Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/lacerbi/dotclaude/claude-mdgit clone --depth 1 https://github.com/lacerbi/dotclaudeWrote 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/lacerbi/dotclaude/claude-md)<a href="https://agentmods.dev/instructions/lacerbi/dotclaude/claude-md"><img src="https://agentmods.dev/badge/instructions/lacerbi/dotclaude/claude-md.svg" alt="Measured on agentmods" 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 | $0.00668 | $0.00668 |
| Opus 5 | $0.00334 | $0.00334 |
| Sonnet 5 | $0.00134 | $0.00134 |
| Haiku 4.5 | $0.00067 | $0.00067 |
Grade A, and why
dotclaude CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 26 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shell tooling
I'm on Windows PowerShell. @'...'@ here-strings work only in the PowerShell tool, not the Bash tool (and <<'EOF' only in Bash). For multi-line text like commit messages, use repeated -m flags.
Smoke tests / long runs must stream output. Don't pipe python ... | grep — a non-TTY pipe makes Python block-buffer stdout, so nothing shows until it exits. -u (or PYTHONUNBUFFERED=1) is the key bit; have the script print(..., flush=True) on a cadence so there's something to watch.
- Local: run unbuffered to a uniquely-named log (don't clobber a prior run):
python -u script.py > runs/run_$(date +%s).log 2>&1(background ok), then Read/tail -Fit. Filter when reading (grepthe file), not in the run pipe. - HPC: don't hand-redirect — Slurm captures each job's stdout to its own per-jobid file (so no clobber across runs). Use
hpc logs <job> --once/-n Nfor occasional dumps, orhpc logsto follow. The script still needs periodicflush=Trueso the Slurm log updates before the job ends.
Agent Selection
When deploying sub-agents, calibrate agent intelligence to task complexity:
- Haiku: Only trivial, read-only tasks requiring no interpretation (listing files, reading contents, checking if something exists). Best suited for parallelizing many simple tasks at scale.
- Sonnet: Standard implementation, straightforward execution, mechanical checks (linting output, test pass/fail)
- Opus: Anything requiring judgment—analysis, review, verification, planning, debugging, architectural decisions
Parallel fan-out — only one heavy-compute agent. When fanning out parallel agents (e.g. for review/doublecheck), at most ONE may run heavy compute (GPU/CPU-intensive processes: training, torch, test suites, builds). Instruct the rest to go deep but stay read-only / static-analysis (reading files, reasoning, grepping) — explicitly tell them not to run such processes. Running heavy compute in several agents at once once froze the machine. Cleanest variant: keep the single heavy slot in the main thread yourself and make the whole fan-out read-only.
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.
- 5d ago First seen · 26 lines · 668 tokens per session scan A 32cee759c6cd
dotclaude CLAUDE.md is an instructions file published in the GitHub repository lacerbi/dotclaude (2 stars, last pushed 5d ago), licensed MIT. It adds 668 tokens to every session, about $0.0033 per session on Opus 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 2026-08-31.
Other instructions, from other repositories
agent-skills CLAUDE.md
Claude Code instructions for oryanmoshe/agent-skills, a project described as: Battle-tested skills for disciplined AI-assisted development. Task tracking, code review, parallel exploration, and more.
agent-skills AGENTS.md
AGENTS.md instructions for oryanmoshe/agent-skills, covering agent skills, project structure, skills, working in this repo and releasing a new version.
matt-skills-with-to-goal CLAUDE.md
Instructions for tt-a1i/matt-skills-with-to-goal: Skills are organized into bucket folders under skills/.
ephemeral-sandbox CLAUDE.md
Instructions for Ephemeral-AI-Lab/ephemeral-sandbox, covering claude.md, project, engineering practice (required), build & test and sandbox tools.
Cerebro AGENTS.md
Instructions for AgenticFirst/Cerebro, covering cerebro — agent context, read before touching integrations, project layout, conventions a fresh agent would otherwise miss and running locally.
critical-thinking AGENTS.md
Instructions for Johna2an/critical-thinking, covering agents.md, repo map, install (for a user asking "should i install this?"), working in this repo and running an eval round.