Problem-solving guide

How to share AI coding run logs with a team

Preserve enough task, repository, check, and recovery context for review without sharing secrets or an unfiltered model transcript.

Problem and general approach

A team needs task, repository, changed files, checks, retries, stop reason, provider context, and human intervention to reproduce and challenge an AI-assisted change. Share a redacted run summary and project review record, not an unfiltered conversation.

Where ad-hoc workflows fall short

Chat exports often omit final file state and blur a proposed patch with an applied change. They may also contain bearer tokens, API keys, prompts, proprietary source, or customer data.

When minitok fits

Use minitok when a local run record and structured evidence make repository work easier to inspect later. This supports review and handoff; it is not automatically a team-hosted audit system.

minitok status
minitok run list
minitok run show <run-id>

Review .minitok/last-run.json, .minitok/evidence/runs/ when enabled, and .minitok/last-run.patch when a patch was preserved. Redact API keys, bearer tokens, passwords, private keys, customer data, and proprietary source.

Alternatives and limits

A pull request, CI artifact, issue tracker, or approved internal evidence store may better fit a team's retention and access policy. Follow repository confidentiality rules and label fixtures separately from real execution evidence.