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Transforms single-attempt coding into a multi-attempt, test-validated refinement loop by running your actual test suite and feeding failures back to the LLM as
Transforms single-attempt coding into a multi-attempt, test-validated refinement loop by running your actual test suite and feeding failures back to the LLM as structured directives.
Swedish: the honer — from finslipa, meaning "to hone", "to fine-tune" or "to put the finishing touches on."
Finsliparn is a Bun-native MCP Server and Claude Code Plugin that transforms single-attempt coding into a multi-attempt, test-validated refinement loop. It adapts the philosophy of Poetiq's ARC-AGI solver to general software development.
LLMs often generate code that looks correct but fails in edge cases. Finsliparn solves this by enforcing a Refinement Loop:
"Test results are the objective truth—not diffs, not prompts, not opinions."
Finsliparn uses a Filesystem-as-IPC architecture to support multiple AI platforms with a single core engine.
graph LR
subgraph "AI Client"
Claude[Claude Code]
Copilot[GitHub Copilot]
end
subgraph "Finsliparn Core"
MCP[MCP Server]
Directive[Directive.md]
Tests[Test Runner]
end
Claude --"Hooks"--> MCP
Copilot --"Agent"--> MCP
MCP --"Writes"--> Directive
Directive --"Reads"--> Claude
Directive --"Reads"--> Copilot
MCP --"Executes"--> Tests
directive.md: The single source of truth. It tells the LLM exactly what to do next (e.g., "Fix failing tests in src/foo.ts").# Clone the repository
git clone https://github.com/jgabor/finsliparn.git
cd finsliparn
# Install dependencies
bun install
# Link the plugin (for Claude Code)
# (Coming soon)
# Start a refinement session
/finslipa Implement a fibonacci function in src/math.ts
Finsliparn will automatically intercept your edits, run tests, and guide you until the tests pass.
# Start the agent
copilot run --agent finsliparn "Implement a fibonacci function"
The agent will autonomously loop through the directive.md instructions until completion.
Jonathan Gabor
Выполни в терминале:
claude mcp add finsliparn -- npx Безопасность
Низкий рискАвтоматическая эвристика по публичным данным — не гарантия безопасности.