agentleFS
aman-bhandariaman-bhandari/claude-code-agent-skills-framework/.claude/skills/evolution-log/SKILL.md

evolution-log

Skill0 starsChanged 7 months agoMITView on GitHub

Activate at the end of every Topic completion. Generates a System Evolution Log comparing code complexity, identifying meta-patterns mastered, documenting intentional agent stress-tests, and evaluating the student's growth trajectory. Invoke with /evolution.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

Files
SKILL.md

System Evolution Log (Telemetry of Mastery)

When This Fires

  • At the end of every Topic (mandatory)
  • At milestone completion
  • Manually via /evolution

The Evolution Log Format

Written 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]

Why This Matters

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:

  • Quantified complexity growth
  • Named meta-patterns (not just "I learned stuff")
  • Agent reliability data (the student EVALUATES the system that teaches them)
  • Honest trajectory with click moments and open gaps

This is the "Telemetry of Mastery" -- the black box recorder of an engineer's development.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.