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 whystrohm/shotkit --skill brand-lock-extractorgit clone --depth 1 https://github.com/whystrohm/shotkitWrote 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/whystrohm/shotkit/brand-lock-extractor)<a href="https://agentmods.dev/skills/whystrohm/shotkit/brand-lock-extractor"><img src="https://agentmods.dev/badge/skills/whystrohm/shotkit/brand-lock-extractor/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/whystrohm/shotkit/brand-lock-extractor"><img src="https://agentmods.dev/badge/skills/whystrohm/shotkit/brand-lock-extractor.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 | $0.00126 | $0.01980 |
| Opus 5.5 | $0.00050 | $0.00792 |
| Sonnet 5.5 | $0.00025 | $0.00396 |
| Haiku 4.5 | $0.00013 | $0.00198 |
Grade A, and why
brand-lock-extractor 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 27d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Lock Extractor
The blank brand-pack template (_template.md) is the single biggest point of friction in shotkit. Nobody wants to hand-author nine sections of brand parameters before they can produce a single storyboard. This skill removes that wall: hand it what you already have, get a filled brand-lock back.
The output is a brand-lock.md in the exact format validate_brand_lock.py validates and every other skill consumes. Same file the pipeline reads, produced from your assets instead of from scratch.
This skill extracts. It does not invent. Every value is sampled from a real asset or flagged as an estimate the user must confirm. A confident-sounding wrong hex is worse than a flagged guess.
Where the tools and packs are
After ./install.sh, the validators and helpers are in ~/.claude/shotkit-tools/ and the
brand packs are in ~/.claude/shotkit-brand-packs/. A --project install puts both
under ./.claude/ instead. From a repo clone they are tools/ and brand-packs/. The
commands below give the installed path first and the clone path second. The tools need
pip install pyyaml jsonschema.
When to use
Trigger when the user:
- Wants to onboard a brand into shotkit and has assets (a site, a brand book, screenshots)
- Says "extract my brand", "build a brand lock", "make a brand pack from my site/PDF"
- Hands over a URL, a PDF, image files, or a written brand description and asks for a brand-lock
- Has a brand-lock that is half-filled and wants the gaps extracted from assets
If the user has nothing but a vague idea (no assets, no description), they are not extracting, they are authoring. Point them at ~/.claude/shotkit-brand-packs/_template.md (from a clone, brand-packs/_template.md) and help them fill it directly.
Inputs
You can work from any one of these. More sources is a better extraction.
| Source | How you read it | Best for |
|---|---|---|
| Website URL | WebFetch homepage + about + one more page |
Voice, positioning, palette, type |
| Brand book PDF | Read the PDF |
Palette (exact hex), type, motion rules |
| Screenshots / image files | Read the images |
Palette (sample pixels), mood, layout |
| Written description | Use directly | Identity, archetype, voice posture |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 27d ago Changed · +8 lines 36d2c502d0b0
- 1mo ago First seen · 123 lines · 126 tokens per session scan A b6517ce9f5f0
brand-lock-extractor is a skill published in the GitHub repository whystrohm/shotkit (23 stars, last pushed 27d ago), licensed Apache-2.0. It adds 126 tokens to every session and 1,980 once invoked, about $0.0005 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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