Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mickeyyaya/refactoring-skills --skill agent-self-evaluation-patternsgit clone --depth 1 https://github.com/mickeyyaya/refactoring-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mickeyyaya/refactoring-skills/agent-self-evaluation-patterns)<a href="https://agentmods.dev/skills/mickeyyaya/refactoring-skills/agent-self-evaluation-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/agent-self-evaluation-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mickeyyaya/refactoring-skills/agent-self-evaluation-patterns"><img src="https://agentmods.dev/badge/skills/mickeyyaya/refactoring-skills/agent-self-evaluation-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.04763 |
| Opus 5.5 | $0.00027 | $0.01905 |
| Sonnet 5.5 | $0.00014 | $0.00953 |
| Haiku 4.5 | $0.00007 | $0.00476 |
Grade A, and why
agent-self-evaluation-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 427 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Self-Evaluation Patterns
Overview
AI agents that cannot evaluate their own output quality are unreliable in production. A model that confidently produces wrong answers, fabricates citations, or never flags uncertainty becomes a liability rather than an asset. Self-evaluation patterns give agents structured mechanisms to detect errors, express calibrated uncertainty, and improve output quality before results reach users.
When to use: Designing agents that produce factual claims, code, analysis, or structured data; reviewing agent pipelines for hallucination risk; building eval suites for AI-generated content; any system where incorrect LLM output has meaningful downstream consequences.
Quick Reference
| Pattern | Core Problem | Key Technique | Failure Mode |
|---|---|---|---|
| Confidence Scoring | Agent returns wrong answers with false certainty | Logprob analysis, self-consistency sampling | Overconfident scoring — high score on hallucinated output |
| Chain-of-Thought Reflection | Errors baked into first draft go unchallenged | Generate → critique → revise cycle | Rubber-stamp reflection — critique that validates the original uncritically |
| LLM-as-Judge | Model cannot objectively evaluate its own output | Separate judge call with scoring rubric | Same model judging itself — no independence, shared biases |
| Hallucination Self-Detection | Claims unverifiable against source material | Source grounding checks, API existence verification | Reflection loop amplifies fabricated details instead of catching them |
| Output Quality Verification | Schema or structural errors in generated output | Assertion-based checking, schema validation | Checking format only — passes schema but semantically wrong |
| Eval-Driven Development | No objective measure of agent improvement | Define graders before implementation, regression gates | Graders written after the fact, shaped to pass existing output |
Confidence Scoring
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 427 lines · 68 tokens per session scan A 531a19c579a9
agent-self-evaluation-patterns is a skill published in the GitHub repository mickeyyaya/refactoring-skills (6 stars, last pushed 7mo ago), licensed MIT. It adds 68 tokens to every session and 4,763 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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