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Cm

Checked
dicklesworthstone·vlatest·ai-ml

Cm analyzes text to identify and extract core meaning, streamlining information processing for quicker understanding & summarization.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add dicklesworthstone/agent_flywheel_clawdbot_skills_and_integrations --skill cm -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

dicklesworthstone

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

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

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

  • No undeclared network calls: passed

    Only contacts known or declared hosts: raw.githubusercontent.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

    raw.githubusercontent.comsource host

  • 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.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

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

  • Pinned to a commit: passed

    Scanned dicklesworthstone/agent_flywheel_clawdbot_skills_and_integrations at 4d332440c3.

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

Commit
4d332440c3
Manifest
skills/cm/SKILL.md
Scanned
1 file, 754 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

Cm, or CASS Memory System, provides a procedural memory system for AI coding agents. It transforms scattered session data into persistent, cross-agent knowledge, allowing agents to learn from each other's experiences and avoid repeating past mistakes. The system uses a three-layer architecture (Episodic, Working, and Procedural Memory) to structure and distill information gained across various agents like Claude Code, Codex, Cursor, Aider, PI, Gemini, and ChatGPT.

When to use it

  • Before starting non-trivial coding tasks to leverage past experiences and avoid redundant work.
  • When encountering recurring bugs or issues that seem to require restarting from scratch.
  • To ensure knowledge gained in one agent (e.g., Cursor) is accessible and beneficial to other agents (e.g., Claude Code).
  • For cross-agent collaboration, allowing different AI coding tools to share learning and best practices.

Key capabilities

  • Cross-Agent Learning: Enables knowledge sharing between different AI coding agents.
  • Three-Layer Cognitive Architecture: Organizes memory into Episodic (raw logs), Working (summaries), and Procedural (rules) layers.
  • Context Retrieval: Provides relevant rules, anti-patterns, past sessions, and suggested searches based on the current task.
  • History Snippet Filtering: Allows filtering of historical data by source ("local" or "remote").
  • Confidence Decay System: Rules degrade in confidence over time without revalidation, with a 90-day half-life and a harmful multiplier for mistakes.

Example prompts

  • cm context "<your task>" - Retrieves relevant information for the specified task.
  • cm context "Debugging authentication errors" - Searches for past solutions to authentication issues.
  • historySnippets[].origin.kind is "remote" . origin.host : "workstation" - Filters history snippets to show only remote sessions from a workstation.

Tips & gotchas

  • Run the cm context "<your task>" command before starting any significant coding task to benefit from shared knowledge.
  • Be aware of the confidence decay system; rules lose effectiveness over time without feedback or revalidation.
  • The system relies on session data from various agents, so its usefulness depends on the quality and quantity of available information.