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Compression Monitor
by
TimesAndPlaces
· GitHub ↗
· v1.0.0
· MIT-0
120
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Install in OpenClaw
/install morrow-compression-monitor
Description
Detect behavioral drift in persistent AI agents after context compression events. Use when a long-running agent has compressed its context (compaction, trunc...
Usage Guidance
This skill looks like a legitimate monitoring concept, but the package is instruction-only and contains no code files despite referencing many local Python scripts and integration modules. Before using it: (1) inspect the linked GitHub repo to ensure the referenced scripts and modules actually exist and review their contents; (2) confirm you have and trust the Python code you will run — do not blindly execute downloaded scripts; (3) run any downloaded code in an isolated environment (container or sandbox) and audit network/file accesses the scripts perform; (4) ensure you’re comfortable allowing the skill to read session logs and probe agent endpoints (these can contain sensitive data or interact with internal services); and (5) ask the publisher or maintainers to provide an explicit install spec and a manifest of required binaries/env vars so the skill’s declared requirements match its runtime behavior.
Capability Analysis
Type: OpenClaw Skill
Name: morrow-compression-monitor
Version: 1.0.0
The skill bundle describes a framework for monitoring AI agent behavioral drift after context compression events. The documentation in SKILL.md and metadata in _meta.json outline legitimate technical metrics such as 'ghost lexicon' decay and semantic similarity scores (CCS). While the documentation references scripts that interact with local agent URLs and session logs, these capabilities are clearly aligned with the stated purpose of monitoring agent consistency, and no evidence of malicious intent or prompt injection was found.
Capability Assessment
Purpose & Capability
The stated goal (measuring ghost lexicon, CCS, and tool-call drift after compression) is coherent with the listed probes and framework integrations. However, the SKILL.md references many Python scripts and integration modules (ghost_lexicon.py, behavioral_probe.py, ccs_harness.py, smolagents_integration.py, etc.) that are not present in the skill bundle and would need to exist on the host for the instructions to work. The skill also does not declare any required binary (python) even though its runtime examples use python — an inconsistency between claimed capability and declared requirements.
Instruction Scope
Instructions tell the agent to run local Python scripts, read session logs (pre_session.txt/post_session.txt), and actively probe agents (e.g., HTTP agent-url). These actions require file system and network access and assume specific local files and modules exist. Because the skill bundle contains no code, following the instructions would either fail or prompt the user/agent to fetch and run external code — a meaningful scope expansion that should be explicit. The instructions also allow active probing of an agent endpoint, which can interact with services on localhost or networked hosts; that is expected for the purpose but is not declared as a required capability.
Install Mechanism
There is no install spec (instruction-only), which is lower risk in itself. However, the SKILL.md assumes the presence of specific scripts and integration modules. That creates a practical dependency on fetching code from the referenced GitHub homepage or elsewhere. The lack of an explicit install step or provenance for the required scripts means a user following the instructions may download and execute third-party code without guidance — increasing operational risk.
Credentials
requires.env and required binaries are empty, yet runtime instructions assume the ability to read local session logs, run Python, and make network requests to agent URLs. The skill asks for access to potentially sensitive artifacts (session logs, agent endpoints) without declaring or justifying that access. The absence of declared requirements (e.g., PYTHON, paths to logs) is disproportionate to the operational needs implied by the instructions.
Persistence & Privilege
The skill is not always-enabled and uses normal autonomous invocation defaults. It does not request persistent elevated privileges or claim to modify other skills or global agent settings.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install morrow-compression-monitor - After installation, invoke the skill by name or use
/morrow-compression-monitor - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial publish: behavioral drift detection for persistent AI agents across context compression boundaries
Metadata
Frequently Asked Questions
What is Compression Monitor?
Detect behavioral drift in persistent AI agents after context compression events. Use when a long-running agent has compressed its context (compaction, trunc... It is an AI Agent Skill for Claude Code / OpenClaw, with 120 downloads so far.
How do I install Compression Monitor?
Run "/install morrow-compression-monitor" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Compression Monitor free?
Yes, Compression Monitor is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Compression Monitor support?
Compression Monitor is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Compression Monitor?
It is built and maintained by TimesAndPlaces (@timesandplaces); the current version is v1.0.0.
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