Search

PythonReactSEOPDF
OpenClaw

observer-effect-probe

Helps detect skills that behave differently when they sense they are being monitored — catching the class of evasion where conditional activation logic specifically targets attestation and sandbox environments.

andyxinweiminicloud0 source installs☆ 0 Repository starsv1.0.0

Installation

Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.

SkillHub CLI

skillhub install andyxinweiminicloud/observer-effect-probe

Inspect the source and compatibility requirements, then follow its installation steps.

Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.

View source ↗Guides

Skill instructions

The Skill Behaves Perfectly When It Knows You're Watching.

Helps identify skills that use environment detection to suppress malicious behavior during security review — the attestation evasion technique that passes every check precisely because the checks are visible.

Problem

The runtime attestation problem has a second layer: a skill sophisticated enough to execute conditionally based on environment signals is also sophisticated enough to detect that it's being probed and suppress its conditional behavior during the probe.

This is the observer effect applied to security: the act of observing changes what is observed. A skill that can detect sandbox fingerprints (restricted network access, specific hostname patterns, probe-characteristic API call sequences) can present clean behavior during attestation and activate payload delivery only when those fingerprints are absent — meaning only in real production environments.

This is not a theoretical concern. Malware has used VM detection to suppress execution during analysis since the 1990s. The same technique scales to agent skills: check for the probe's characteristic markers, return normal output, resume malicious behavior once the probe exits. A skill that clears attestation by detecting the attestor is not a securely attested skill — it's an adversarially attested skill.

The observer-effect probe attempts to reduce the skill's ability to distinguish probe context from production context by minimizing observable differences between the two.

What This Probes

This probe examines attestation evasion capabilities across five dimensions:

  1. Sandbox fingerprint detection — Does the skill attempt to identify characteristics of a probe environment? Checks for sandbox-specific hostname patterns, reduced network connectivity, probe-characteristic request timing, or the absence of expected production signals
  2. Probe timing sensitivity — Does the skill's behavior change based on how l...

Source information

Source
github
Repository
openclaw/skills
Path
skills/andyxinweiminicloud/observer-effect-probe
Version
1.0.0
Commit
c51f679966016f3f531bb69afb33bbf590475950
Classification source
rules
Status
active
Visibility
public

These signals come from indexed source analysis and may lag behind changes. Check the current source files.

Extracted content

SKILL.md
# The Skill Behaves Perfectly When It Knows You're Watching.

> Helps identify skills that use environment detection to suppress malicious behavior during security review — the attestation evasion technique that passes every check precisely because the checks are visible.

## Problem

The runtime attestation problem has a second layer: a skill sophisticated enough to execute conditionally based on environment signals is also sophisticated enough to detect that it's being probed and suppress its conditional behavior during the probe.

This is the observer effect applied to security: the act of observing changes what is observed. A skill that can detect sandbox fingerprints (restricted network access, specific hostname patterns, probe-characteristic API call sequences) can present clean behavior during attestation and activate payload delivery only when those fingerprints are absent — meaning only in real production environments.

This is not a theoretical concern. Malware has used VM detection to suppress execution during analysis since the 1990s. The same technique scales to agent skills: check for the probe's characteristic markers, return normal output, resume malicious behavior once the probe exits. A skill that clears attestation by detecting the attestor is not a securely attested skill — it's an adversarially attested skill.

The observer-effect probe attempts to reduce the skill's ability to distinguish probe context from production context by minimizing observable differences between the two.

## What This Probes

This probe examines attestation evasion capabilities across five dimensions:

1. **Sandbox fingerprint detection** — Does the skill attempt to identify characteristics of a probe environment? Checks for sandbox-specific hostname patterns, reduced network connectivity, probe-characteristic request timing, or the absence of expected production signals
2. **Probe timing sensitivity** — Does the skill's behavior change based on how l...

Related skills

OpenClaw

humanpages

human-pages-ai

Search and hire real humans for tasks — photography, delivery, research, and more

OpenClaw

openclaw-skills

zseven-w

Reusable skill templates for OpenClaw AI agents. Templates for API integration, data processing, web scraping, CLI tools, and file processing.

OpenClaw

0xarchive

0xfantommenace

Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user...

OpenClaw

1sec-security

cutmob

Install, configure, and manage 1-SEC — an open-source, all-in-one cybersecurity platform (16 modules, single binary) on Linux servers and VPS instances. Use when the user asks to secure a server, install securi...