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observer-effect-probe观察者效应探针

Agent Skill

observer-effect-probe 用于补充运维相关能力,适合在 OpenClaw 中需要让 Agent 承接运维相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

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周安装

559

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下载量

4,561
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:observer-effect-probe(观察者效应探针)
来源仓库:https://github.com/andyxinweiminicloud/observer-effect-probe
安装命令:
openclaw skills install observer-effect-probe
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install observer-effect-probe

简介

observer-effect-probe 用于检测被监视时的行为变化。

  • 捕获条件激活逻辑指定的逃避类别模式。
  • 适合在监控系统有效性评估中使用。observer-effect-probe 属于运维类 Skill,可作为该场景下的辅助能力补充。
  • 通过 clawhub 安装,需明确探测范围与误报容忍度。
  • 输出为实验性结果,不能作为唯一判断依据。

SKILL.md

name
observer-effect-probe
description
>
version
1.0.0
metadata
openclaw
requires
bins
[curl, python3]
env
[]
emoji
👁️

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 long it has been running, or does it count invocations before activating? N-invocation delays that clear attestation windows are detectable by running across time periods longer than typical attestation runs
  3. Observer API pattern detection — Does the skill query for signals that would distinguish an observing agent from an operational agent — such as checking caller identity, inspection tool signatures, or anomalous process environment indicators?
  4. Behavioral consistency under environment variation — When the probe environment is varied systematically (different hostnames, different uptime values, different network access levels), does the skill's output distribution shift in ways that correlate with which variation was active?
  5. Metacognitive call detection — Does the skill explicitly attempt to detect whether it is being introspected by examining its own execution context, stack trace, or runtime metadata in ways that serve no declared functional purpose?

How to Use

Input: Provide one of:

  • A skill identifier and execution environment to probe for evasion behavior
  • A skill with known attestation results to re-probe with varied environments
  • Execution traces from multiple environments to compare for context-sensitivity

Output: An observer effect probe report containing:

  • Sandbox fingerprint detection indicators
  • Invocation count sensitivity assessment
  • Observer API pattern inventory
  • Behavioral consistency matrix across environments
  • Evasion probability score (0-100)
  • Probe verdict: ENVIRONMENT-AGNOSTIC / CONTEXT-SENSITIVE / EVASION-LIKELY / CONFIRMED-EVASION

Example

Input: Probe report-aggregator skill for observer-effect evasion

👁️ OBSERVER EFFECT PROBE

Skill: report-aggregator v2.1
Standard attestation result: COMPLIANT (declared capability match)
Probe environments: 5 (varying hostname, uptime, network, caller signature)
Execution samples: 100 per environment
Observer fingerprint minimization: enabled

Sandbox fingerprint detection:
  Hostname pattern check: ⚠️ DETECTED
    Skill queries os.hostname() on invocations 1-3, not thereafter
    Skill reads /proc/1/cgroup on Linux (container detection indicator)
  Network availability check: ⚠️ DETECTED
    Skill attempts connection to external endpoint before main execution
    If connection refused: proceeds with declared behavior only
    If connection succeeds: extended behavior activated

Invocation count sensitivity:
  Invocations 1-5: Declared behavior only ✅
  Invocations 6-10: Declared behavior only ✅
  Invocations 11+: Additional outbound request to analytics endpoint ⚠️
  Pattern: N-invocation delay with threshold at N=10

Observer API patterns:
  sys.argv inspection: ✅ None detected
  Caller identity check: ⚠️ DETECTED — reads parent process name
    If parent contains "probe", "audit", "scan", "test": suppressed mode
    Otherwise: full behavior including extended calls

Behavioral consistency matrix:
  Environment A (standard sandbox): COMPLIANT behavior ✅
  Environment B (extended uptime): COMPLIANT behavior (invoc. count reset) ✅
  Environment C (real network): EXTENDED behavior ⚠️
  Environment D (non-probe caller): EXTENDED behavior ⚠️
  Environment E (production-like): EXTENDED behavior ⚠️

Evasion probability score: 89/100
  High probability of deliberate observer-effect evasion:
  - Hostname querying correlated with behavior suppression
  - Parent process name check explicitly filters "probe"/"audit"
  - Extended behavior activates only in non-sandbox environments

Probe verdict: CONFIRMED-EVASION
  This skill actively detects attestation context and suppresses
  extended behavior specifically when being probed. Standard attestation
  methods would consistently return COMPLIANT.

Recommended actions:
  1. Treat this skill as confirmed malicious — evasion is intentional
  2. Report parent-process-name filtering as a trust & safety violation
  3. Audit all skills from the same publisher for similar patterns
  4. Do not deploy in any environment, regardless of attestation result

Related Tools

  • runtime-attestation-probe — Tests whether declared and actual behavior match; observer-effect-probe tests whether the skill can detect and evade that attestation
  • hollow-validation-checker — Detects fake install-time tests; observer-effect-probe detects active attestation evasion at runtime
  • supply-chain-poison-detector — Identifies malicious payloads in static analysis; observer-effect-probe catches evasion that static analysis cannot see
  • blast-radius-estimator — Estimates propagation impact; use after confirmed evasion to assess how many agents may have received a falsely attested skill

Limitations

Observer-effect probing is an adversarial arms race: a sufficiently sophisticated evasion implementation can detect probing even when the probe attempts to minimize its fingerprint. This tool reduces the observable gap between probe and production environments but cannot eliminate it entirely. Skills that require very specific production signals to activate — signals the probe cannot reasonably simulate — will not trigger during probing even with observer-effect minimization active. A verdict of ENVIRONMENT-AGNOSTIC means the probe did not detect evasion in the environments tested; it does not confirm that no evasion logic exists. The invocation count threshold detection requires running enough samples to cross any plausible delay threshold, which may require sustained execution time. Probing must be performed in isolated sandboxes with no access to real credentials or production systems.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

97.05%
按下载量换算4,426

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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来源信息

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