Borrowing it
Nothing to install: this file belongs to whitelonng/dshcode. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/whitelonng/dshcode/master/.agents/skills/record-browser-gif/SKILL.mdgit clone --depth 1 https://github.com/whitelonng/dshcodeWrote 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/whitelonng/dshcode/record-browser-gif)<a href="https://agentmods.dev/skills/whitelonng/dshcode/record-browser-gif"><img src="https://agentmods.dev/badge/skills/whitelonng/dshcode/record-browser-gif/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/whitelonng/dshcode/record-browser-gif"><img src="https://agentmods.dev/badge/skills/whitelonng/dshcode/record-browser-gif.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 | $0.02534 |
| Opus 5.5 | $0.00040 | $0.01014 |
| Sonnet 5.5 | $0.00020 | $0.00507 |
| Haiku 4.5 | $0.00010 | $0.00253 |
Grade A, and why
record-browser-gif 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
100% identical to record-browser-gif — 132 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Record Browser GIF
Produce a short, truthful UI demonstration as a local GIF, and — only when the task includes attaching it to a pull request — publish it through the assets-branch workflow at the end of this skill. Use the browser-control skill for interaction and the bundled encoder for repeatable timing, dimensions, and size.
The evidence-chain decision owns why one storyboard comes from one isolated run and why publication revalidates both the artifact and the demonstrated pull-request head.
Every GUI pull request includes a GIF
A pull request that changes product-user-visible GUI behavior MUST include a demonstration GIF recorded with this skill and embedded in the pull request body via the assets-branch workflow.
The recording itself is part of the evidence: use a real server booted from that pull request's branch tree, a real API key, and real model rounds. Never substitute fixture queries, mock transports, synthetic event injection, or test-only hooks unless the user explicitly asked for a fixture recording. Next to the embed, state the exact demonstrated commit SHA, the tree and origin that served it, any mode flags or browser-state exceptions, and whether a real model round ran, so reviewers know exactly what the recording proves.
Keep recording separate from publication
- Recording produces frame images and one local
.gifartifact only; it never mutates remote state. - Publication — pushing the GIF to an assets branch and embedding it in a pull request body — is the separate final step, performed only when the task includes attaching the GIF to a pull request. It never touches the pull request's own branch.
- Preserve the requested recording conditions. A real-server or real-API demo must not use fixture queries, mock transports, synthetic event injection, or test-only hooks. If credentials or the server are unavailable, report that limitation instead of substituting a fixture.
- Never read or expose credential values. Use the application's normal configuration path and a benign demonstration prompt.
What ships with it
1 file 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.
- 1mo ago First seen · 111 lines · 99 tokens per session scan A d0fd6fca1ef8
record-browser-gif is a skill published in the GitHub repository whitelonng/dshcode (704 stars, last pushed 1mo ago), licensed MIT. It adds 99 tokens to every session and 2,534 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to record-browser-gif, differing in 132 lines, and is treated as a copy.
Other skills, from other repositories
visionary-cli
Analyze images with DeepSeek's vision model via the visionary-server CLI. Use this whenever the user provides an image, photo, screenshot, or document with images - run vision to look at it rather than guessing.
browser-automation
Drive the PenguinHarness agent browser — the desktop app's built-in browser or the user's own Chrome — from the shell with penguin browser: open pages, read them as simplified HTML or text, act with JavaScript and trusted clicks and typing, and pull structured data out of them (orders, search results, tables), signed…
firecrawl
Search the web and scrape pages into clean markdown with the Firecrawl API — query-based discovery, single-URL extraction including public PDFs, driven by curl with a vault-stored API key.
browser-skill
Automate the user's logged-in Chromium browser: read pages, fill forms, scrape data, operate tabs, test a UI, or debug a website. Requires the bsk CLI and browser extension.
deepseek-harness
Use when building AI agent applications with a plugin-based architecture — Web UI, CLI, Python SDK, Cordis plugin system, multi-model orchestration. DeepSeek Harness (dsh): open-source agent harness by DeepSeek AI where everything is a plugin, powered by Cordis for spatiotemporal composability.
browser-qa
Drive a real headless browser against a locally-served web UI and capture screenshot evidence, reporting step-level PASS/FAIL/BLOCKED with extracted DOM text and console errors. Use when a phase on the docker QA image must exercise a rendered UI and attach screenshots — it complements the text-evidence verify/qa-test…