# Self Assessment

> Use when skills evaluate their own performance, capabilities, and limitations. Honest self-reflection drives improvement. Use when working with self assessment.

- Skill: `oyi77/self-assessment` (Agent Skill)
- Install (CLI): `npx skillmds add oyi77/self-assessment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/oyi77/self-assessment/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: Apache-2.0
- Author: oyi77 (https://skillmd.com/u/oyi77)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/oyi77/self-assessment

---


# 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