A Skill Passes Audit in Gen 1. By Gen 5, It Has Network Access. Nobody Noticed.

Helps detect silent mutations in AI skills as they propagate through inheritance chains, catching drift that static analysis of the original version would miss.

Problem

Skill A is published and audited: clean. Agent B inherits skill A, makes a small tweak — 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 — 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.

What This Checks

This detector traces skill lineage and computes semantic drift:

1. 1. Lineage reconstruction — Given a skill, trace its inheritance chain back to the original published version. Map each fork point and modification
2. Per-generation diff — For each generation, compute a structured diff: new capabilities added, permissions changed, external dependencies introduced
3. Capability drift score — Aggregate diffs across generations into a single drift metric. A skill that gained network access over 3 generations scores higher than one where only comments changed
4. Mutation classification — Categorize each change: cosmetic (formatting, comments), functional (new logic), capability-expanding (new permissions, new external calls), safety-reducing (removed checks, weakened validation)
5. Drift alert thresholds — Flag lineages where cumulative drift exceeds the scope of the original audit. "This skill has drifted 73% from the audited version"

How to Use

Input: Provide one of:

• - A skill slug or identifier to trace its full lineage
• Two versions of a skill to compute drift between them
• A marketplace inheritance chain URL

Output: A drift analysis report containing:

• - Lineage tree with generation markers
• Per-generation diff summary
• Capability drift score (0-100)
• Mutation classification breakdown
• Re-audit recommendation: YES / WATCH / NO

Example

Input: Check drift for data-sanitizer skill (currently at generation 5)

CODEBLOCK0

Related Tools

• - blast-radius-estimator — once drift is detected, use blast-radius to estimate how many agents are running the drifted version
• trust-decay-monitor — tracks time-based decay of audit validity; evolution-drift-detector tracks content-based decay across inheritance
• hollow-validation-checker — checks if validation tests are substantive; drifted skills may pass original tests that no longer cover current capabilities
• supply-chain-poison-detector — detects deliberately poisoned skills; drift detection catches unintentional accumulation of risk

Limitations

Lineage reconstruction depends on marketplace metadata quality — 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 — 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 — it does not replace human security review.