Writing a good Claude.md

📝 Discussion Summary (Click to expand)

The three most prevalent themes in this discussion revolve around the practical application, required configuration, and perceived reliability of using LLM coding agents like Claude.

1. The Necessity and Implementation of Explicit Context Configuration $\text{($CLAUDE.md}$)}$

A significant portion of the discussion focuses on the utility, structure, and necessity of dedicated instructional files ($\text{CLAUDE.md}$ or $\text{AGENTS.md}$) to guide the LLM in a codebase, viewing them as essential context injection mechanisms rather than traditional documentation.

Quote: The author of the original post clarified the role: "I think you’re missing that $\text{CLAUDE.md}$ is deterministically injected into the model’s context window. This means that instead of behaving like a file the LLM reads, it effectively lets you customize the model’s prompt."
Quote: Conversely, some users argue that explicit documentation ($\text{README.md}$) should suffice, but others defend the specialized format: "READMEs are written for people, $\text{CLAUDE.md}$s are written for coding assistants. I don’t write “CRITICAL (PRIORITY 0):” in READMEs."

2. Disagreement on the Efficacy and Stability of AI Guidance

Users are highly divided on whether meticulously crafting instructions (via $\text{CLAUDE.md}$ or extensive prompting) provides reliable, consistent results or if it's "reinventing the wheel" or "ritual magic" that LLMs ultimately ignore.

Quote (Skepticism): Many users express frustration that agents ignore these dedicated instructions, suggesting external enforcement is necessary: "Also, there's no amount of prompting will prevent this situation. If it is a concern then you put a linter or unit tests to prevent it altogether..."
Quote (Reliance on Simple Interaction): Others find that explicit configuration files are often unnecessary or distracting, preferring simpler, non-configured interaction: "I simply highlight the relevant code, add it to the chat, and talk to these tools as if they were my colleagues and I’m getting pretty good results."

3. The Debate Over AI Productivity Claims vs. Engineering Rigor

The discussion touches on the underlying productivity claims of these tools. Some users feel the effort required to configure agents negates any potential gains, while others view this effort as necessary "engineering" to tame a non-deterministic tool.

Quote (Effort vs. Reward): One user questions the investment when facing uncertain gains: "If setting up all the AI infrastructure and onboarding to my code is going to take this amount of effort, then I might as well code the damn thing myself which is what I'm getting paid to (and enjoy doing anyway)."
Quote (The Moving Target): There is significant concern that prompt tuning provides only temporary benefits due to model instability: "If we have to perform tuning on our prompts... every model release then I see new model releases becoming a liability more than a boon."

🚀 Project Ideas

Context Proxy & Debugging Utility (CPDU)

Summary

[A lightweight development tool/service that transparently proxies API calls between a developer's local code (CLI, scripts) and external LLM services (like Anthropic/Claude), providing easy logging, inspection, and routing capabilities.]
[Value Proposition: Low-friction, non-expert-level introspection and manipulation of LLM API calls, fulfilling the need for visibility expressed by users wanting to debug prompts and context without deep sysadmin knowledge.]

Details

Key	Value
Target Audience	Developers who use LLM CLIs or SDKs but are "not a system or network expert" (specifically mentioning needing proxies for Claude CLI).
Core Feature	Intercepts, logs (including request/response bodies), and allows environment-variable based routing/modification of LLM API calls, functioning like a simple, integrated `mitmproxy` replacement for AI traffic.
Tech Stack	Go or Rust (for fast, small binary deployment) or Node.js (for ease of integration). Should focus on respecting standard environment variables like `ANY_API_BASE_URL`.
Difficulty	Medium (Building a robust, easy-to-install proxy that handles common auth securely requires non-trivial networking setup, though the core concept is simple).
Monetization	Hobby

Notes

["You can investigate this yourself by putting a logging proxy between the claude code CLI and the Anthropic API... I'd be eager to read a tutorial about that I never know which tool to favour for doing that when you're not a system or network expert." - eric-burel]
[Directly addresses the request for an easy-to-use proxy tool that abstracts away the complexity of tools like mitmproxy, which one commenter noted "Takes like 5 mins to figure out," implying that 5 minutes might be too long for many.]

Automated Context Integrity Checker (ACIC)

Summary

[A CI/CD or pre-commit hook tool that analyzes a developer's codebase structure and generates/updates scaffolding documentation files (CLAUDE.md, AGENTS.md) based on defined rules, while simultaneously validating that existing instructions are not contradictory or overly verbose.]
[Value Proposition: Solves the tension between needing structured context for agents (the CLAUDE.md dance) and the risk of context dilution/contradiction, by automating the generation of tailored context files.]

Details

Key	Value
Target Audience	Engineering teams using agentic tooling (like Claude Code) in complex or brownfield repositories who want structured, performant context injection.
Core Feature	Generates a "Table of Contents" style reference map in the root or directory-level `.md` file, pointing to hyper-specific context files (e.g., `src/db/CLAUDE_DB_RULES.md`) and integrating linter/test status updates derived from the actual codebase checks.
Tech Stack	Python (for easy AST parsing/linter integration) or TypeScript/Node.js. Could integrate with existing static analysis tools (ESLint, TypeScript compiler).
Difficulty	High (Requires understanding codebase structure, identifying patterns that warrant dedicated context files, and integrating with external linting/testing results accurately.)
Monetization	Hobby

Notes

["I have found that enabling the codebase itself to be the “Claude.md” to be most effective. In other words, set up effective automated checks for linting, type checking, unit tests etc and tell Claude to always run these before completing a task." - andersco] coupled with ["I like to write my CLAUDE.md directly, with just a couple paragraphs describing the codebase at a high level, and then I add details as I see the model making mistakes." - lostdog]
[This tool bridges the gap: it automatically gathers the objective data (lint/test status) that humans mentioned, and organizes the subjective, agent-specific instructions (CRITICAL (PRIORITY 0)) into a manageable, hierarchical structure suggested by discussion participants.]

LLM Instruction Performance Benchmark Suite (LIPS)

Summary

[A standardized, open-source benchmarking framework/service designed specifically to measure how well LLMs adhere to complex, multi-step written instructions across different model checkpoints.]
[Value Proposition: Provides the empirical data structure that users explicitly requested ("I would love to see it extended to show Codex... I've never seen someone go and systematically measure it before!"), moving the discussion from anecdotal evidence to measurable performance.]

Details

Key	Value
Target Audience	LLM researchers, PMs at AI labs, and advanced third-party developers building on top of LLM APIs who care about instruction following stability.
Core Feature	Runs a suite of seeded, complex instructions (including style, convention, and negative constraints—e.g., "do not use feature X") and scores the output for adherence, exposing results in a digestible comparative plot format similar to the one mentioned in the discussion.
Tech Stack	Python (using libraries like `Instructor` for structured output validation, Pydantic, and plotting libraries like Bokeh/Plotly). Cloud architecture for running consistent, repeated API calls.
Difficulty	High (Requires rigorous methodology to isolate model variance from prompt variance and to systematically score instruction fidelity beyond simple keyword matching.)
Monetization	Hobby

Notes

["You can investigate this yourself... I'd be eager to read a tutorial about that..." - eric-burel, regarding understanding execution.] and ["That graph is so useful -- I've never seen someone go and systematically measure it before!" - johnfn, regarding instruction following data.]
[This directly creates the "tutorial" (the tool and its results) that users requested regarding debugging proxies, and satisfies the need for hard data on instruction quality, which several users lamented was outdated or missing for newer models.]