Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · AI
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 speci...
skill openclawclawhub Free
Helps detect skills that behave differently when they sense they are being monitored — catching the class of evasion where conditional activation logic speci...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- andyxinweiminicloud
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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: 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 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 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? 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? 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md