Soplint

soplint

**Lint rules for your AI agent's discipline, not its code.**

I've run a personal AI agent 24/7 for a year. It writes good code — that was never the problem. The problem was discipline: it said "fixed and verified" without verifying, silently reversed its own judgments, and made policy decisions that rotted in memory while its behavior drifted back to old habits.

Code linters catch style violations. Memory tools catch broken links and stale notes. Harness linters (e.g. [AgentLint](https://github.com/0xmariowu/AgentLint)) check that your rules files are well-written. soplint checks something else: did the agent actually **keep the working agreements you made with it**?

**Start with the engine first.** soplint is the guardrail, not the runtime: it audits discipline after you already have an agent that can run. If you want a local agent that can take tasks, run workers, and leave file-based evidence, start with [aiwff-runtime](https://github.com/zaxardery8011-design/aiwff-runtime). 中文入口: [小主腦導入頁](https://zax.com.tw/minibrain)。

How it works

Three mechanisms, all extracted from a year of real incidents:

1. Belief revision audit trail

Every time your agent overturns a prior judgment, it must log it:

Import-Module ./lib/BeliefLog.psm1
Add-BeliefRevision -From "Assumed the cache layer was thread-safe" `
                   -To "Race confirmed under load; needs a lock" `
                   -Trigger estimate_correction -ConfidenceShift "high->low"

One JSON line per revision (`from_belief`, `to_belief`, `trigger`, `confidence_shift`), appended to a greppable JSONL file. The `trigger` field is a deliberate enum — free-text triggers turn an audit log into noise.

Why external file instead of a prompt rule? Because prompt rules fail. I watched my agent violate a written "acknowledge belief changes" rule three times in one day. An agent can ignore an instruction silently; a missing or stale log file is loud — and a lint check audi