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 shinpr/claude-code-discover --skill recipe-refine-visualsgit clone --depth 1 https://github.com/shinpr/claude-code-discoverWrote 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/shinpr/claude-code-discover/recipe-refine-visuals)<a href="https://agentmods.dev/skills/shinpr/claude-code-discover/recipe-refine-visuals"><img src="https://agentmods.dev/badge/skills/shinpr/claude-code-discover/recipe-refine-visuals.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 | $0.00031 | $0.00848 |
| Opus 5 | $0.00015 | $0.00424 |
| Sonnet 5 | $0.00006 | $0.00170 |
| Haiku 4.5 | $0.00003 | $0.00085 |
Grade A, and why
recipe-refine-visuals 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.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context: Refine the concrete tokens used by a named prototype or downstream design consumer. The output remains docs/product/design/brand-direction.md so consumers read one approved source.
When to Use
brand-direction.mdcontains concrete tokens, or the user names a consumer that now requires them- A design expert wants to refine the values that affect that consumer
Orchestrator Definition
Execution Protocol:
- Follow the refinement flow defined below
- Approval gate: At
[STOP — BLOCKING], present the token decision and resume after explicit user confirmation
Workflow
Read the approved direction and named consumer → review affected tokens → confirm retain/refine decisions → update the shared brand direction when values change.
Execution Decision Flow
1. Context Reading
Gate: Approved brand direction and a named consumer requiring concrete values must be inspectable. Existing tokens may be refined; absent tokens may be added only for that consumer. Return the exact missing decision when either input is unavailable.
Read:
docs/product/design/brand-direction.md— Design Intent, Decision-Relevant Direction, and current Concrete Tokens when presentdocs/product/design-principles.md— trade-off contextdocs/product/personas/— audience context- A referenced product only when its current design can change a token under review
2. Token Review
Present the current or required tokens that affect the named consumer, showing:
- Current value and its derivation source
- How it traces to a Decision-Relevant Direction row or other governing evidence
- Any inconsistencies between tokens (e.g., contrast ratio issues between text and surface colors)
Stop the review when remaining token changes cannot affect accessibility, approved direction, or the named consumer.
3. Expert Refinement
Guide the expert through only the token categories selected for refinement:
Color Tokens
- Present current palette as a visual summary (hex values with role labels)
- Ask: which values to keep, which to override
- Validate applicable text and surface token pairs against the WCAG AA contrast boundary
- Validate color harmony across the full palette
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 · 94 lines · 31 tokens per session scan A 4b9aae7d5f5d
recipe-refine-visuals is a skill published in the GitHub repository shinpr/claude-code-discover (10 stars, last pushed 10d ago), licensed MIT. It adds 31 tokens to every session and 848 once invoked, about $0.0002 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
blueprint-standards
Defines structural design artifact formats — information architecture, user flows, content model, brand direction, Visual Tokens, and AI interaction model. Use when creating or reviewing design artifacts that precede prototype generation.
design-perspective
Integrates design principles, WCAG 2.2 AA accessibility, persona context, and state design into product decisions. Use when reviewing UX decisions, checking accessibility, applying design principles, or ensuring state coverage in acceptance criteria.
recipe-blueprint
Selects and defines the structural design context needed by a prototype or PRD. Use when shared information architecture, flows, content, brand, Visual Tokens, or AI interaction decisions are missing.
recipe-persona
Creates or updates evidence-backed personas with the context and JTBD needed for product decisions. Use when user segments or behavior affect discovery, design, or requirements.
prototype-guide
Defines evidence-grounded prototype quality and external-generator prompt construction. Use when generating or evaluating a prototype, or exporting a prompt for Lovable, v0, or a similar tool.
recipe-refine-visuals
Use when a design-capable human wants to refine the auto-derived Visual Tokens in docs/product/design/brand-direction.md.