Agent Capability Substitution Resolver
Assess a proposed tool replacement against declared schemas, semantic identifiers, side effects and supplied paired fixtures. Auditable JSON; no tool execution.
14%
users
Apify builder
Alessandro Bonometti I build focused automation tools that turn supplied evidence into structured, testable outputs.
Leaderboard position
out of 10,354
New actors
#1,188
↓ -109
Total actors
#2,213
↑ +972
Monthly users
#2,702
↑ +814
Total users
#4,114
↑ +907
Monthly runs
#2,857
↑ +3,501
Total runs
#5,752
↑ +1,379
Portfolio stats
New actors
5
↑ +3
Total actors
7
↑ +5
Monthly users
7
↑ +5
Total users
14
↑ +10
Monthly runs
152
↑ +141
Total runs
195
↑ +175
New actors
Each bar shows one day in the last 30 days.
Portfolio concentration
6 of 7 actors cover 80%+ of monthly users.
Assess a proposed tool replacement against declared schemas, semantic identifiers, side effects and supplied paired fixtures. Auditable JSON; no tool execution.
14%
users
Evaluate whether an AI agent should buy or execute a paid action. Deterministic expected-value math compares baseline, proposed action and substitutes. Returns BUY, SKIP, STOP or ESCALATE, a rational price ceiling and structured reasons.
14%
users
Check structured evidence against source, freshness and confidence requirements. Return SUPPORTED, CONTRADICTED or INSUFFICIENT with auditable reasons. No LLM calls.
14%
users
Calculate future obligations, cancellation costs and hold liquidity from structured transaction terms. Exact decimal amounts; explicit unknown liability.
14%
users
Find missing verification obligations across reachable agent execution paths. Structured invariants, supplied evidence and auditable gaps. No test execution.
14%
users
Resolve conflicting structured state reports using per-field source authority, freshness and explicit invariants. Auditable JSON; abstains on unresolved conflicts.
14%
users
ActorHunt MCP
ActorHunt gives paid members MCP access to keyword data, actor evidence, and Store movement for deciding what to build next.