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 pandysp/claude-plugins --skill design-optionsgit clone --depth 1 https://github.com/pandysp/claude-pluginsWrote 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/pandysp/claude-plugins/design-options)<a href="https://agentmods.dev/skills/pandysp/claude-plugins/design-options"><img src="https://agentmods.dev/badge/skills/pandysp/claude-plugins/design-options/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/pandysp/claude-plugins/design-options"><img src="https://agentmods.dev/badge/skills/pandysp/claude-plugins/design-options.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 | $0.00099 | $0.01152 |
| Opus 5.5 | $0.00040 | $0.00461 |
| Sonnet 5.5 | $0.00020 | $0.00230 |
| Haiku 4.5 | $0.00010 | $0.00115 |
Grade A, and why
design-options 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 1mo 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design options: strong options with explicit tradeoffs
Present multiple strong options (all worth choosing) with explicit tradeoffs. The user picks the direction, not you.
The ideals-first method
Don't list "Option A, B, C" off the top of your head. That produces one real option and two straw men. Anchoring on a single ideal is the same failure one level up: every option becomes a point on one line. The user picks how far to go but never which direction. Anchor against at least two ideals.
1. Find the ideals (at least two)
What's the right answer with no constraints? No time pressure, no legacy code, no budget limits. That question has more than one answer because design values conflict: simplicity or flexibility, throughput or latency, consistency or autonomy. Each weighting gives a different ideal target architecture. Find at least two with genuinely different tradeoff profiles.
Every target is a real ideal state. Targets differ in what they optimize for, never in ambition. Constraints enter in step 2, not here.
Targets are navigation tools. Every practical option is a conscious step toward one of them, with a clear reason for the compromise.
If one target honestly dominates on every axis the user cares about, say so and show why. Then the only open question is how far to go. Try a competing weighting before concluding this.
2. Identify the constraints
What prevents each ideal? Name each explicitly: time, existing code, dependencies, knowledge gaps, risk tolerance. Each constraint justifies a deviation.
3. Generate options as stepping stones toward the targets
Each option is a step toward a named target. Range from a step within current constraints → relaxing one major constraint → near-target. The set must span at least two targets. You don't need the full gradient for each. Options vary in direction and scope, never in craftsmanship. The cheaper option is a smaller, well-made thing, not a sloppy version of the bigger one. Every option must be elegant for its declared scope; if you wouldn't genuinely recommend it, don't include it.
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.
- 1mo ago First seen · 86 lines · 99 tokens per session scan A a5300e24721e
design-options is a skill published in the GitHub repository pandysp/claude-plugins (6 stars, last pushed 7d ago), licensed MIT. It adds 99 tokens to every session and 1,152 once invoked, about $0.0004 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 2026-08-31.
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