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๐Ÿ” Sightglass โ€” Agent Supply Chain Intelligence

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

by davidgeorgehope ยท published 2026-03-22

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2026-03
// Install command
$ claw add gh:davidgeorgehope/davidgeorgehope-sightglass
View on GitHub
// Full documentation

# ๐Ÿ” Sightglass โ€” 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 โ€” 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 โ€” 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)
  • Sightglass makes this invisible decision-making visible.

    Discovery Classification

    Sightglass classifies **how** your agent found each dependency:

    | Classification | What It Means | Risk Level |

    |---|---|---|

    | **TRAINING_RECALL** | Agent just "knew" it from training data โ€” no search performed | ๐ŸŸก Medium |

    | **CONTEXT_INHERITANCE** | Found in existing project files (package.json, imports, etc.) | ๐ŸŸข Low |

    | **REACTIVE_SEARCH** | Agent hit a problem and searched for a solution | ๐ŸŸก Medium |

    | **PROACTIVE_SEARCH** | Agent actively compared alternatives before choosing | ๐ŸŸข Low |

    | **USER_DIRECTED** | Human explicitly told the agent what to use | โšช None |

    High `TRAINING_RECALL` percentages are a red flag โ€” it means your agent is on autopilot, not thinking.

    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](https://sightglass.dev) to enable cloud analysis and history.

    3. Watch

    sightglass watch

    Starts the background watcher that monitors agent sessions โ€” 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** โ€” `hooks/pre-spawn.sh`:

  • Records start time and project context
  • Ensures the watcher daemon is running
  • **After a session** โ€” `hooks/post-session.sh`:

  • 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:

    ๐Ÿ“Š Session Summary
      Dependencies added: 12
      Risks found: 3
      Training recall: 67%
      Alternatives missed: 5
    
      โš ๏ธ  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`)

    | Command | Description |

    |---|---|

    | `sightglass init` | Initialize Sightglass in a project directory |

    | `sightglass login` | Authenticate with sightglass.dev |

    | `sightglass setup` | Interactive first-time configuration |

    | `sightglass watch` | Start the watcher daemon |

    | `sightglass analyze` | Analyze agent sessions and dependency decisions |

    Skill Scripts

    | Script | Description |

    |---|---|

    | `setup.sh` | Install CLI, configure, verify watcher |

    | `analyze.sh` | Standalone analysis with `--since`, `--session`, `--format`, `--push` flags |

    | `hooks/pre-spawn.sh` | Pre-session hook โ€” records start, ensures watcher |

    | `hooks/post-session.sh` | Post-session hook โ€” analyzes and summarizes |

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

    All data syncs to [sightglass.dev](https://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.*

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