Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Sightglass monitors AI coding agent sessions to track how dependencies get selected, classifying each one by how the agent discovered it — training recall, context inheritance, search, or user direction. It surfaces dependency risks, missed alternatives, and bias patterns after each session. Data syncs to sightglass.dev when authenticated.
Unlike manual post-hoc dependency review, Sightglass captures the discovery method at session time, making it possible to distinguish reasoned choices from autopilot training recall.
When to use it
- Auditing which packages an AI agent added without explicit instruction
- Checking training recall percentage after a large agent-driven refactor
- Comparing what alternatives the agent ignored when picking a library
- Flagging unmaintained or vulnerable packages introduced by an agent session
- Reviewing dependency history across multiple agent sessions on a project
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: analyze what my agent just did in the last hour
- 1Run pre-spawn.sh before starting the coding agent session to record start time and ensure watcher is running
- 2Agent completes its work, installing dependencies and making architectural choices
- 3Run post-session.sh to trigger analysis of the completed session
- 4Review the summary output showing dependencies added, risk count, training recall percentage, and missed alternatives
- 5Run sightglass analyze --since '1 hour ago' --format json for full detail
Session report listing 12 dependencies added, 3 risks, 67% training recall, and 5 alternatives the agent never considered
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.