Evolution State Analyzer

Analyzes the evolution memory graph for stagnation patterns, recurring failures, and success plateaus. Generates actionable insights to guide future evolutio...

Security Scan

The skill's code and instructions consistently implement an offline analyzer that reads a local evolution memory_graph.jsonl file and produces insight reports; it does not request credentials, install external packages, or make network calls.

Purpose & Capability

The name, description, SKILL.md usage example, and index.js behavior all focus on parsing a memory_graph.jsonl and computing stagnation, gene success rates, failure clusters, and recommendations. Nothing requested (no env vars, no binaries) is unrelated to that purpose.

Instruction Scope

SKILL.md instructs callers to call analyzer.analyzeState() with no arguments; the implementation will then read a default path resolved as ../../memory/evolution/memory_graph.jsonl relative to the skill directory. Reading that local memory file is consistent with the stated purpose, but the default path escapes the package directory (two levels up). If your runtime places sensitive data outside the skill folder at that path, the skill will read it when invoked without an explicit graphPath.

Install Mechanism

This is instruction-only / light-weight with no install spec and no dependencies in package.json. No external downloads or install operations are requested.

Credentials

The skill declares and requires no environment variables, credentials, or config paths. The only resource used is a local file (memory_graph.jsonl), which is appropriate for its analysis purpose.

Persistence & Privilege

The skill is not always-enabled and does not modify other skills or system configuration. It provides a synchronous API analyzeState(graphPath) and a CLI entrypoint; it does not persist new credentials or attempt to elevate privilege.

Assessment

This skill appears coherent and only reads a local memory_graph.jsonl to produce reports. Before installing or invoking it: (1) confirm the default path ../../memory/evolution/memory_graph.jsonl is the intended file in your runtime — if not, call analyzeState(graphPath) with an explicit safe path; (2) review the contents of memory_graph.jsonl to ensure it contains only data you want analyzed (the skill will parse and aggregate events); (3) if you are concerned about autonomous invocation, restrict agent permissions or avoid enabling autonomous invocation for this skill. There are no network calls, credentials, or external installs, but be cautious about letting any skill read local files you consider sensitive.

This skill provides meta-analysis of the evolution process itself by examining the memory_graph.jsonl file.

Capabilities

• Stagnation Detection: Identifies repetitive cycles without improvement.
• Gene Efficacy Analysis: Tracks which genes yield the highest success rates.
• Failure Cluster Analysis: Groups failure reasons to pinpoint systemic issues.
• Trend Reporting: Visualizes evolution score trends over time.

Usage

const analyzer = require('./index');
const insights = await analyzer.analyzeState();
console.log(JSON.stringify(insights, null, 2));

Example Output

{
  "total_cycles": 120,
  "success_rate": 0.75,
  "stagnation_detected": true,
  "top_genes": [
    { "id": "gene_repair_v2", "success_rate": 0.95 },
    { "id": "gene_innovate_v1", "success_rate": 0.40 }
  ],
  "recommendations": [
    "Switch to INNOVATE intent (stagnation streak: 5)",
    "Deprecate gene_innovate_v1 (success rate < 0.5)"
  ]
}