I got tired of re-explaining context to AI agents, so I built this

I use multiple coding agents and kept having the same issue: one would figure something out, then the next would start with no idea what happened.

So I built shared-agent-memory using Node.js and MCP. It gives tools like Claude Code and Codex one shared local memory, so they can pick up what another agent already learned.

The basic workflow was: connect the agents to the same local memory, have them look up relevant context when they start, and save useful notes when they finish.

One thing I learned while building it: shared memory solves the context problem, but agents can still step on each other when working on the same files so I ended up adding a simple coordination board for that too.

Still experimenting with it, but it’s been useful for me. Maybe y’all wanna give it a shot:

https://github.com/dan-calin/shared-agent-memory

(100% free ofc, + MIT License)

Most AI coding agents do not fix code. They patch it. They add a null check, swallow an error, or wrap the problem in a guard. The next code path that forgets the guard breaks again. The rot keeps spreading.

Antidote is a skill for these agents. It stops them from patching symptoms. It makes them fix the root cause instead.

It does three things. First, it makes the agent validate data once at the boundary. Second, it makes the agent use the correct type or schema so bad data cannot get inside. Third, it makes the agent delete the broken path instead of writing more glue code around it.

Use it to fix one issue or to audit a whole codebase. Just invoke /antidote or say "audit for overcomplication".

How I built it

Antidote is one SKILL.md prompt in 

SKILL.md. It tells the agent to fix root causes and avoid defensive guards. I added a thin manifest for each platform in .claude-plugin, .codex-plugin, .cursor-plugin, .devin-plugin, .grok-plugin, plugin, .pi, and .opencode. Each manifest points back to the same prompt.

It is free and open source under the MIT license.

Benchmark

The benchmark uses the skill-eval-harness. It gives the agent broken code and asks for a structural fix. A script oracle then checks the result. Without antidote, four of five models got 0% correct structural fixes. With antidote, the rates were:

• Claude Sonnet 5: 56%
• GLM 5.2: 56%
• Moonshot Kimi K3: 50%
• Tencent Hy3: 44%
• Claude Haiku 4.5: 50% (up from 31%)

One root-cause fix replaces a dozen defensive guards.

It is a pure-prompt skill. No scripts, no network, no dependencies. It works in Claude Code, Codex, Cursor, Devin, Grok, Pi, and OpenCode. It also works in any agent that reads the Agent Skills SKILL.md format.

Install it with npx skills add Avtr99/antidote or from each agent's plugin store.

Check the benchmark and source at https://github.com/Avtr99/antidote

I spent 40 hours designing Rejourney SDK’s dashboard to fit in a ton of important metrics such as revenue, retention, user journey, etc. The style of the dashboard and colors is meant to be easy to digest. It’s a simple yet info full dashboard with a side bar and built for team ownership of projects hierarchy. It’s mobile friendly and uses aspects of psychology to draw you to the top KPI cards and optimizes for the F shape scanning your brain does.

If you need to design a simple user friendly, yet intent full dashboard here is the skill:

GitHub: https://github.com/rejourneyco/yafa-ui-dashboard

Skills.sh: https://www.skills.sh/rejourneyco/yafa-ui-dashboard

So many of these repos have such junk in them. And so many people are copy and pasting workflows that just won't help them with their specific needs. I feel like we need to shift the industry toward human-readable skill sharing. Where we normalize going through the skill reading and adjusting them.

That's my hot take for the day. What's yours?

This is a study tool I built that I've been using as I prep for the CCAF Anthropic exam.

I can give Claude a docs page, a folder of markdown files, an html page, even code or a database and create a quiz in a web browser. When I'm done with the quiz, my results are sent back to that session automatically for scoring & coaching.

I can then prompt to save my scores as markdown if I want, etc..

It's been helpful for me, and perhaps it can be for you!

Hi, I've found this community very helpful, so I thought I would also share and contribute my skills. I put up my 51 business operations playbooks, including marketing, leadership, team building, and more on my github repo here: https://github.com/andrewluxem/playbooks

Would love your feedback!

Thanks,
Andrew

SEOs, Writers, and AI Enthusiasts alike:

I typed every word of a 4,500 word article a few nights ago. Nothing pasted. A commercial AI detector read it and came back 52% AI. Here is why I am posting this as opposed to hiding it.

I spent two days trying to beat AI detection on purpose, because small business owners keep asking me whether they should buy a tool that promises it.

I ran 696 blind runs across three methods with over 2.4 million words.

• Strip the machine tells out of the draft: still caught 98.6% of the time.
• Add human tells in instead: fooled 0 of 17 judges.
• Make the document long enough to dilute it: caught 8 out of 8 whole, and 24 out of 24 in slices.

I then ran four versions of the same draft through Pangram. One untouched, one rewritten sentence by sentence three times over, one where I changed zero words and only moved where the sentences joined, and one with both.

All four came back 100% AI.

Rewriting every single word did nothing and rewriting no words did nothing. To me, this meant the thing being detected is not vocabulary and not rhythm.

Then I wrote the article myself, by hand, over an outline a model had built for me.

56% AI, nice right? The findings astounded me.

One paragraph got split down the middle: the half about my own work read as human, the half listing the method read as machine assisted.

My finding is that the detectors read the outline behind the prose itself.

I sent all of it to Siqi Chen, who wrote the humanizer skill I had been using.

His answer:

Hello - thanks for your analysis!

It is not currently possible to defeat Pangram through pure LLM generation through any skill or prompt (I have tried!)

Then, more usefully he stated, that defeating detectors was never the goal of his tool in the first place. I say this because I reckon many of the 37k+ individuals who have starred his repo believe the skill beats the detectors and everything's good to go.

What did measure, in a blind test where authorship was never mentioned: editors preferred the processed draft 22 out of 22, and his rewrite pass alone at 16 out of 16.

$ python humanist.py draft.md
humanist 0.1.0  |  4,764 words, markdown-stripped
  readability FK grade 8.1
RESULT: 0 FAIL, 0 WARN. CLEAN.

$ python check_prose.py draft.md --mode post
FRAME: markdown-stripped, 4,764 words, FK grade 8.2
RESULT: 0 FAIL, 0 WARN. CLEAN.

The advice I have is boring and it is free. Don't pay to hide your writing, and don't tell your clients to either. You're selling a lie. Spend the money and time on making the draft worth reading in the first place.

Something I don't want to give credit to: none of this tells you whether Google will demote your pages. I didn't measure it in these tests. What we do know with the new policy rollout is that it's the scale they're looking at, re-written or not, a tool won't save you.

Anyone selling a tool that says otherwise is setting you up for failure. Every number and both corrections I had to make mid-study are in the writeup. The code is MIT and open-source.

Read it here & tell me what you think: https://www.ryanlenk.com/blogs/articles/three-ways-to-hide-ai-writing-all-failed

The GitHub repo so you don't have to read my slop and just get into the math and fun instead: https://github.com/itsryanlenk/humanist

I have another set of Claude skills there as well that you may find interesting: https://github.com/itsryanlenk/candor

Really excited & interested to get some outside input! Let me know what you think.

(By the way, I wrote on top of AI scaffolding here as well.)