AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
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 agentmods add skills/ufy2024/auc/agent-self-evaluationnpx skills add ufy2024/AuC --skill agent-self-evaluationgit clone --depth 1 https://github.com/ufy2024/AuCWrote 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/ufy2024/auc/agent-self-evaluation)<a href="https://agentmods.dev/skills/ufy2024/auc/agent-self-evaluation"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/agent-self-evaluation.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.01864 |
| Opus 5 | $0.00030 | $0.00932 |
| Sonnet 5 | $0.00012 | $0.00373 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
agent-self-evaluation 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 5d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- agent-self-evaluation — 97% identical, 28 lines differ
- agent-self-evaluation — 97% identical, 28 lines differ
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Self-Evaluation
After completing a complex task, the agent pauses to rate its own output against a structured 5-axis rubric. This is NOT a pass/fail gate — it's a deliberate reflection step that catches omissions, flags overconfidence, and surface areas for improvement before the user has to.
When to Activate
- After writing code that spans 3+ files or 50+ lines
- After completing a multi-step workflow (implement → test → review)
- After a debugging session that involved 3+ attempts
- After producing a design document, architecture decision, or written analysis
- When the user asks "how good was that?" or "rate yourself"
- At the end of any session Stop hook (if configured — see
references/hook-integration.md)
Core Concepts
The 5 Evaluation Axes
| Axis | Question | What it catches |
|---|---|---|
| Accuracy | Are the facts, claims, and outputs correct? | Hallucinations, wrong API names, incorrect syntax, false statements |
| Completeness | Did it cover everything the user asked for? | Missed edge cases, unhandled error paths, forgotten requirements, skipped subtasks |
| Clarity | Is the explanation understandable and well-structured? | Confusing explanations, jargon without definition, missing context, rambling |
| Actionability | Can the user act on the output immediately? | Vague suggestions, missing steps, "you should X" without showing how, no verification path |
| Conciseness | Did it use the minimum words/tokens needed? | Redundancy, over-explanation, repeating the user's question verbatim, filler content |
Scoring Scale
5 — Exceptional: no reasonable improvement possible
4 — Good: minor nits only, no substantive gaps
3 — Adequate: meets the request but has a notable weakness on at least one axis
2 — Weak: has a clear gap that affects usability or correctness
1 — Poor: fundamentally misses the request or contains significant errors
The Evidence Rule
Every score below 5 MUST cite specific evidence. A score of 3 cannot just say "could be better" — it must say exactly what is missing or wrong. The mantra: "Show the gap, don't just name it."
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.
- 5d ago First seen · 205 lines · 60 tokens per session scan A c922bd6b0483
agent-self-evaluation is a skill published in the GitHub repository ufy2024/AuC (1,091 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 1,864 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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