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 skills add ufy2024/AuC --skill canary-watchgit 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/canary-watch)<a href="https://agentmods.dev/skills/ufy2024/auc/canary-watch"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/canary-watch/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/ufy2024/auc/canary-watch"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/canary-watch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 22 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00054 | $0.00924 |
| Opus 5 | $0.00027 | $0.00462 |
| Sonnet 5 | $0.00011 | $0.00185 |
| Haiku 4.5 | $0.00005 | $0.00092 |
Grade A, and why
canary-watch 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 7d 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
7 near-identical copies found in the catalogue:
- canary-watch — 89% identical, 29 lines differ
- canary-watch — 89% identical, 29 lines differ
- canary-watch — 89% identical, 29 lines differ
- canary-watch — 89% identical, 29 lines differ
- canary-watch — 86% identical, 28 lines differ
- canary-watch — 84% identical, 36 lines differ
- canary-watch — 84% identical, 36 lines differ
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Canary Watch — Post-Deploy Monitoring
When to Use
- After deploying to production or staging
- After merging a risky PR
- When you want to verify a fix actually fixed it
- Continuous monitoring during a launch window
- After dependency upgrades
How It Works
Monitors a deployed URL for regressions. Runs in a loop until stopped or until the watch window expires.
What It Watches
1. HTTP Status — is the page returning 200?
2. Console Errors — new errors that weren't there before?
3. Network Failures — failed API calls, 5xx responses?
4. Performance — LCP/CLS/INP regression vs baseline?
5. Content — did key elements disappear? (h1, nav, footer, CTA)
6. API Health — are critical endpoints responding within SLA?
7. Static Assets — are JS, CSS, image, and font requests returning 2xx/3xx with expected content types?
8. SSE Streams — do event-stream endpoints connect and receive an initial event or heartbeat?
Watch Modes
Quick check (default): single pass, report results
/canary-watch https://myapp.com
Sustained watch: check every N minutes for M hours
/canary-watch https://myapp.com --interval 5m --duration 2h
Diff mode: compare staging vs production
/canary-watch --compare https://staging.myapp.com https://myapp.com
Alert Thresholds
critical: # immediate alert
- HTTP status != 200
- Console error count > 5 (new errors only)
- LCP > 4s
- API endpoint returns 5xx
- Static asset returns 4xx/5xx
- SSE endpoint cannot connect or drops before first heartbeat
warning: # flag in report
- LCP increased > 500ms from baseline
- CLS > 0.1
- New console warnings
- Response time > 2x baseline
- Static asset content type changed unexpectedly
- SSE heartbeat latency > 2x baseline
info: # log only
- Minor performance variance
- New network requests (third-party scripts added?)
Notifications
When a critical threshold is crossed:
- Desktop notification (macOS/Linux)
- Optional: Slack/Discord webhook
- Log to
~/.claude/canary-watch.log
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.
- 7d ago First seen · 130 lines · 54 tokens per session scan A 5a813c2d9721
canary-watch is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 54 tokens to every session and 924 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 2026-09-03.
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