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/dinglebear-ai/cortex/frustration-assessmentnpx skills add dinglebear-ai/cortex --skill frustration-assessmentgit clone --depth 1 https://github.com/dinglebear-ai/cortexWrote 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/dinglebear-ai/cortex/frustration-assessment)<a href="https://agentmods.dev/skills/dinglebear-ai/cortex/frustration-assessment"><img src="https://agentmods.dev/badge/skills/dinglebear-ai/cortex/frustration-assessment.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 5.1 | $0.00063 | $0.01435 |
| Opus 5 | $0.00032 | $0.00718 |
| Sonnet 5 | $0.00013 | $0.00287 |
| Haiku 4.5 | $0.00006 | $0.00144 |
Grade A, and why
frustration-assessment 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.
The source is not reproduced here
Licensed AGPL-3.0
The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 130 lines · 63 tokens per session scan A 4987a1af7b8a
frustration-assessment is a skill published in the GitHub repository dinglebear-ai/cortex (3 stars, last pushed today), licensed AGPL-3.0. It adds 63 tokens to every session and 1,435 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 2026-08-31.
Other skills, from other repositories
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website-design-clone
Use when the user wants an agent-ready DESIGN.md derived from a site's observable visual language using Axon brand, scrape, and screenshot evidence.
rag-synthesize
RAG synthesis prompt for axon ask — source-grounded, depth-adaptive, injection-hardened. Loaded at runtime by src/vector/ops/commands/ask/synthesisprompt.rs.
research-papers
Use when the user wants a literature review, paper summary, research landscape, or sourced synthesis from papers, reports, abstracts, or converted PDFs.
cli
Route web search, source indexing, extraction, RAG, jobs, and watch tasks through the local Axon CLI.
knowledge-base
Use when crawling docs, ingesting source families, building or refreshing a Qdrant-backed RAG corpus, capturing docs sections, or producing LLM-ready markdown.