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/pdudotdev/ainoc/oncallnpx skills add pdudotdev/aiNOC --skill oncallgit clone --depth 1 https://github.com/pdudotdev/aiNOCWhat 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 | $0.00030 | $0.03190 |
| Opus 5 | $0.00015 | $0.01595 |
| Sonnet 5 | $0.00006 | $0.00638 |
| Haiku 4.5 | $0.00003 | $0.00319 |
Grade A, and why
On-Call SLA Troubleshooting 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 2d 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.
The source is not reproduced here
Licensed GPL-3.0
The repository is licensed GPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
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.
- 2d ago First seen · 211 lines · 30 tokens per session scan A 08800497ce33
On-Call SLA Troubleshooting is a skill published in the GitHub repository pdudotdev/aiNOC (11 stars, last pushed 2d ago), licensed GPL-3.0. It adds 30 tokens to every session and 3,190 once invoked, about $0.0002 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.
Other skills, from other repositories
localization-toolkit
This skill should be used when setting up, auditing, or enforcing internationalization/localization in UI codebases (React/TS, i18next or similar, JSON locales), including installing/configuring the i18n framework, replacing hard-coded strings, ensuring en-US/zh-CN coverage, mapping error codes to localized messages…
capture-usage4claude-screenshots
Automate Usage4Claude interface screenshots with CleanShot X on macOS. Use when Codex needs to capture localized Usage4Claude menu-bar popover screenshots, switch Usage4Claude display/language settings, save files such as [email protected], or troubleshoot CleanShot/window-capture automation that depends on…
review-design
Internal UI/UX design review pass of the agentic-workflow review pack — composed in-turn by review-change and product-audit; not a menu entry. Checks the changed UI against the project's design doc: consistency, states, responsiveness, and reuse — applies only when the project has a UI and the change touches it.…
cross-cutting-checklist-generator
Detect when a concern is scattered across many places in a project (config keys, enums, translations, feature flags, API endpoints, doc references) and auto-generate a project-local checklist so that same concern is never partially updated again. Use whenever you add or change something that also exists in several…
borderline
Delegate mechanical, boring, low-risk tasks to the Antigravity CLI (agy) where it is just as reliable as Claude: bulk translations and i18n, trivial CSS/color/style changes, repetitive renames and replacements across many files, boilerplate, formatting and simple copy rewrites. Use it when the task is monotonous, its…
chain-llm-pattern
Build multi-step LLM reasoning chains in n8n using Groq, OpenAI, or Claude for structured data extraction, categorization, scoring, and analysis. Use this skill whenever the user wants to chain multiple LLM calls together in an n8n workflow — phrases like "extract entities then categorize", "multi-step LLM prompt"…