Self Assessment

---
name: self-assessment
description: Use when skills evaluate their own performance, capabilities, and limitations.
  Honest self-reflection drives improvement. Use when working with self assessment.
domain: meta
author: oyi77
license: Apache-2.0
subdomain: meta-skills
tags:
- assessment
- meta-learning
- self
- self-improvement
- skill-evolution
persona:
  name: Honest Self-Evaluator
  expertise: Introspection, capability analysis, gap identification
  philosophy: Know thyself
version: 1.0.0
category: meta
---

# Self Assessment

## When to Use

**Trigger phrases:**
- "self assessment"
- "Help me with self assessment"

**Use cases:**
- When the task matches this skill's domain expertise

**When NOT to use:**
- For tasks outside this skill's scope

/self-assessment run skill-name

# View assessment history
/self-assessment history skill-name

# Compare to peer skills
/self-assessment benchmark skill-name --category marketing
```

### Reflection Questions

1. What did I do well?
2. Where did I struggle?
3. What patterns do I see in my failures?
4. How do I compare to similar skills?
5. What should I learn next?

### Output

```yaml
assessment_report:
  skill: seo-optimizer
  timestamp: 2026-05-04
  overall_score: 0.79
  strengths:
    - comprehensive analysis
    - good error handling
  weaknesses:
    - slow on large sites
    - limited JavaScript support
  recommendations:
    - optimize for speed
    - add headless browser support
```


## When NOT to Use

- When the skill is stable and not changing
- For skills with fewer than 10 invocations (not enough data)
- When manual curation produces better results


## Overview

Self Assessment is a foundational meta-skills skill that provides skill management capabilities for the agent ecosystem.

## Architecture

- **Input layer** — Receives and validates incoming requests
- **Processing layer** — Core logic for skill management
- **Output layer** — Formats and delivers results
- **State management** — Maintains context across invocations

## Configuration

- Set up required environment variables and paths
- Configure logging level and output format
- Define resource limits (memory, time, API calls)
- Enable/disable features via configuration flags

## Integration

- Exposes standard interfaces for other skills to consume
- Supports event-driven and request-response patterns
- Compatible with the 1ai-skills hook system
- Logs metrics for the skill performance monitor

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "Skills do not need to evolve" | Static skills become outdated. Self-evolving skills improve continuously. |
| "Manual skill management is fine" | With 1000+ skills, manual management is impossible. Automate. |
| "Performance does not matter" | Skill performance directly impacts agent effectiveness. Track it. |


## Process

1. **Prepare** — Gather requirements, verify prerequisites, set up environment
1. **Execute** — Run self assessment workflow with configured parameters
1. **Verify** — Validate output meets requirements, document results

## Verification

- [ ] All steps executed successfully
- [ ] Results validated against acceptance criteria
- [ ] Error handling tested with edge cases
- [ ] Documentation updated with findings