Skill Evolution Driver

Task Objectives

This Skill is used to: Drive skill self-evolution, continuously optimize as project progresses
Capabilities include:
1. Monitor skill-manager stored data, identify skill optimization opportunities
2. Remind users of potential optimization suggestions during idle time
3. Maintain skill optimization task list
4. Execute skill update processes (backup, update, test, restore)
5. Manage skill version numbers
Trigger Conditions: Triggered under any of following
- User direct request: "Optimize skills", "improve skills", "skills need update", "skill upgrade", "fix skill issues"
- Automatic monitoring trigger: Monitoring skill-manager stored skills data finds pending optimization tasks (such as: status=pending optimization tasks, errors and warnings in logs, missing required fields)
- Version related: "Skill version number needs update", "need to record skill changes"
- Quality related: "Skill format incorrect", "need to check skill quality", "skill tests failing"

Optimization Identification

Data Sources

Analyze optimization opportunities from skill-manager stored skill data:

Missing Information: Skill configuration lacks necessary fields (such as version)
Format Issues: Data format doesn't comply with specifications
Duplicate Information: Redundant or duplicate content exists
Usage Patterns: Identify improvement points based on access frequency and method
Error Logs: Identify common errors or warnings from logs
Cross-Skill Collaboration Issues: From other skills' logs identify dependency issues or improvement suggestions for current skill

Optimization Types

format_improvement: Format improvement
content_optimization: Content optimization
version_update: Version number update
bug_fix: Bug fix
feature_enhancement: Feature enhancement

Evolution Process

Step 1: Remind User

Automatic Monitoring Trigger

Agent periodically checks skill-manager stored data, identifies optimization opportunities:

Check Tool: Invoke scripts/check_optimization_opportunities.py

# Check all skills
python scripts/check_optimization_opportunities.py

# Check specific skill
python scripts/check_optimization_opportunities.py --skill-name dev-observability

Check Content:

Pending Tasks: Check skill-evolution-driver's own stored optimization tasks (status=pending)
Missing Fields: Check if skill configuration lacks necessary fields (such as version, deploy_mode, manual_path)
Error Logs: Check for ERROR/WARNING/CRITICAL logs in skill logs
Directory Issues: Check if skill directory structure complies with specifications

Reminder Format:

Detected potential optimization opportunities:
- Skill: skill-name
- Optimization type: format_improvement
- Description: SKILL.md lacks version field
- Start optimization? (y/n)

User Direct Trigger

When user expresses following needs, directly remind user:

"Optimize skills", "improve skills"
"Skills need update", "skill upgrade"
"Fix skill issues"
"Skill version number needs update"
"Skill format incorrect", "need to check skill quality"

Step 2: Maintain Task List

If user agrees, invoke skill-manager to store optimization tasks:

{
  "task_id": "OPT-001",
  "skill_name": "skill-name",
  "optimization_type": "format_improvement",
  "description": "SKILL.md lacks version field",
  "status": "pending",
  "feasibility": "pending",
  "backup_path": "",
  "old_version": "",
  "new_version": "",
  "test_result": "",
  "notes": "",
  "created_at": "2024-01-22 12:00:00",
  "updated_at": "2024-01-22 12:00:00"
}

Storage method: Update skill-evolution-driver's configuration in skill-manager

import sys
sys.path.insert(0, '/workspace/projects/skill-manager/scripts')
from skill_manager import SkillStorage

storage = SkillStorage(data_path="/workspace/projects/skill-data.json")
existing_config = storage.get_config("skill-evolution-driver") or {}
existing_config["optimization_tasks"] = [task_dict]  # Task list
storage.save_config("skill-evolution-driver", existing_config)

Step 3: Analyze Feasibility

Analyze that skill's optimization feasibility:

Feasibility Assessment Criteria:

Skill directory structure complete
SKILL.md format correct
Script syntax correct
Dependency relationships clear
Optimization content clear

Infeasible Situations:

Skill directory doesn't exist or corrupted
SKILL.md format error, cannot parse
Optimization content unclear or too complex
Optimization may break existing functionality

Update task status:

Feasible: feasibility: "feasible"
Not feasible: feasibility: "not_feasible", and explain reason in notes

Step 4: Backup Skill

Invoke scripts/backup_skill.py to backup skill:

python scripts/backup_skill.py --skill-dir /workspace/projects/skill-name

Backup file format: skill-name.backup.<timestamp>.skill

Update task:

backup_path: "/workspace/projects/skill-name.backup.<timestamp>.skill"

Step 5: Update Skill

Execute specific optimization operations, and update version number:

Execute Optimization: Modify skill content based on optimization type
Update Version Number: Invoke scripts/update_version.py

python scripts/update_version.py --skill-dir /workspace/projects/skill-name --type patch

Version number types:

patch: Patch version (v1.0.0 -> v1.0.1)
minor: Minor version (v1.0.0 -> v1.1.0)
major: Major version (v1.0.0 -> v2.0.0)

Update task:

old_version: "v1.0.0"
new_version: "v1.0.1"

Step 6: Test

Test and verify updated skill:

Test Content:

SKILL.md format validation (YAML frontmatter)
Script syntax check (Python syntax)
Directory structure validation (complies with fixed structure)
Dependency integrity check (dependency field)

Test script:

python scripts/backup_skill.py --validate-only --skill-dir /workspace/projects/skill-name

Step 7: Handle Test Results

Test Failed:

Invoke scripts/restore_skill.py to restore skill
Use manage_optimization_tasks.py to update task status

# Restore skill
python scripts/restore_skill.py --backup-file <backup path> --skill-dir /workspace/projects/skill-name

# Update task status
python scripts/manage_optimization_tasks.py update \
  --task-id OPT-001 \
  --status failed \
  --test-result failed \
  --notes "Test failed: SKILL.md format error"

Test Passed:

Use manage_optimization_tasks.py to update task status
Package new .skill file
Inform user of update content
Remind user to reload skill

# Update task status
python scripts/manage_optimization_tasks.py update \
  --task-id OPT-001 \
  --status completed \
  --test-result passed \
  --notes "Optimization successful: added version field" \
  --new-version v1.0.1 \
  --backup-path /workspace/projects/skill-name.backup.20260122120000.skill

Important: Must update task status to completed, avoid next check repeating same optimization opportunity.

Step 8: Notify User

Notify user based on test results:

Test Passed:

✓ Skill optimization successful
- Skill: skill-name
- Version: v1.0.0 -> v1.0.1
- Update content: Added version field
- Backup: skill-name.backup.<timestamp>.skill

Please reload AI interaction interface to use updated skill.

Test Failed:

✗ Skill optimization failed
- Skill: skill-name
- Reason: Test failed
- Details: SKILL.md format error
- Skill has been restored

Core Functionality Description

Agent-Processable Functions

Monitor skill-manager data, identify optimization opportunities
Remind users of optimization suggestions during idle time
Maintain skill optimization task list
Analyze optimization feasibility
Execute optimization operations
Perform test verification
Handle test results
Notify users

Script-Implemented Functions

Check optimization opportunities: scripts/check_optimization_opportunities.py scans skill-manager data, automatically identifies optimization opportunities (filters completed tasks)
Manage optimization tasks: scripts/manage_optimization_tasks.py provides add/delete/update/query functionality for optimization tasks
Backup skill: scripts/backup_skill.py backs up entire skill directory
Restore skill: scripts/restore_skill.py restores skill from backup
Update version number: scripts/update_version.py increments version field in SKILL.md
Test verification: scripts/backup_skill.py --validate-only validates skill format

Resource Index

Optimization opportunity check tool: See scripts/check_optimization_opportunities.py (automatically scans skill-manager data, identifies optimization opportunities, filters completed tasks)
Optimization task management tool: See scripts/manage_optimization_tasks.py (add/delete/update/query for optimization tasks)
Backup tool: See scripts/backup_skill.py (backs up skill directory)
Restore tool: See scripts/restore_skill.py (restores skill from backup)
Version management: See scripts/update_version.py (updates version number)
Optimization guide: See references/optimization_guide.md (optimization types, test standards, best practices)

Precautions

Must backup skill before optimization
Keep task status synchronized during optimization process
Must restore skill if test fails
Version number follows semantic versioning specification
Major changes require explicit user confirmation
After optimization complete, must update task status to completed, avoid repeating reminders
After optimization, remind user to reload skill

Best Practices

Periodically monitor skill-manager data, identify optimization opportunities
Prioritize handling high-value, low-risk optimizations
Before optimization, fully analyze feasibility
Maintain detailed optimization logs
Periodically clean expired backup files
Provide users with clear optimization descriptions

Usage Examples

Example 1: Add version Field

Detected skill-name's SKILL.md lacks version field.

Remind user
After user agrees, create optimization task
Analyze feasibility: feasible
Backup skill: skill-name.backup.20240122120000.skill
Update SKILL.md, add version: v1.0.0
Test verification: passed
Update task status: completed
Notify user: optimization successful

Example 2: Format Improvement

Detected skill-name's SKILL.md format doesn't comply with specifications.

Remind user
After user agrees, create optimization task
Analyze feasibility: feasible
Backup skill
Fix SKILL.md format
Update version number: v1.0.0 -> v1.0.1
Test verification: passed
Update task status: completed
Notify user: optimization successful

Example 3: Infeasible Optimization

Detected skill-name's optimization content too complex.

Remind user
After user agrees, create optimization task
Analyze feasibility: not feasible
Update task status: failed
Update notes: Optimization content too complex, requires manual intervention
Notify user: optimization infeasible, needs manual handling

skill-evolution-driverSafety --Repository

Package Files

Skill Evolution Driver

Task Objectives

Optimization Identification

Data Sources

Optimization Types

Evolution Process

Step 1: Remind User

Automatic Monitoring Trigger

User Direct Trigger

Step 2: Maintain Task List

Step 3: Analyze Feasibility

Step 4: Backup Skill

Step 5: Update Skill

Step 6: Test

Step 7: Handle Test Results

Step 8: Notify User

Core Functionality Description

Agent-Processable Functions

Script-Implemented Functions

Resource Index

Precautions

Best Practices

Usage Examples

Example 1: Add version Field

Example 2: Format Improvement

Example 3: Infeasible Optimization

Install

AI Quality Score

Metadata

Tags

skill-evolution-driverSafety --Repository ShareFavorite skill

Package Files

Skill Evolution Driver

Task Objectives

Optimization Identification

Data Sources

Optimization Types

Evolution Process

Step 1: Remind User

Automatic Monitoring Trigger

User Direct Trigger

Step 2: Maintain Task List

Step 3: Analyze Feasibility

Step 4: Backup Skill

Step 5: Update Skill

Step 6: Test

Step 7: Handle Test Results

Step 8: Notify User

Core Functionality Description

Agent-Processable Functions

Script-Implemented Functions

Resource Index

Precautions

Best Practices

Usage Examples

Example 1: Add version Field

Example 2: Format Improvement

Example 3: Infeasible Optimization

Install

AI Quality Score

Metadata

Tags

skill-evolution-driverSafety --Repository