I see what this is going for but can’t help but feel like it’s overwrought. It feels a bit like the most likely outcomes are increased token burn, review surface, and time per task vs not using this.
Factor III — Mission Definition. Factor IV — Structured Planning. Factor IX — Traceability.”
Why are the Roman numerals in that order, what do they reference and why. Why is this Pilar 2. Etc. It’s easy to get lost in this, even if it would be the best approach in the world.
IMO, these agentic guard rails aren't the answer. It seems like we're seeing that the more you stuff context, the more the agents forget and don't follow the guidelines.[1]
Things like ArchUnit, static analyzers, and other deterministic tools can help with lower level things like architecture. For higher up stuff, I am increasingly feeling like agents don't guarantee anything and in many cases its just the opposite. This is where a thoughtful engineer and reviewer can keep things in check.
It's possible I'm off base here, but I can't make heads or tails of the LLM written readme.
I’ve found hooks to be really useful for correcting or inducing certain behaviors/outcomes that tend to occur during agentic development.
For example, I’m working on a project to remake the Final Fantasy XI client. My repository has a bunch of git submodules that reference other peoples’ related efforts, and an open source server. For example, despite including CLAUDE.md to suggest otherwise, Claude Code always ends up writing these insanely dense comments referring to specific files and lines of code in submodules. When those submodules update, now the comments are no longer correct.
So I added a hook to detect when comments are included. Then, for example, I have a deterministic heuristic and script involved to remove some kinds of comments, and another that asks Haiku to quickly LLM-as-a-judge whether or not to edit/remove the comment.
Similarly, I have a stop hook that reminds Claude to commit its code logically on main, noting that other changes may have been added by other concurrent sessions (I avoid worktrees and even branching for this particular project and stage of development.) It works well.
why is so hard for people to create a CONTRIBUTE.MD file which tells anyone (including AI) how to contribute to the repo. You can also set gates and everything.
I feel like this is going to add 100K tokens and 10K rules to everything that the agent is trying to do. Sort of like dumping the Clean Code book into context and saying, hey now you know how to code cleanly, write great code now
I created a simple git repository with company skills. Basically just a collection of skills around tools and practices we share. One of the skills is "update company skills" this simply pulls the changes from git and wires them into the user's ~/.codex directory. You can probably do something similar for claude code.
This is far from perfect but we're in this weird transition phase where none of the major AI tool providers are really focusing much on team use of their stuff. But I expect that will start changing soon.
Current tools mostly focus on individuals doing things in isolation. And of course in a team there's more to collaborating than throwing stuff at each other via github. A central repository of company skills is merely our way of improvising a solution.
I find it interesting that Anthropic hired a few of the key people behind Zulip recently. Team chat with tightly integrated AI tools could be a missing piece here. Team communication flows and processes, including ways of working and guardrails are sort of the next piece of the puzzle here. Going from everyone doing their own thing to teams and companies doing things together is going to be a bit of a journey.
The split that has worked best for me is to keep agent instructions about intent and workflow, then move anything mechanically checkable into tests, linters, or build gates. More rules in context are not enforcement.
For a central skills repository I would also record the exact rules revision in each run or generated artifact. Otherwise a failed run becomes hard to reproduce after the shared repository changes. It would be useful if each skill declared which claims are advisory and which are backed by a command the agent can execute and verify.
you can read each run step by step and see what the agents are doing and how the system prompt and available tools are steering their behaviour to take longer and higher cost
I hate to be that guy, but I'm not reading a readme written by AI, nor using their tool. You don't care to put in a few hours to describe your project, I'm not going to bother learning about it.
DO NOT INSTALL THIS VIA NPX OR OPEN THIS REPO IN VSCODE. This repo has been infected by malware.
It seems like it was added in commit 74f317d at 11:06 UTC today, with five new hidden files being added under .claude and .vscode that together seem designed to either a) autorun a vscode tasks.json entry, or b) run a Claude session start hook, that will execute a large obfuscated payload. The payload looks like it will fingerprint your system and try to exfil your GitHub tokens.
Edit:
- It also exfils your AWS credentials (~/.aws/credentials, ~/.aws/config), named AWS profiles, and AWS secret managers and SSM parameter store contents
- Same with K8s secrets, with specific searches for GitHub and npm tokens, AWS keys, GCP keys, Azure keys, Stripe keys, Slack tokens, and Twilio keys
- Same with HashiCorp vault contents
- It will try to use your GitHub tokens (if they have the workflow permission) to run actions on your repository and try to exfiltrate secrets from there
- It will try to read a whole bunch of files from your local environment. I didn’t manage to extract the exact file list, unfortunately.
- If the normal C&C server is not available, it tries to create / select a GitHub repo, and commits your data as results-*.json files 100kb at a time
- It also has a bunch of stealth and persistence measures that I’m not qualified to really analyze. Don’t assume that deleting the files is necessarily enough.
27 comments
[ 3.0 ms ] story [ 25.4 ms ] threadWho knows, maybe that’s a good thing.
First off I would expect a (team) methodology to be referenced. There are tons to choose from. From that point on other terms may make more sense.
For example:
“ Pillar 2: Product Strategy & Architectural Governance (PDRs & ADRs)
Factor III — Mission Definition. Factor IV — Structured Planning. Factor IX — Traceability.”
Why are the Roman numerals in that order, what do they reference and why. Why is this Pilar 2. Etc. It’s easy to get lost in this, even if it would be the best approach in the world.
Things like ArchUnit, static analyzers, and other deterministic tools can help with lower level things like architecture. For higher up stuff, I am increasingly feeling like agents don't guarantee anything and in many cases its just the opposite. This is where a thoughtful engineer and reviewer can keep things in check.
It's possible I'm off base here, but I can't make heads or tails of the LLM written readme.
1: https://arxiv.org/html/2510.05381v1
For example, I’m working on a project to remake the Final Fantasy XI client. My repository has a bunch of git submodules that reference other peoples’ related efforts, and an open source server. For example, despite including CLAUDE.md to suggest otherwise, Claude Code always ends up writing these insanely dense comments referring to specific files and lines of code in submodules. When those submodules update, now the comments are no longer correct.
So I added a hook to detect when comments are included. Then, for example, I have a deterministic heuristic and script involved to remove some kinds of comments, and another that asks Haiku to quickly LLM-as-a-judge whether or not to edit/remove the comment.
Similarly, I have a stop hook that reminds Claude to commit its code logically on main, noting that other changes may have been added by other concurrent sessions (I avoid worktrees and even branching for this particular project and stage of development.) It works well.
I dont want to be biased but i can get myself to read this after an opening like that .
This is far from perfect but we're in this weird transition phase where none of the major AI tool providers are really focusing much on team use of their stuff. But I expect that will start changing soon.
Current tools mostly focus on individuals doing things in isolation. And of course in a team there's more to collaborating than throwing stuff at each other via github. A central repository of company skills is merely our way of improvising a solution.
I find it interesting that Anthropic hired a few of the key people behind Zulip recently. Team chat with tightly integrated AI tools could be a missing piece here. Team communication flows and processes, including ways of working and guardrails are sort of the next piece of the puzzle here. Going from everyone doing their own thing to teams and companies doing things together is going to be a bit of a journey.
For a central skills repository I would also record the exact rules revision in each run or generated artifact. Otherwise a failed run becomes hard to reproduce after the shared repository changes. It would be useful if each skill declared which claims are advisory and which are backed by a command the agent can execute and verify.
try a fresh coding session without skills, agents.md, system prompt and additional tools
I think you will be positively surprised how good current models like GPT 5.6 Sol are when they are not oversteered and context spammed
Here is a task (python templating) with 9 runs with OpenCode, Pi and smol
https://smolenv.com/t/nested-template-includes-60636/
you can read each run step by step and see what the agents are doing and how the system prompt and available tools are steering their behaviour to take longer and higher cost
(disclaimer: I'm working on smol)
It seems like it was added in commit 74f317d at 11:06 UTC today, with five new hidden files being added under .claude and .vscode that together seem designed to either a) autorun a vscode tasks.json entry, or b) run a Claude session start hook, that will execute a large obfuscated payload. The payload looks like it will fingerprint your system and try to exfil your GitHub tokens.
Edit:
- It also exfils your AWS credentials (~/.aws/credentials, ~/.aws/config), named AWS profiles, and AWS secret managers and SSM parameter store contents
- Same with K8s secrets, with specific searches for GitHub and npm tokens, AWS keys, GCP keys, Azure keys, Stripe keys, Slack tokens, and Twilio keys
- Same with HashiCorp vault contents
- It will try to use your GitHub tokens (if they have the workflow permission) to run actions on your repository and try to exfiltrate secrets from there
- It will try to read a whole bunch of files from your local environment. I didn’t manage to extract the exact file list, unfortunately.
- If the normal C&C server is not available, it tries to create / select a GitHub repo, and commits your data as results-*.json files 100kb at a time
- It also has a bunch of stealth and persistence measures that I’m not qualified to really analyze. Don’t assume that deleting the files is necessarily enough.
Rotate your keys, folks.