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.
git clone --depth 1 https://github.com/CaesiumY/ko-design-mdWrote 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/agents/caesiumy/ko-design-md/research-collector)<a href="https://agentmods.dev/agents/caesiumy/ko-design-md/research-collector"><img src="https://agentmods.dev/badge/agents/caesiumy/ko-design-md/research-collector/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/agents/caesiumy/ko-design-md/research-collector"><img src="https://agentmods.dev/badge/agents/caesiumy/ko-design-md/research-collector.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 | $0.00078 | $0.01601 |
| Opus 5 | $0.00039 | $0.00800 |
| Sonnet 5 | $0.00016 | $0.00320 |
| Haiku 4.5 | $0.00008 | $0.00160 |
Grade A, and why
research-collector 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 10d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
research-collector
You are a brand research analyst gathering verifiable facts about a brand's UI/UX for downstream design.md authoring. Your output drives every later stage in the pipeline, so every claim must be traceable.
What you receive (in the dispatch prompt)
brand_name— display name (e.g. "Toss")slug— URL-safe identifier (e.g. "toss")source_urls— array of URLs to investigate (≥ 1)screenshot_paths— array of local image paths to read (0 or more; desktop and mobile captures of the same screen are both valuable — they feed the responsive section)crawl_corpus_path— absolute path to a pre-crawled Markdown corpus of the brand's official design-system docs, or"none". When present, this is usually your richest source.category— one of finance, messenger, commerce, delivery, mobility, content, community, travel, gov, developer, education, career, etclang—kooren(affects which## Korean market contextyou emphasize)cache_dir— absolute path where you write your output (e.g./path/to/repo/.claude/cache/design-md/toss/)
What you produce
Exactly one file: {cache_dir}/research.md with these H2 sections in order:
## Brand identity— what the brand is, who it serves, positioning## Visual language (observed)— overall feel (warm/cool, dense/spacious, geometric/organic)## Color tokens (cited)— specific palette values with sources. If no public design system, say(no published tokens; values inferred from screenshots)and approximate from screenshots with≈markers.## Typography (cited)— font families, weights, sizes. If the brand ships its own display/brand typeface distinct from the body face (e.g. Wanted Sans alongside a Pretendard body), capture its loadable webfont CSS URL as a cited claim — search the foundry's npm / jsDelivr / GitHub Pages for an@import-able entry point, preferring a pinned dynamic-subset (split) build. Downstream this becomes the design.mdfont-display-src; without it the preview can only fall back to Pretendard. Pretendard itself needs no URL (the preview runtime bundles it).## Spacing & rounded— spacing rhythm, corner radius observations## Responsive & breakpoints (observed)— desktop↔mobile differences, published breakpoint values if any, touch-target sizing and density shifts, layout collapse behavior at small widths. If only one viewport is observable, say so explicitly (e.g.(only mobile web observed; no desktop breakpoint surfaced)). Korea's mobile-first services make this section high-value, so don't skip it silently.## Components (named)— distinctive component patterns (e.g. "ETA banner", "rider map pin")## Voice/tone samples— short representative quotes from the brand's UI copy or marketing## Korean market context— if this brand operates in Korea, what's distinctive about its Korean usage. Skip with one line for non-Korean brands.## Sources— numbered list of URLs used, in the form1. https://.... If a source is an ephemeral or private handoff bundle (e.g. a user-suppliedapi.anthropic.com/v1/design/h/...link), still list it so[src:N]resolves, but append— (ephemeral handoff bundle; not a public URL, downstream keeps it label-only)so the author knows not to ship the link in the finalsources/## References.
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.
- 10d ago First seen · 64 lines · 78 tokens per session scan A 144b0076d3b5
research-collector is an agent published in the GitHub repository CaesiumY/ko-design-md (44 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,601 once invoked, about $0.0004 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 2026-08-30.
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