Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Sightglass
Monitors AI coding agents to track dependency choices, classify discovery methods, flag risks, and reveal biases and missed alternatives in your project.
skill openclawclawhub Free
Monitors AI coding agents to track dependency choices, classify discovery methods, flag risks, and reveal biases and missed alternatives in your project.
β¬ 0 downloads β
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, analyze.sh, hooks/post-session.sh, hooks/pre-spawn.sh, setup.sh, skill.json
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- davidgeorgehope
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
π Sightglass β 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 β 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 β 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: ClassificationWhat It MeansRisk LevelTRAINING_RECALLAgent just "knew" it from training data β no search performedπ‘ MediumCONTEXT_INHERITANCEFound in existing project files (package.json, imports, etc.)π’ LowREACTIVE_SEARCHAgent hit a problem and searched for a solutionπ‘ MediumPROACTIVE_SEARCHAgent actively compared alternatives before choosingπ’ LowUSER_DIRECTEDHuman explicitly told the agent what to useβͺ None High TRAINING_RECALL percentages are a red flag β it means your agent is on autopilot, not thinking.
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 β file changes, package installs, tool calls.
4. Analyze
sightglass analyze # or ./skills/sightglass/analyze.sh --since "1 hour ago" --format json
Automatic Session Tracking
Sightglass provides pre/post hooks for coding agent sessions: Before a session β hooks/pre-spawn.sh: Records start time and project context Ensures the watcher daemon is running After a session β 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: π Session Summary Dependencies added: 12 Risks found: 3 Training recall: 67% Alternatives missed: 5 β οΈ 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.
CLI (@sightglass/cli)
CommandDescriptionsightglass initInitialize Sightglass in a project directorysightglass loginAuthenticate with sightglass.devsightglass setupInteractive first-time configurationsightglass watchStart the watcher daemonsightglass analyzeAnalyze agent sessions and dependency decisions
Skill Scripts
ScriptDescriptionsetup.shInstall CLI, configure, verify watcheranalyze.shStandalone analysis with --since, --session, --format, --push flagshooks/pre-spawn.shPre-session hook β records start, ensures watcherhooks/post-session.shPost-session hook β 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 β every package added, removed, or upgraded Discovery method β how the agent found each one (training recall vs. searched) Risk flags β known vulnerabilities, unmaintained packages, better alternatives Alternatives report β what the agent could have chosen but didn't consider Bias indicators β 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Scripts1 Docs1 Config
- SKILL.md
- analyze.sh
- hooks/post-session.sh
- hooks/pre-spawn.sh
- setup.sh
- skill.json