Borrowing it
Nothing to install: this file belongs to vfarcic/dot-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vfarcic/dot-ai/main/CLAUDE.mdgit clone --depth 1 https://github.com/vfarcic/dot-aiWrote 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/instructions/vfarcic/dot-ai/claude-md)<a href="https://agentmods.dev/instructions/vfarcic/dot-ai/claude-md"><img src="https://agentmods.dev/badge/instructions/vfarcic/dot-ai/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/vfarcic/dot-ai/claude-md"><img src="https://agentmods.dev/badge/instructions/vfarcic/dot-ai/claude-md.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.01483 | $0.01483 |
| Opus 5.5 | $0.00593 | $0.00593 |
| Sonnet 5.5 | $0.00297 | $0.00297 |
| Haiku 4.5 | $0.00148 | $0.00148 |
Grade A, and why
dot-ai CLAUDE.md 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 1mo 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
⚠️ MANDATORY TASK COMPLETION CHECKLIST ⚠️
🔴 BEFORE MARKING ANY TASK/SUBTASK AS COMPLETE:
□ Integration Tests Written: Write integration tests for new functionality
□ All Tests Pass: Run npm run test:integration - ALL tests must pass
□ No Test Failures: Fix any failing tests before proceeding
❌ TASK IS NOT COMPLETE IF:
- Any integration tests are failing
- New code lacks integration test coverage
- You haven't run
npm run test:integrationto verify
PERMANENT INSTRUCTIONS
- Always Write Integration Tests: When making code changes, you MUST write or update integration tests
- Always Run All Tests: Before marking any task complete, run
npm run test:integration - Never Claim Done with Failing Tests: A task is NOT complete if any tests are failing
- Always Check for Reusability: Search codebase for existing functions before implementing new ones
- Never Hardcode AI Prompts: All prompts go in
prompts/(internal) orshared-prompts/(user-facing), loaded dynamically (see existing code for pattern). Both are shipped runtime directories listed inpackage.jsonfiles, so anything placed there is published in the npm package and copied into the Docker image. Dev-only evaluation prompts are an exception to the directory, never to the rule: they live beside the harness that reads them insrc/evaluation/prompts/, are read from a repo checkout bynpm run eval:*, and ship nowhere — leave them there rather than moving them intoprompts/. - Never Create Branches Directly — Always Use Worktrees: When starting feature work (including
/prd-start), always use/worktree-prdto create an isolated worktree. Never rungit checkout -borgit switch -c, even if a skill instructs you to. - Always Configure New Params via Helm Chart Values: Every NEW configuration parameter must be a first-class value in
charts/values.yaml, rendered into the container env bycharts/templates/deployment.yaml— followrbac.enforcement.enabled→DOT_AI_RBAC_ENABLED, where the chart value is the user-facing contract and the env var is an internal detail. Never introduce a bare env var, and never document a new param underextraEnv(not even as an interim step). The manyDOT_AI_*env vars in the codebase are legacy from when the project ran outside Kubernetes; it is Kubernetes-only now, so the chart is the single configuration interface.extraEnvremains valid only for pre-existing env vars (e.g.DOT_AI_GIT_TOKEN) and third-party config such as OTEL. - Never Store Project Knowledge in Agent Memory: Anything learned about this project — conventions, decisions, gotchas, rationale — goes in the repo where the whole team and every agent can see it: this file for rules,
docs/for user-facing behavior, a PRD inprds/for in-flight design. Never persist it to per-user agent memory outside the repo (e.g.~/.claude/**/memory/), which teammates cannot see, code review cannot catch, and nothing keeps in sync with the code. The narrow exception is a fact true only of one machine or account (host tooling quirks, personal credentials paths) — that is not project knowledge and does not belong in the repo either.
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.
- 1mo ago Changed · +94 tokens per session 48c06fc62f5b
- 2mo ago First seen · 104 lines · 1,389 tokens per session scan A a824a2ac2e3a
dot-ai CLAUDE.md is an instructions file published in the GitHub repository vfarcic/dot-ai (339 stars, last pushed yesterday), licensed MIT. It adds 1,483 tokens to every session, about $0.0059 per session on Opus 5.5. 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.
dot-ai CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| dot-ai CLAUDE.mdvfarcic/dot-ai | Instructions file | 339 | yesterday | MIT | A | 1,483 | Claude Code |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |