
for q in questions.questions:
    print(f"{q.id}: {q.name}")
    for a in (q.answers or []):
        print(f"  {a.name}: {a.probability_formatted}")

# Get individual forecasts and rationales for a question
forecasts = client.prediction_sets.list(question_id=1234)
for ps in forecasts.prediction_sets:
    print(f"{ps.membership_username}: {ps.rationale}")
    for pred in (ps.predictions or []):
        print(f"  Answer {pred.answer_id}: {pred.forecasted_probability:.1%}")

# Backtest: get data as it was available on a past date
past_questions = client.questions.list(cutoff_date="2025-06-01")
past_forecasts = client.prediction_sets.list(question_id=1234, cutoff_date="2025-06-01")

Full Method Reference

See

references/methods.md

for all 4 methods with complete parameter details.