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Loom Learning Graph

Listed
pepicrft·v1.0.0·agents·MIT

Build and expand a personal knowledge graph with spaced repetition reviews. Organizes learning paths as atomic nodes with prerequisites, unlocks, and check-yourself prompts.

Repository

Install on your platform

Showing OpenClaw.

  1. 1

    Run this in your terminal

    terminal
    $ openclaw skills install openclaw-loom

What we know about this skill

Publisher

pepicrft

Taken from the source listing. We haven't verified who controls this account.

Install target

Pinned to 2c0f2be9

Installs fetch a stored snapshot of that commit, so the code can't change after the fact.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 3 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    SKILL.md has no YAML frontmatter, so it declares nothing machine-readable.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: failed

    Contacts 2 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: opencollective.com, tidelift.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    opencollective.comundeclaredgithub.comsource hosttidelift.comundeclared

    What it found (3 of 12)

    • package-lock.json:808contacts opencollective.com
      "url": "https://opencollective.com/vitest"
    • package-lock.json:823contacts opencollective.com
      "url": "https://opencollective.com/vitest"
    • package-lock.json:848contacts opencollective.com
      "url": "https://opencollective.com/vitest"

  • No pipe-to-shell installers: failed

    Runs code fetched at install time in 1 place.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

    What it found

    • src/index.ts:68npm install straight from a URL
      execShell("npm install -g https://github.com/tobi/qmd");

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • Pinned to a commit: passed

    Scanned pepicrft/openclaw-plugin-loom at 2c0f2be96a.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
2c0f2be96a
Manifest
SKILL.md
Scanned
12 files, 3,465 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

OpenClaw Loom transforms your learning materials into an interactive graph of interconnected knowledge. It combines mastery learning with spaced repetition and retrieval practice, allowing you to track progress through nodes, paths, and context captures all indexed for fast search within Markdown and QMD files. The system automatically recommends next steps and unlocks content based on your performance.

When to use it

  • Structured Learning: Organize complex topics like Nix configuration or German grammar into a clear learning path with prerequisites and dependencies.
  • Mastery Tracking: Monitor progress through a curriculum, ensuring each concept is truly understood before moving on.
  • Real-World Application: Integrate "context captures" – real-life situations – to apply learned concepts in practical scenarios.
  • Efficient Review: Leverage spaced repetition scheduling to reinforce knowledge and prevent forgetting.

Key capabilities

  • Learning graph with prerequisites, unlocks, and mastery states
  • Spaced repetition scheduling with review ratings
  • Next-node recommendations with automatic unlocks
  • Context captures for real-life application
  • Local-first Markdown/QMD library with folder structure
  • Semantic search using QMD indexing

Example prompts

  • "Show me my next recommended node in the Nix path."
  • "What are the prerequisites for understanding 'nix/derivations'?"
  • "Review all nodes due today."

Tips & gotchas

  • Nodes should be narrow, testable, and self-contained to maximize effectiveness.
  • Use wikilinks (e.g., [[nix/derivations]]) within notes to connect them to other nodes in your learning graph.

Tags

learningknowledge-graphspaced-repetitioneducationnotes