Borrowing it
Nothing to install: this file belongs to victoriacity/openakari. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/victoriacity/openakari/main/.claude/skills/design/SKILL.mdgit clone --depth 1 https://github.com/victoriacity/openakariWrote 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/victoriacity/openakari/design)<a href="https://agentmods.dev/skills/victoriacity/openakari/design"><img src="https://agentmods.dev/badge/skills/victoriacity/openakari/design/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/victoriacity/openakari/design"><img src="https://agentmods.dev/badge/skills/victoriacity/openakari/design.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 | $0.00017 | $0.02265 |
| Opus 5.5 | $0.00007 | $0.00906 |
| Sonnet 5.5 | $0.00003 | $0.00453 |
| Haiku 4.5 | $0.00002 | $0.00227 |
Grade A, and why
design 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/design
You are designing a rigorous empirical study. Your job is to turn a research question into a concrete experiment design that follows the experiment-design schema in CLAUDE.md — but with the methodological judgment to fill it well, not just structurally.
The argument is a research question, an open question from a project, or a project path. If a project path, read the README to find the relevant open questions and existing results.
Step 0: Knowledge framing
Before any design work:
- What knowledge does this experiment/change produce?
- If this is an infrastructure change: it is an experiment on the system itself. The knowledge output is "does this design pattern work?" Findings are about the system's behavior, not about code correctness.
- State the knowledge output explicitly before proceeding.
Step 1: Understand the question
- Read relevant project READMEs, logs, and existing results.
- Identify which CI layers are involved. State them explicitly.
- Check
decisions/for prior methodological choices that constrain the design. - Check existing results — are there baselines, pilot data, or error analyses that inform the design?
Step 2: Formulate the hypothesis
Write a falsifiable hypothesis. A good hypothesis:
- Makes a specific, directional prediction ("Rubric prompts will increase agreement by >5pp" not "Rubric prompts might help")
- Is grounded in prior evidence or reasoning (cite the evidence)
- Can be refuted by an achievable experiment
If the question is exploratory (no clear prediction possible), say so explicitly and frame it as a measurement study with defined quantities of interest rather than forcing a hypothesis.
Step 3: Choose variables and metrics
Independent variables: What are you varying? Justify why these variables and not others. Name alternatives you considered and why you rejected them.
Dependent variables / metrics: What are you measuring? For each metric:
- What does it capture? (agreement, accuracy, ranking consistency, etc.)
- What are its assumptions? (e.g., Cohen's kappa assumes nominal categories; Kendall's tau assumes ordinal ranking; Pearson's r assumes interval scale and linearity)
- What are its failure modes? (e.g., kappa is sensitive to prevalence; accuracy ignores ties)
- Why this metric over alternatives?
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.
- 2mo ago First seen · 190 lines · 17 tokens per session scan A 80a962571ecb
design is a skill published in the GitHub repository victoriacity/openakari (47 stars, last pushed 7mo ago), licensed MIT. It adds 17 tokens to every session and 2,265 once invoked, about $0.0001 per session on Opus 5.5. 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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