🔍 Sightglass — Agent Supply Chain Intelligence

Your AI coding agent just added 47 dependencies to your project. Do you know why it picked any of them?

Sightglass instruments AI coding agents to capture every tool selection, dependency install, and architectural choice — then surfaces risks, biases, and better alternatives you never saw.

Why This Matters

When a human developer picks a dependency, there's a reasoning trail: blog posts read, alternatives compared, team discussions had. When an AI agent picks one, that trail is invisible. The agent "just knows" packages from training data — which means it's biased toward:

• Whatever was popular when training data was cut off
• Packages with the most Stack Overflow mentions (not the best packages)
• Dependencies it's seen in similar projects (not necessarily right for yours)

Discovery Classification

Sightglass classifies how your agent found each dependency:

Quick Start

1. Setup

./skills/sightglass/setup.sh

This installs the CLI (@sightglass/cli), runs initial configuration, and checks the watcher daemon.

2. Login

sightglass login

Authenticate with sightglass.dev to enable cloud analysis and history.

3. Watch

sightglass watch

Starts the background watcher that monitors agent sessions — file changes, package installs, tool calls.

4. Analyze

sightglass analyze
# or
./skills/sightglass/analyze.sh --since "1 hour ago" --format json

OpenClaw Integration

Automatic Session Tracking

Sightglass provides pre/post hooks for coding agent sessions:

Before a session — hooks/pre-spawn.sh:

• Records start time and project context
• Ensures the watcher daemon is running

• Runs analysis on everything that happened
• Outputs a summary: risks found, training recall %, alternatives missed

Using with a Coding Agent

When you spawn a coding agent through OpenClaw, wrap it with Sightglass:

# Before spawning
source ./skills/sightglass/hooks/pre-spawn.sh /path/to/project

# ... agent does its work ...

# After session ends
./skills/sightglass/hooks/post-session.sh

The post-session output looks like:

📊 Session Summary
  Dependencies added: 12
  Risks found: 3
  Training recall: 67%
  Alternatives missed: 5

  ⚠️  Run 'sightglass analyze --since ...' for details

67% training recall means two-thirds of the packages were grabbed from memory with zero comparison shopping. Sightglass will show you what alternatives existed.

Commands Reference

CLI (@sightglass/cli)

Skill Scripts

analyze.sh Flags

--since <time>     Analysis window start (ISO timestamp or relative like "1 hour ago")
--session <id>     Analyze a specific session by ID
--format <fmt>     Output format: text (default), json, markdown
--push             Push results to https://sightglass.dev

What Sightglass Surfaces

For each agent session, you get:

• Dependency inventory — every package added, removed, or upgraded
• Discovery method — how the agent found each one (training recall vs. searched)
• Risk flags — known vulnerabilities, unmaintained packages, better alternatives
• Alternatives report — what the agent could have chosen but didn't consider
• Bias indicators — patterns showing training data influence over reasoned choice

API

All data syncs to sightglass.dev when authenticated. Use --push with analyze or configure auto-push in setup.

Your agent's dependencies are your dependencies. Know where they came from.