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 MarieLynneBlock/arcanum-artifex --skill eval-driven-devgit clone --depth 1 https://github.com/MarieLynneBlock/arcanum-artifexWrote 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/marielynneblock/arcanum-artifex/eval-driven-dev)<a href="https://agentmods.dev/skills/marielynneblock/arcanum-artifex/eval-driven-dev"><img src="https://agentmods.dev/badge/skills/marielynneblock/arcanum-artifex/eval-driven-dev/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/marielynneblock/arcanum-artifex/eval-driven-dev"><img src="https://agentmods.dev/badge/skills/marielynneblock/arcanum-artifex/eval-driven-dev.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 | $0.00059 | $0.03878 |
| Opus 5 | $0.00030 | $0.01939 |
| Sonnet 5 | $0.00012 | $0.00776 |
| Haiku 4.5 | $0.00006 | $0.00388 |
Grade A, and why
eval-driven-dev 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 8d 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
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
18 files 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.
- references/1-a-project-analysis.md 5.7 KB
- references/1-b-entry-point.md 2.3 KB
- references/1-c-eval-criteria.md 7.5 KB
- references/2a-instrumentation.md 7.3 KB
- references/2b-implement-runnable.md 7.4 KB
- references/2c-capture-and-verify-trace.md 5.7 KB
- references/3-define-evaluators.md 10 KB
- references/4-build-dataset.md 22 KB
- references/5-run-tests.md 6.0 KB
- references/6-analyze-outcomes.md 16 KB
- references/evaluators.md 18 KB
- references/runnable-examples/cli-app.md 2.0 KB
- references/runnable-examples/fastapi-web-server.md 4.1 KB
- references/runnable-examples/standalone-function.md 1.8 KB
- references/testing-api.md 16 KB
- references/wrap-api.md 8.8 KB
- resources/setup.sh 3.1 KB runs code
- resources/verify_step6_completion.py 4.4 KB runs code
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.
- 8d ago First seen · 212 lines · 59 tokens per session scan A 6a6b1ee1d1bf
eval-driven-dev is a skill published in the GitHub repository MarieLynneBlock/arcanum-artifex (4 stars, last pushed 3d ago), with no licence file. It adds 59 tokens to every session and 3,878 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
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"…
golden-set-maintenance
Curate and maintain "golden set" eval items — the small, high-signal cases that must never regress. Covers selection criteria, review cadence, retiring stale items, and keeping the set sharp. Use this skill when building a sanity-check eval that runs on every PR, defending against silent quality drops, or your full…
regression-evals
Set up continuous regression evals so model/prompt/tool changes don't silently break existing behavior. Covers gating thresholds, CI integration, statistical significance, and response to regressions. Use this skill when deploying prompts to production, gating model upgrades, or noticing "it worked yesterday" in AI…
nemo-automodel-recipe-development
Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.
neuron-test-engineer
Write tests for Neuron AI agents, RAG systems, workflows, and tools using the built-in testing utilities. Use this skill when the user mentions testing agents, writing unit tests, mocking AI providers, testing tool execution, verifying RAG retrieval, testing workflow behavior, or creating test cases for Neuron AI…
quality-hooks
Language-specific auto-lint/format/typecheck pipeline. Supports Python (ruff+pyright), TypeScript (prettier+eslint+tsc), Go (gofmt+golangci-lint). Auto-fix and convergence loops.