/goal for Claude Code: persistent tasks with adversarial review
The /goal command for Claude Code lets you set a persistent long task that keeps Claude working across many turns without stopping, and then double-checks the work at the end. By default, a second, independent Claude session reviews the result — so Claude can't just claim it's done.
Use Cases
- Building something from a spec: paste the requirements, walk away, come back when every item is actually implemented.
- "Keep going until the tests pass": Claude can't cheat by deleting failing tests, because the reviewer compares the repo against the original goal.
- Long research or prototype sessions: one goal keeps Claude going for hours instead of stopping after each turn.
Configuration
You can set an optional soft token budget, e.g. --tokens 250K, to tell Claude to wrap up when that budget is spent.
Audit strictness is configurable:
- adversarial (default) — a separate Claude session reviews the work
- self — Claude reviews itself (faster and free)
- off — no review
Switch with: /goal config set audit.mode <value>
Installation
From the plugin marketplace:
./plugin marketplace add balakumardev/claude /plugin install claude-code-goal
📖 Read the full source: r/ClaudeAI
👀 See Also
InsForge: Self-Hosted Postgres Backend with MCP Integration for AI Coding Agents
InsForge is an open-source, self-hosted backend alternative to Supabase that connects to Claude Code via MCP, allowing AI agents to see schema, policies, and service state. It includes PostgreSQL 16.4, PostgREST, Deno Runtime, auth, storage, and edge functions.
Decision Passport: An Audit Layer for AI Agent Execution Governance
The Claude Code leak highlights a gap in AI agent governance. Decision Passport addresses this with append-only execution records, portable proof bundles, and offline verification for tamper-evident audit trails.
Flash-MoE: Running 397B Parameter Qwen Model on MacBook Pro with Pure C/Metal
Flash-MoE is a pure C/Metal inference engine that runs Qwen3.5-397B-A17B, a 397 billion parameter Mixture-of-Experts model, on a MacBook Pro with 48GB RAM at 4.4+ tokens/second. The 209GB model streams from SSD through custom Metal compute shaders with no Python or frameworks.
Codebase Memory MCP: Graph-based code exploration for Claude Code
A developer built an MCP server that indexes codebases into a persistent knowledge graph using Tree-sitter and SQLite, reducing token usage by 20x on average for structural queries like call tracing and dead code detection.