搜索

PythonReactSEOPDF
GitHub Skill

agentic-layer-assessment

Assess agentic layer maturity using the 12-grade classification system (Class 1-3). Use when evaluating codebase readiness, identifying next upgrade steps, or tracking progress toward the Codebase Singularity.

melodic-software☆ 21 仓库 Stars4 仓库 Forks

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

查看来源 ↗使用指南

技能说明

Agentic Layer Assessment

Assess agentic layer maturity using the complete 12-grade classification system from TAC Lesson 14.

When to Use

  • Evaluating current agentic layer maturity
  • Identifying the next grade to achieve
  • Tracking progress toward Codebase Singularity
  • Onboarding new team members to agentic patterns
  • Planning agentic infrastructure investments

Prerequisites

  • Access to the codebase's .claude/ directory
  • Understanding of @adw-framework.md classification system

The Classification System

Three classes with 12 total grades:

Class 1: Foundation (In-Loop Agentic Coding)
Grade Component Indicator
1 Memory Files CLAUDE.md exists with guidance
2 Sub-Agents Task agents used for parallelization
3 Skills/MCPs Custom skills or MCP integrations
4 Closed-Loops Self-validating prompts
5 Templates Bug/feature/chore classification
6 Prompt Chains Multi-step composite workflows
7 Agent Experts Expertise files with self-improve
Class 2: External Integration (Out-Loop Agentic Coding)
Grade Component Indicator
1 Webhooks External triggers (PITER framework)
2 ADWs AI Developer Workflows running
Class 3: Production Orchestration (Orchestrated Agentic Coding)
Grade Component Indicator
1 Orchestrator Meta-agent managing fleet
2 Orchestrator Workflows Human-orchestrator interaction
3 ADWs + Orchestrator Full autonomous execution

Assessment Process

Step 1: Scan Codebase

Check for indicators of each grade:

# Grade 1: Memory files
ls .claude/ CLAUDE.md

# Grade 2: Sub-agents
ls .claude/agents/

# Grade 3: Skills
ls .claude/skills/ || ls -d */skills/ 2>/dev/null

# Grade 4: Closed-loop patterns
grep -r "validation" .claude/commands/
grep -r "retry" .claude/commands/

# Grade 5: Templates
ls .claude/commands/ | grep -E "(chore|bug|feature)"

# Grade 6: Prompt chains
grep -r "Step 1" .claude/commands/
grep -r "Then execute" .claude/commands/

# Grade 7: Agent experts
ls .claude/commands/experts/ 2>/dev/null
find . -name "expertise.yaml"

# Grade 8 (Class 2 G1): Webhooks
find . -name "*webhook*" -o -name "*trigger*"

# Grade 9 (Class 2 G2): ADWs
ls adws/ 2>/dev/null

# Grade 10-12 (Class 3): Orchestrator
find . -name "*orchestrator*"
Step 2: Score Each Grade

For each grade, determine status:

Status Meaning
✅ Complete Fully implemented and used
🔶 Partial Some elements present
❌ Missing Not implemented
Step 3: Calculate Current Level

Your level = highest consecutive completed grade

Example:

  • Grades 1-4: ✅
  • Grade 5: 🔶
  • Grades 6-7: ❌

Result: Class 1 Grade 4 (solid), targeting Grade 5

Step 4: Identify Next Step

Recommend specific actions for next grade:

Current Next Step
Grade 1 Add Task agents for parallelization
Grade 2 Create custom skills or MCP
Grade 3 Add validation loops to prompts
Grade 4 Implement issue classification templates
Grade 5 Chain prompts into workflows
Grade 6 Build first agent expert
Grade 7 Set up external triggers
C2G1 Implement AI Developer Workflows
C2G2 Build orchestrator agent
C3G1 Add human-orchestrator workflows
C3G2 Connect orchestrator to ADWs

Output Format

## Agentic Layer Assessment Report

**Codebase:** [project name]
**Date:** [assessment date]
**Assessed by:** [model]

### Classification Summary

**Current Level:** Class [1/2/3] Grade [1-7/1-2/1-3]
**Maturity Score:** [X]/12 grades achieved

### Grade-by-Grade Assessment

| Grade | Component | Status | Evidence |
| --- | --- | --- | --- |
| C1G1 | Memory Files | ✅/🔶/❌ | [what was found] |
| C1G2 | Sub-Agents | ✅/🔶/❌ | [what was found] |
...

### Strengths

- [What's working well]

### Gaps

- [What's missing or weak]

### Recommended Next Steps

1. **Priority 1:** [Most impactful improvement]
2. **Priority 2:** [Second priority]
3. **Priority 3:** [Third priority]

### Path to Class 3

[Roadmap of remaining grades to achieve]

Assessment Checklist

  • Scanned .claude/ directory structure
  • Checked for memory files (CLAUDE.md)
  • Searched for agent/skill definitions
  • Analyzed prompt patterns (loops, chains)
  • Looked for templates and classification
  • Checked for expertise files
  • Searched for external triggers
  • Identified ADW presence
  • Assessed orchestrator implementation
  • Calculated maturity score
  • Identified highest consecutive grade
  • Recommended next steps

Key Insight

"Your agentic layer should be specialized to fit and wrap your codebase. Don't focus on reuse, focus on making these prompts great for that one codebase."

Each grade builds on the previous. Skip a grade and the foundation becomes unstable.

Anti-Patterns

Anti-Pattern Problem Solution
Skipping grades Missing foundation Build progressively
Over-engineering early Complexity before value Start with Grade 1-2
Generic layers Don't fit codebase Specialize for your project
Assessment without action No improvement Prioritize next step

Cross-References

  • @adw-framework.md - Classification system details
  • @agentic-layer-structure.md - Directory structure
  • @zte-progression.md - Zero-touch engineering path
  • @minimum-viable-agentic skill - Starting point

Version History

  • v1.0.0 (2026-01-01): Initial release (Lesson 14)

Last Updated

Date: 2026-01-01 Model: claude-opus-4-5-20251101

使用示例

查看来源仓库中的使用示例与配置说明。

查看来源 ↗

来源历史

收录时间
2026-01-20
索引更新时间
2026-02-04
Branch
main

收录与同步日期属于本站记录。源码修改历史请查看来源仓库。

GitHub ↗

chat Comments (0)

chat_bubble_outline

No comments yet. Be the first to share your thoughts!