hunt
Run multiple independent (fetch, plan, score) strategies concurrently, then merge and rank all results into a single leaderboard to compare yields across different hunt framings.
Instructions
Run multiple (fetch → map → score) hunt strategies in parallel and merge.
Each strategy is an independent (fetch, plan, score) triple with its own seed prompt or explicit seeds, plan template, and rubric. Strategies run concurrently; a failure in one does not halt the others. All scored refs across all strategies are merged into a single globally-ranked leaderboard so you can see which strategy produced the highest-yield finding.
This is the compound combinator that makes "run all the hunt framings at once" tractable — rather than sequentially trying one hunt shape at a time, you parallelise across framings (problem-driven, gap-in-field, broken-claims, tool-landscape, connection) and let the scoring sort them.
Strategy dict fields:
name(required) — short label used for tagging and per-strategy reporting in the leaderboard.seeds(optional) — pre-supplied list of seed strings (each becomes a map input). Exactly one ofseedsorfetcher_promptmust be set.fetcher_prompt(optional) — prompt sent to a singleruncall that must emit a JSON array of seed strings (or a JSON object with aseedskey). The agent runs with network enabled.planner_template(required) — prompt template for the map step. Use{input}as the placeholder for each seed.rubric(required) — evaluator directive for the score step. Any form accepted by_evaluate_node:validate:<type>,score:<criterion>, orexec:<cmd>.top_k(optional, default 3) — how many winners this strategy contributes to the unified leaderboard.model_planner(optional, default "haiku") — model for the map step.model_fetcher(optional, default "haiku") — model for the fetch step.timeout(optional) — per-agent timeout in seconds.
Args: strategies: JSON array of strategy dicts (or a Python list). max_parallel_strategies: Upper bound on strategies running at once (default: 5). Each strategy internally parallelises its map step. top_k_global: Size of the unified leaderboard across all strategies (default: 10).
Returns:
JSON with run_id, strategies (per-strategy summary including
name, error-if-any, total_cost, winner_ref, best_score), and
leaderboard (globally-ranked list tagged by strategy). The full
per-ref scoring trace lives at strategy_details[i].ranked.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| strategies | Yes | ||
| max_parallel_strategies | No | ||
| top_k_global | No |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |