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 adilkalam/orca --skill shapegit clone --depth 1 https://github.com/adilkalam/orcaWrote 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/adilkalam/orca/shape)<a href="https://agentmods.dev/skills/adilkalam/orca/shape"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/shape/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/adilkalam/orca/shape"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/shape.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 | $0.00049 | $0.01217 |
| Opus 5.5 | $0.00020 | $0.00487 |
| Sonnet 5.5 | $0.00010 | $0.00243 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
shape 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.
This is a copy
86% identical to shape — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MANDATORY PREPARATION
Invoke {{command_prefix}}impeccable, which contains design principles, anti-patterns, and the Context Gathering Protocol. Follow the protocol before proceeding. If no design context exists yet, you MUST run {{command_prefix}}impeccable teach first.
Register calibration (NEW — runs BEFORE Bakaus's discovery interview)
Invoke interfaces-that-feel peer skill first to determine the product's emotional register: joy/belonging, efficiency/competence, trust/safety, or urgency/clarity. The register conditions which parts of the main spine apply fully vs. which dial back.
See skills/interfaces-that-feel/SKILL.md for the calibration table.
Shape the UX and UI for a feature before any code is written. This skill produces a design brief: a structured artifact that guides implementation through discovery, not guesswork.
Scope: Design planning only. This skill does NOT write code. It produces the thinking that makes code good.
Output: A design brief that can be handed off to {{command_prefix}}impeccable craft, {{command_prefix}}impeccable, or any other implementation skill.
Philosophy
Most AI-generated UIs fail not because of bad code, but because of skipped thinking. They jump to "here's a card grid" without asking "what is the user trying to accomplish?" This skill inverts that: understand deeply first, so implementation is precise.
Phase 1: Discovery Interview
Do NOT write any code or make any design decisions during this phase. Your only job is to understand the feature deeply enough to make excellent design decisions later.
Ask these questions in conversation, adapting based on answers. Don't dump them all at once; have a natural dialogue. {{ask_instruction}}
Purpose & Context
- What is this feature for? What problem does it solve?
- Who specifically will use it? (Not "users"; be specific: role, context, frequency)
- What does success look like? How will you know this feature is working?
- What's the user's state of mind when they reach this feature? (Rushed? Exploring? Anxious? Focused?)
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 · 108 lines · 49 tokens per session scan A 123b86c59bff
shape is a skill published in the GitHub repository adilkalam/orca (2 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 1,217 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to shape, differing in 28 lines, and is treated as a copy.
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