evolution-log — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited evolution-log (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
/evolutionWritten to evolution/topic-N-evolution.md:
# System Evolution Log: Topic N → Topic N+1
**Date:** YYYY-MM-DD
**Sessions covered:** [session range]
**Time invested:** [hours]
## Code Complexity Comparison
### Topic N-1 Code
- Lines of code: [count]
- Number of classes/functions: [count]
- Test count: [count]
- Design patterns used: [list]
- Abstraction depth: [max nesting / composition depth]
### Topic N Code
- Lines of code: [count]
- Number of classes/functions: [count]
- Test count: [count]
- Design patterns used: [list]
- Abstraction depth: [max nesting / composition depth]
### Delta
- Complexity growth: [quantified]
- New patterns introduced: [list with first-use date]
- Patterns that became automatic: [list -- things the student does without thinking]
## Meta-Patterns Mastered
Identify 3-5 patterns the student has internalized across topics:
1. **[Pattern Name]** (e.g., "Dependency Inversion in AI Pipelines")
- First encountered: [topic/session]
- Became automatic: [topic/session]
- Evidence: [specific code example where student applied it without prompting]
2. **[Pattern Name]**
...
## Agent Stress-Test Log
### Intentional Mistakes Used
Document any deliberate mistakes introduced to test the agent's coaching:
- What was the mistake?
- Did the agent catch it?
- How did the agent's recovery logic work?
- What does this reveal about the agent's reliability?
### Agent Coaching Quality
- Concepts where the agent excelled: [list]
- Concepts where the agent struggled: [list]
- Teaching approach changes made during this topic: [list]
- Model version used: [Claude model ID]
## Trajectory Evaluation
### Assessment Scores: Start vs End of Topic
| Dimension | Start | End | Delta | Evidence |
|-----------|-------|-----|-------|----------|
| Python Engineering | | | | |
| Problem Decomposition | | | | |
| Mathematical Thinking | | | | |
| ML/DL Understanding | | | | |
| System Design | | | | |
| Human-AI Collaboration | | | | |
| Engineering Craft | | | | |
| Evaluation Thinking | | | | |
| Communication | | | | |
### Velocity
- Exercises per session (average): [number]
- Time per exercise (average): [minutes]
- Trend: [accelerating / steady / decelerating]
### The "Click Moments"
List the 2-3 moments during this topic where understanding visibly shifted:
1. [What clicked, when, what triggered it]
### Open Gaps Carried Forward
- [Concept] -- deferred to Topic N+1 because [reason]At 18 months, the evolution/ directory contains a longitudinal record of growth that no interview can replicate. A recruiter reads topic-4-evolution.md and sees:
This is the "Telemetry of Mastery" -- the black box recorder of an engineer's development.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.