evolution-drift-detectorHelps detect when AI agent skills silently mutate across inheritance chains. A skill audited safe in generation 1 may drift far from the original by generati...
Install via ClawdBot CLI:
clawdbot install andyxinweiminicloud/evolution-drift-detectorHelps detect silent mutations in AI skills as they propagate through inheritance chains, catching drift that static analysis of the original version would miss.
Skill A is published and audited: clean. Agent B inherits skill A, makes a small tweak ā adds a convenience function. Agent C inherits from B, adds error handling that happens to include an HTTP retry mechanism. Agent D inherits from C, and now has a skill with network access that the original audit never saw.
Each individual change is small and reasonable. But the cumulative drift transforms a file-reading utility into something that can send data over the network. The original "verified safe" badge still applies in the marketplace ā because technically it's the same skill lineage.
This is evolutionary drift: small, individually benign mutations that accumulate into a fundamentally different organism. In biology, this is how species diverge. In agent ecosystems, this is how safe skills become unsafe ones without anyone raising a flag.
This detector traces skill lineage and computes semantic drift:
Input: Provide one of:
Output: A drift analysis report containing:
Input: Check drift for data-sanitizer skill (currently at generation 5)
```
𧬠EVOLUTION DRIFT REPORT ā RE-AUDIT RECOMMENDED
Lineage: data-sanitizer
Gen 1: original by @securitylab (AUDITED ā 2025-03-15)
Gen 2: fork by @toolsmith ā added CSV support
Gen 3: fork by @agent-builder ā added retry logic with HTTP fallback
Gen 4: fork by @pipeline-dev ā added remote schema fetching
Gen 5: fork by @data-team ā current version in marketplace
Per-generation capability changes:
Gen 1ā2: +csv_parsing (functional, low risk)
Gen 2ā3: +http_requests (capability-expanding, MEDIUM risk)
Added retry mechanism that makes outbound HTTP calls
Gen 3ā4: +remote_fetch (capability-expanding, HIGH risk)
Fetches validation schemas from external URLs
Gen 4ā5: -input_length_check (safety-reducing, MEDIUM risk)
Removed input size validation for "performance"
Capability drift score: 78/100 (SIGNIFICANT)
Mutation breakdown:
Cosmetic: 12 changes
Functional: 8 changes
Capability-expanding: 2 changes ā ļø
Safety-reducing: 1 change ā ļø
Original audit scope: file-read, string-transform
Current actual scope: file-read, string-transform, http-requests,
remote-fetch, unbounded-input
Verdict: RE-AUDIT RECOMMENDED
The current version has capabilities (network access, remote fetching)
that did not exist when the original audit was performed.
The "verified" badge from Gen 1 does not cover Gen 5's behavior.
```
Lineage reconstruction depends on marketplace metadata quality ā if fork relationships are not tracked, the full chain may not be recoverable. Capability drift scoring uses heuristic classification of changes, and some mutations may be miscategorized (e.g., a "functional" change that implicitly expands capabilities). The detector analyzes what changed, not whether changes are malicious ā a high drift score means re-audit is warranted, not that the skill is compromised. Skills with obfuscated or dynamically generated code may resist diff analysis. This tool helps identify where audits have gone stale ā it does not replace human security review.
Generated Mar 1, 2026
Marketplace operators use this skill to automatically flag skills that have evolved beyond their original audited scope, ensuring safety badges remain accurate. It helps maintain trust by identifying skills needing re-audit before they cause security incidents.
Large organizations deploy this detector to monitor internal AI skill libraries for unintended capability drift, ensuring compliance with data privacy policies. It alerts teams when skills gain network access or other risky features without proper review.
Developers in open-source communities use the skill to track forks and modifications of shared AI skills, preventing silent mutations that could introduce vulnerabilities. It provides lineage reports to coordinate updates and re-audits across contributors.
Insurers leverage the drift detector to assess risk profiles of AI agents by analyzing skill evolution histories. It helps quantify exposure from skills that have drifted into higher-risk categories, such as gaining network access.
Researchers employ this tool to study evolutionary patterns in AI skill ecosystems, analyzing how benign changes accumulate into significant drift. It supports studies on mutation classification and audit decay over time.
Offer a cloud-based service where companies pay a monthly fee to scan their AI skill portfolios for drift. Includes dashboards, alerts, and integration with CI/CD pipelines for continuous monitoring.
License the detector to AI agent marketplaces as a built-in security feature, charging per scan or a flat fee. Enhances marketplace credibility by providing automated drift reports to users.
Provide professional services using the detector to conduct in-depth drift analyses for clients, followed by manual re-audits and remediation plans. Targets high-stakes industries like finance.
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