Optimal-path problem solving for AI agents — triage, prune, investigate. MCP server + agent skill combining Newton's Analysis-Synthesis method, NASA P10 rules, and heuristic-search pruning so agents n
Optimal-path problem solving for AI agents Triage · Prune · Investigate — never overcomplicate
inquisitor makes AI agents solve problems the way a chess engine plays chess: it cannot explore every branch, so it estimates complexity first, prunes paths that add no information, and spends its search budget only where the problem actually is.
- MCP server (inquisitor-mcp) — the engine. Web search, project analysis, code tracing, project scaffolding, and a persistent investigation state machine. Works with any MCP-compatible agent: OpenCode, Claude Code, Claude Desktop, Cursor. - Agent skill (skills/inquisitor/SKILL.md) — the behavioral layer. Injects the triage heuristic, the pruning rules, and the full methodology into the agent's reasoning.
The methodology synthesizes four sources:
Source Contribution ---------------------- Newton's Opticks (1704) Analysis→Synthesis method: define, decompose, experiment, reconstruct, and end with open Queries — hypotheses non fingo NASA/JPL Power of Ten 10 hard rules, few enough to remember, strict enough to check mechanically Karpathy's LLM coding guidelines Think before coding · simplicity first · surgical changes · goal-driven execution Ponytail decision ladder YAGNI → reuse → stdlib → native → installed dep → one line → minimum code
Web search, codebase scans, and code tracing are tools invoked when local evidence is insufficient — never mandatory rituals.
From the project README.
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