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 agentmods add skills/nicknisi/claude-plugins/link-readernpx skills add nicknisi/claude-plugins --skill link-readergit clone --depth 1 https://github.com/nicknisi/claude-pluginsWrote 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/nicknisi/claude-plugins/link-reader)<a href="https://agentmods.dev/skills/nicknisi/claude-plugins/link-reader"><img src="https://agentmods.dev/badge/skills/nicknisi/claude-plugins/link-reader.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 | $0.00053 | $0.01045 |
| Opus 5 | $0.00026 | $0.00522 |
| Sonnet 5 | $0.00011 | $0.00209 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
link-reader 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 3d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Link Reader
Read content from URLs that block direct web fetching by routing through proxy APIs.
Supported Platforms
Twitter / X
URL patterns: twitter.com/*/status/*, x.com/*/status/*, twitter.com/*/article/*, x.com/*/article/*
Proxy: FxTwitter API (api.fxtwitter.com)
Steps:
- Extract the path after the domain (e.g.,
/trq212/status/1234567890) - Fetch
https://api.fxtwitter.com{path}using WebFetch - Parse the JSON response
- Format based on content type (see below)
Regular Tweets
The response JSON has shape: { tweet: { text, author, likes, retweets, ... } }
Format as:
**@{author.screen_name}** ({author.name})
{tweet.text}
{if media.photos: list image URLs}
{if media.videos: list video thumbnail URLs}
{if quote: show quoted tweet inline, indented}
Likes: {likes} · Retweets: {retweets} · Views: {views}
{created_at}
Twitter/X Articles
When tweet.article is present, the tweet is an article (long-form post). The article content uses Draft.js block format.
The article object contains:
title— article titlecover_image— hero image metadata (useoriginal_img_url)content.blocks[]— array of content blockscontent.entityMap— links and media referenced by blocks
Converting blocks to markdown:
Each block has a type and text:
unstyled→ plain paragraphheader-one→# headingheader-two→## headingheader-three→### headingblockquote→> quotecode-block→ fenced code blockunordered-list-item→- list itemordered-list-item→1. list itematomic→ look up in entityMap for embedded media/links
Resolving entity ranges:
Each block may have entityRanges: [{ key, offset, length }]. Look up content.entityMap[key]:
- If
type: "LINK"→ wrap the text span in[text](url)usingdata.url - If
type: "IMAGE"→ insert
Resolving inline styles:
Each block may have inlineStyleRanges: [{ style, offset, length }]:
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.
- 3d ago First seen · 146 lines · 53 tokens per session scan A 2743959e2efc
link-reader is a skill published in the GitHub repository nicknisi/claude-plugins (114 stars, last pushed 23d ago), licensed MIT. It adds 53 tokens to every session and 1,045 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
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brainstorming
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auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…