chronulus
Skills for Claude Code, Claude.ai and OpenAI Codex that teach your agent how to use the Chronulus MCP server. This plugin includes two skills: predict and forecast.
The plugin also works on Claude.ai and ChatGPT.com, and the MCP server includes UIs built specifically for those platforms (such as the prediction and forecast scorecards).
predict
A skill that teaches Claude how to use the chronulus-mcp Chronulus MCP server to build reusable, calibrated probability predictors for prediction-market questions (Kalshi, Polymarket, and similar) — binary markets like "will A beat B?", "will X happen by date D?" or "will Y exceed a threshold?", and markets with more than two outcomes, like a soccer win/draw/loss line, an ordinal ladder (a rate-decision market), or an exclusive-winner field (an award category, a tournament, a multi-candidate race).
Category references
skills/predict/references/ has one guide per broad market category, each with
the market shapes, a session template, input fields, an evidence checklist and the
pitfalls specific to that category:
| Category | Reference | Detailed subcategory guides |
|---|---|---|
| Sports | sports/index.md | football, soccer, tennis, volleyball, basketball, e-sports (CS2, League of Legends, Dota 2, Valorant) |
| Elections | elections/index.md | planned |
| Politics and policy | politics/index.md | planned |
| Culture and entertainment | culture/index.md | planned |
| Crypto | crypto/index.md | planned |
| Commodities | commodities/index.md | planned |
| Climate and weather | climate/index.md | planned |
| Economics | economics/index.md | planned |
| Mentions | mentions/index.md | planned |
| Finance | finance/index.md | planned |
| Tech and science | tech-science/index.md | planned |
What it covers
- Setting up one session + one agent per (category, bet type) that gets reused across every future question, instead of recreating either per question.
- Two input schemas: a matchup schema (event-level fields plus symmetric
side1_*/side2_*fields) for contests, and a proposition/negation schema for thresholds, "by date" events, draws and multi-outcome markets. - Data-collection guidance: research instead of fabricate, explicit "not available" notes over blanks/guesses, and keeping evidence parallel between sides.
- The dual-framing technique — run every question twice with the framing swapped, then average the Beta parameters — to cancel directional framing bias before trusting or quoting a probability.
- Batching: running many predictions concurrently in one call with
batch_reuse_prediction_agents_and_get_predictions— both framings of a question, a slate of games, or every outcome of a multi-outcome market — including the 32-expert batch cap and per-item error handling. - Beyond binary: eliciting one dual-framed, debiased Beta per outcome (via a
proposition/negation input schema instead of fixed sides) and reconciling them into
a single Dirichlet with
reconcile_ordinal_outcome_dirichlet/reconcile_exclusive_outcome_dirichletand theirrender_*_dirichlet_scorecardcounterparts — covering ordinal outcomes (a draw between two win outcomes, a "hold" between cuts and hikes) as well as unordered exclusive-winner fields. - Comparing against market prices: unit conversion (Kalshi cents, Polymarket dollars), tradable prices net of fees, and keeping market prices out of the inputs.
forecast
A skill for producing time-series forecasts with the Chronulus NormalizedForecaster: no
historical data required, a 0–1 normalized series plus the agent's explanation, over a
horizon of hours, days or weeks.
- Setting up one session + one agent per forecasting use case and reusing it across
items that share an
input_data_model. - Writing the session
situation/task, designing the input fields, and the same research-don't-fabricate data-collection rules. - Setting
forecast_start_dt_str,time_scaleandhorizon_len. - Showing the result with
render_forecast_scorecard(plot plus explanation tooltip) instead of hand-building a chart. - Rescaling into real units with
y_min/y_maxandinvert_scale, including the exact formula, and when to userescale_forecastfor the numbers themselves. - The optional, on-demand risk assessment scorecard.
Requirements
A Chronulus account. This plugin bundles a connection to the hosted Chronulus
MCP server at https://mcp.chronulus.com/mcp (the production deployment of
chronulus-mcp) — no local install or
API key file needed. You'll be prompted to sign in via OAuth the first time a tool
from it is used.
Installation
Claude Code
Install via this marketplace:
/plugin marketplace add ChronulusAI/plugin-marketplace
/plugin install chronulus
Run /mcp afterward to confirm the Chronulus server connected and to complete
sign-in.
OpenAI Codex
Add the marketplace, then install chronulus from the Plugins directory in Codex:
codex plugin marketplace add ChronulusAI/plugin-marketplace