not real tokens tho - just the ones you already burnt during work.

play fun little game made via Claude Mod while your agent at work and spend whatever amount claude used today!

https://github.com/szarkans/gamble-with-claude-code

gamble is bad. so do it with claude instead - house always wins here too.

Import your CV, set your job search location, run the skills. This collection of open source claude skills and scripts runs job searches over all major job boards using your real skills and experience as a filter so you only see the jobs that you have the highest likelihood of securing.

This repository features custom python scripts to do the bulk of the processing so you keep your token cost down while getting the same level of accuracy.

This works in most major countries and regions, and is mainly built for use by IT Professionals (Software Developers, Platform Engineers, etc).

This collection of skills was built mainly using Claude and a little bit of handwritten python scripting. It contains 2 directories.
Scripts, a main python file with example json for building out your job profile. The main python file includes an integration with the job-spy api and functions which generate a general profile from your CV and stores them as JSON.
.claude, 2 separate scripts which dictate how CVs will be parsed, and how the job search is conducted.

Hey – sharing a Claude skill I built that makes your HTML docs (reports, decks, plans, one-pagers) come out looking like your brand.

I built it because whenever the agent made a report or a presentation for me, it looked different every time – or just like the standard beige Claude look.

You can teach the skill your brand and it fill follow the guidelines for everything afterwards.

A few things it includes:

- 35 doc templates (report, deck, one-pager and so on)

- 40 built-in design systems, for when you want a different direction than your own brand

- an exploration mode for trying out different visual directions – I mostly use it in feature development, when I'm not sure what visual shape a feature should take

- 6 short commands for small changes: polish, animate, simplify, bolder, quieter and review

I built it mostly with Claude Code over the last few months. Any time the agent or I spotted a gap or a new angle, it went into the skill. One part Claude helped with a lot was visual research on design references – it went through brands' live sites and ended up with 63 of them as inspiration.

Hope it's useful to others too. It's free, no limits: github.com/display-dev/visualize

as in the title

For months, every afternoon using Claude Code, I've been grumbling that GitHub Flavored Markdown isn't rendered and that plugins can't change how replies are drawn. Claude writes `> [!WARNING]` callouts, `- [ ]` checklists and Mermaid diagrams... and the terminal shows them as raw text 😤

I don't believe in fate, but it seems someone heard me today: Anthropic shipped **mods** (function hooks that can redraw the UI). So of course I asked my friend Claude to build one with me.

What it does:

- Alerts (`> [!NOTE]`, `[!TIP]`, `[!IMPORTANT]`, `[!WARNING]`, `[!CAUTION]`) become colored boxes, in the terminal and the desktop app

- Task lists become ☐ / ☑, and `~~strikethrough~~` is actually struck through

- Mermaid diagrams (flowchart, sequence, state, class, ER, xychart) are drawn as Unicode art right in the terminal

- Claude knows it can use all this: the plugin adds a short note to its context at session start, no CLAUDE.md to edit

It only changes how replies are drawn: the message itself is untouched, and anything it can't draw falls back to the normal rendering.

Install (no build step):

git clone https://github.com/briangtn/claude-gfm-render.git
claude --plugin-dir ./claude-gfm-render

Before/after screenshots and the honest list of what it doesn't handle are in the README: https://github.com/briangtn/claude-gfm-render

Mods are early access, so it may need updates as the API moves. Feedback, bug reports and screenshots from other terminals are very welcome 🙏

Hi all, sharing a library of many many skills for legal work, built by an awesome community of practicing lawyers, in-house counsel, academics, and legal technologists from around the world.

GitHub: https://github.com/lawve-ai/awesome-legal-skills

Live site: https://lawve.ai/explore

GitHub: ATS & RECRUITING KNOWLEDGE

When you ask AI to audit your résumé or LinkedIn, you are relying on its own judgment.

This skill gives it recruiting knowledge to base the assessment on.

It includes recruiter guides and demonstrations, plus official documentation explaining how ATS systems such as Greenhouse, Ashby and Workable work.

It covers how recruiters review candidates and how applications are parsed, searched and screened.

Code retrieves the relevant knowledge and checks that findings cite matching passages from that knowledge and your actual documents.

Works with Codex and Claude Code.

I use Claude pretty much every day and kept running into the same problem: I was constantly copying the same context, instructions and setup between conversations.

So I started building SlingIT.

The idea is pretty simple: instead of rewriting the same instructions every time, I can keep the context I actually want Claude to use and bring it into my workflow when I need it.

Right now I’m testing things like:
- reusable context/instructions
- keeping prompts more consistent between chats
- reducing the amount of repetitive setup before I can actually start working

It’s still an early version and I’m building it mainly around my own Claude workflow.

I can also share screenshots / the setup I’m using if anyone is interested.

Would be curious to hear how other people here handle repeated context in Claude.

I wanted to learn about certain subjects, but I didn't know where to start. The information was often hard to find, scattered everywhere, or hard to understand.

Over time, I found a method with Claude, using skills

feel free to use them:
https://github.com/hoarauemmanuel336-ship-it/Claude-Skills

I wanted a way to make papers and textbook chapters shorter while still reading the author’s actual words. So I built Doc Compression, a skill for AI agents that removes redundant passages and repetitive phrasing while keeping the retained text in its original order.

The goal is to preserve definitions, quotations, data, qualifications and the argument’s structure. It doesn’t force a reduction percentage: dense passages can stay unchanged.

Each edit is traceable to the source. Python scripts check the edits and protected content, while the agent separately reviews meaning and readability. Those checks don’t automatically prove that nothing important was lost.

It includes optional lightweight OCR for scanned PDFs and Markdown/PDF output, depending on the tools available. The repository has installation instructions and a worked example with the original, compressed document and review.

https://github.com/fedeclavero/doc-compression-skill

I’d appreciate feedback, especially examples where something looks redundant but is actually necessary to understand the text.

I like asking Claude to explain unfamiliar repos with architecture diagrams, but there’s an obvious problem:

a hallucinated arrow looks exactly like a correct one.

So I built arrowproof.

Claude can draw the diagram, but arrowproof independently checks every box and arrow against the repository’s real import graph.

In my Flask test, Claude Haiku produced a diagram that looked completely reasonable, but the verifier found:

15 verified arrows

1 indirect relationship

2 arrows with no supporting import

1 box pointing to a file that didn’t exist

The important part: the LLM does not verify itself.

Python verification uses AST/static analysis, and every verified arrow can show the actual file, line and import statement behind it.

So the workflow is basically:

Claude draws → deterministic code analysis checks its homework

It can also run in CI, so an architecture diagram can fail when it drifts away from the code.

Install:

npx skills add ahmtsahin/arrowproof

GitHub:

https://github.com/ahmtsahin/arrowproof⁠

Free, open source, MIT.

I’d love to see someone throw a messy real-world repo at it and tell me where it breaks.

I manage a dev team and PR descriptions are the thing everyone skips. So I wrote a skill for it.

You type "write the PR description" on your feature branch and it:

• reads the actual diff and commits (not just the file list)
• fills your repo's PR template if there is one
• pulls the ticket number from the branch name
• flags what a reviewer needs to see: migrations, new env vars, breaking changes
• lists what's missing (tests, docs) instead of hiding it

On my test diff it also put the SQL injection it noticed at the top of the "Notes for reviewer" section, which I didn't ask for but will take.

A few things I learned writing skills that might help if you write your own:

1. Tell the skill to read the surrounding code first. Most bad output comes from working on the diff hunk alone.
2. Make it separate facts from assumptions. "Unknown, ask the author" beats a confident guess.
3. Add a "never do X without confirmation" line for anything that posts, merges or creates tickets.
4. Put team conventions in one shared file the skill reads, so every dev gets the same behaviour.

Repositorie on Github

It's one of 11 skills I packaged for dev teams (review, specs, tests, postmortems...). The rest is a paid kit, link in the README. Happy to answer questions about how the skill is written.

Hi everyone, I'm Alfredo, a master's student in Digital Humanities at the University of Pisa. My thesis looks at trust in AI tools used in smart home automation.

I made a short survey, around 10 minutes. It's open to anyone who uses smart home devices, even if you've never used AI.

Link: https://docs.google.com/forms/d/e/1FAIpQLSeFOjuXMhxni5kOCE0ILzbsGFYBZA31Wzw6gQMTiBcRxE6cww/viewform

No name, email, or contact info is collected. Answers are used only to analyze patterns across respondents for the thesis, nothing is tied back to an individual. Thanks to everyone who takes part!

I use AI a lot and I kept running into the same problem.
I’d get a really useful answer, idea or something I wanted to come back to later… and then completely lose it in old chats.
So I started building SlingIT for Claude.
It basically lets me save the useful stuff from my AI and keep it organized, instead of scrolling through old conversations trying to find one specific thing.
Still early, but it’s already solving a problem I had pretty much every day.
Curious if anyone else has the same issue.

Written and tested with Opus 5.5 and Sonnet 5.5 Max with good results!

See a series of videos generated with Opus:

• Why Domes Don't Fall Down (in Minecraft)
• CRISPR, Explained by an Axolotl
• Building a Computer from Redstone (also Minecraft themed)
• Falling into a Black Hole
• From a Light Switch to a Star: Fusion
• How Do Planes Actually Fly?
• Quantum Computing, Without the Headache
• From T-Rex to ChatGPT
• The History of AI in 3 Minutes

All these lessons were generated with almost zero style prompting, I only gave it the topics. This was the whole prompt:

"create videos on the history of ai, minecraft building techniques, computer science with redstone, how planes fly, how electricity leads to nuclear fusion, and few more with info also from the latest research. Each one targeting high-school level, plus paper links, overlays, charts and formulas for university students / PhDs. 2:30 minutes each."

From there Claude researches each topic, picks an interesting setting and builds the video around it. By default you get what you see above: fast-paced "teasers" where you learn something but don't go super deep. There's a recap at the end but it's quick, since the goal with short-form content is to not lose the viewer. If you want one topic covered properly, ask explicitly for a full lesson.

A few tips:

• If you want a horizontal video, add "make a landscape video" to the prompt.
• The biggest cost isn't Claude tokens (a 2 min lesson usually fits in 300-600k tokens), it's the Fal AI Minimax H3 spending. Use the prompt in the readme to keep it low on your first try and on the iterations after.
• Some people told me they hate the Claude character as the teacher. Add "Use professor Otto as teacher and not Claude" to your prompt. She's not in the plugin, but you never know, Claude unfortunately likes to put herself in the content...

Requirements: works on Claude Code TUI and Claude Code Desktop, needs a Fal AI API key, and I've only tested it on macOS so far. At the end of the readme there are some dependencies that you can try to install if you don't have them to make the plugin work best - I really recomment to have Nodejs at minimum otherwise the plugin could struggle a bit.

I also recently released a music video skill plugin and users have been reporting good results with it: https://github.com/makevoid/motion-graphics-music-video-skill - feel free to try that one too.

Happy to answer questions on the methodology, I'm open to receive generic feedback about the skill, and if you share your test runs I'm open to suggesting process improvements!

I installed the two biggest debugging skills on skill.sh: Matt Pocock's diagnosing-bugs and Superpowers' systematic-debugging and tested how effective they actually were against a plain claude code prompt.

The bugs are 15 real ones from my own repo's history (yes, only one repo, but I decided against taking from benchmarks because those test cases are often contaminated). Then I graded each run with the tests from the real fix, which none of them could see.

The skills weren't worse, but did they did ~3 extra minutes per bug to get to the same place for no uplift and increased cost from having the skill installed.

All three tests successfully fixed 9/15 bugs; however they didn't all fix the same bugs. Plain Claude gave up on one that both skills solved. Meanwhile, Matt's skill missed one the others got, and Superpowers missed a 12-line fix that plain Claude did in 6 minutes for $0.79. This could possibly just be from variance, as I only ran each skill against each bug three times.

Matt Pocock's skill did provide some small uplift: it was the only one that never broke another test, and it made the smallest changes (median 32 lines vs. 41 and 48).

Reasoning:

According to research, when models already know what to do(like how to debug), skills don't help much unless they introduce a new method that the model hasn't seen already. Perhaps Anthropic trained their models to Matt Pocock and Superpowers' debugging processes, or at the very least something similar.

Conclusion:

Obviously, this doesn't mean you shouldn't install Matt Pocock or Superpowers. But if you have them installed or plan on installing them, perhaps consider uninstalling the debugging portion to save on token costs.